Predicting severe radiation pneumonitis in patients with locally- advanced non-small cell lung cancer after thoracic radiotherapy: Development and internal validation of a nomogram based on the clinical, hematological and dose–volume histogram parameters

Predicting severe radiation pneumonitis in patients with locally- advanced non-small cell lung cancer after thoracic radiotherapy: Development and internal validation of a nomogram based on the clinical, hematological and dose–volume histogram parameters | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predicting severe radiation pneumonitis in patients with locally- advanced non-small cell lung cancer after thoracic radiotherapy: Development and internal validation of a nomogram based on the clinical, hematological and dose–volume histogram parameters Ying Zhang, Yu-Jie Yan, Shi-Hong Zhou, Lei-Lei Wu, Xiao-Shuai Yuan, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4967531/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Severe radiation pneumonitis (grade≥3 RP) remains an important dose-limiting toxicity after thoracic radiotherapy (RT). This study aimed to investigate risk factors for severe RP in patients with locally-advanced non-small cell lung cancer (NSCLC) after thoracic RT, develop a prediction model to identify high-risk groups and investigate impact of severe RP on overall survival (OS). Methods We retrospectively collected clinical, hematological and dosimetric factors from 351 stage-Ⅲ NSCLC patients after thoracic RT between 2018 and 2022. The primary endpoint was development of severe RP. The secondary endpoint was OS. Logistic regression and least absolute shrinkage and selection operator (LASSO) regression analysis were used to identify risk factors of severe RP. Nomogram was generated based on multivariate regression coefficients. Area under the ROC curve (AUC), calibration curve, and decision curve analysis (DCA) were conducted to validate the model. After a long-term follow-up, OS of patients with RP vs. non-RP and mild RP vs. severe RP groups was analyzed by Kaplan‒Meier method. Results ILD (p<0.001), percentage of contralateral lung volume receiving≥5Gy (contraV 5 ) (P=0.013), percentage of ipsilateral lung volume receiving≥20Gy (ipsiV 20 )(P=0.039), pre-RT derived neutrophil lymphocyte ratio (dNLR) (P=0.015) and post-RT systemic inflammation response index (SIRI) (p=0.001) were showed to be independent predictors of severe RP and were included in the nomogram. ROC curves revealed the AUC of the nomogram was 0.782. Calibration curves showed favorable consistency, and DCA showed satisfactory positive net benefits of the model. Median follow-up time was 19.8 months (1.4-52.9 months), and cases who developed severe RP showed shorter OS than those developed mild RP (P=0.027). Conclusion We identified that ILD, contraV 5 (>11%), ipsiV 20 (>45%), pre-RT dNLR (>1.9) and post-RT SIRI (>3.4) could predict severe RP among patients with locally-advanced NSCLC receiving thoracic RT. Combining these indicators, a nomogram was first built and validated, showing its potential value in clinical practice. Non-small cell lung cancer (NSCLC) Radiotherapy Radiation pneumonia Dosimetric parameter Peripheral blood biomarkers. Figures Figure 1 Figure 2 Figure 3 Introduction Nowadays, lung cancer is one of the most common malignancies and the leading cause of cancer-related death worldwide [ 1 , 2 ] , and non-small cell lung cancer (NSCLC) accounts for most cases [ 3 ] . Radiation therapy (RT) has played an important role in the treatment of NSCLC for many years, especially concurrent chemoradiotherapy (CCRT) remains the current standard of care for patients with inoperable locally advanced NSCLC [ 4 , 5 ] . However, the benefits of RT are occasionally disturbed by relevant adverse effects, among which radiation pneumonitis (RP) is one of the most common thoracic RP-related toxicity [ 6 , 7 ] , and is one of a main dose-limiting factors that affects the efficacy of RT [ 8 – 12 ] . RP was been defined as the acute injury stage of radiation-induced lung injury (RILI) [ 13 , 14 ] , and could be stratified into five grades by the clinical manifestation and imaging performance through Common Terminology Criteria for Adverse Events version 5.0 (CTCAE v. 5.0) [ 15 ] . RP was been reported with an incidence rate varying from 5%-40% [ 7 , 8 , 10 ] , and mild RP consisted majority of cases [ 11 , 16 ] . RP can lead to chronic respiratory insufficiency, especially for severe RP, i.e., RP of grade 3 or higher, can seriously influence the quality of life of patients, and may even directly threaten the life of patients, leading to treatment-related death [ 17 ] , with a mortality as high as 30% [ 18 ] . Therefore, early detection and intervention are crucial for the management of severe RP, and robust markers for the prediction of severe RP is of urgent needed. Various clinical, dosimetric and hematological factors have been found to be associated with the incidence of RP, the former include age, the performance status (PS), smoking status, chronic obstructive pulmonary disease (COPD), pulmonary emphysema, interstitial lung disease (ILD), pulmonary function, concurrent chemotherapy [ 10 , 19 – 23 ] . Among dose-volume histogram (DVH) metrics, gross tumor volume (GTV), mean lung dose (MLD), percent of the lung volume receiving ≥ 5, 20, 30Gy (V 5 /V 20 /V 30 ) and heart dosimetric variables were reported to be related to the occurrence of RP [ 10 , 24 – 27 ] . As for peripheral blood leukocyte (PBL) biomarkers, lymphocytes, neutrophil-to-lymphocyte (NLR), derived neutrophil-to-lymphocyte ratio (dNLR), monocyte-to-lymphocyte ratio (MLR) and derived monocyte-to-lymphocyte ratio (dMLR) have previously been reported to be predictors for RP [ 28 , 29 ] . However, studies on risk factors and prediction model for severe RP, especially in patients with locally-advanced NSCLC, are limited. Therefore, in order to optimize risk stratification and predict the risk of severe RP more accurately, as well as further guide individualized treatment management decisions, we performed a retrospective study to investigate the potential risk factors for severe RP, including clinical characteristics, DVH parameters and PBL indicators, and aimed at generating a predictive model to quantify risk of severe RP in the population of locally-advanced NSCLC patients treated with thoracic RT. What’s more, we further made a long-term follow-up to track the survival status of patients, and identified the impact of severe RP on survival. Methods and Materials Patients This retrospective study was approved by the ethics committee of Shanghai Pulmonary Hospital, Shanghai, China. We retrospectively reviewed the medical charts of patients with locally-advanced NSCLC receiving thoracic radiotherapy (total dose > 50 Gy and single dose 2 Gy) between April 2018 and August 2022. The inclusion criteria were as follows: (1) histologically or cytologically proven stage Ⅲ NSCLC, (2) with an Eastern Cooperative Oncology Group performance status (ECOG PS) of 0–2, (3) patients received thoracic radiotherapy from 2018 to 2022, (5) no previous thoracic radiation therapy. Definition of clinical, DVH and PBL factors We retrospectively collected clinical, dosimetric and hematological factors for analysis. The clinical factors included age, gender, ECOG PS, smoking status, presence of underlying lung disease (ILD, COPD and pulmonary emphysema), surgery history, total RT dose, chemotherapy history, regimen and sequence, and pulmonary function, which including forced expiratory volume in 1 second (FEV1) and diffusing capacity of the lung for carbon monoxide (DLCO). Pulmonary function testing was done immediately before RT. The dosimetric factors were collected as follows: GTV, planning tumor volume (PTV), total/contralateral/ipsilateral lung MLD and V 5 / 20 / 30 , mean heart dose (MHD) and heart V 50 . V x was defined as the percentage of lung/heart volume receiving ≥ x Gy. DVH data were obtained from electronic radiation treatment planning documents. What’s more, we collected hematological factors both pre-radiotherapy (pre-RT) and post-radiotherapy (post-RT), and the latter were defined as the data at one month after the completion of radiotherapy. The collected hematological indicators were composed of white blood cell (WBC) count and its classification count, including the absolute count of neutrophils (NEU), lymphocytes (LYM), monocytes (MON) and eosinophils (EOS), as well as neutrophil-to-lymphocyte ratio (NLR), derived neutrophil-to-lymphocyte ratio (dNLR), monocyte-to-lymphocyte ratio (MLR), derived monocyte-to-lymphocyte ratio (dMLR) and systemic inflammation response index (SIRI). The NLR, dNLR, MLR, dMLR and SIRI were calculated as follows: Radiotherapy Three-dimensional conformal radiotherapy (3D-CRT), intensity-modulated radiotherapy (IMRT) or volumetric arc therapy (VMAT) techniques were used to deliver radiotherapy courses. What’s more, four-dimensional computed tomography (4D-CT) scanning of the whole lung was performed to measure intrafraction respiratory movement and create an internal target volume to compensate respiratory motion, with CT scans at intervals of 2.5–5.0 mm and each patient immobilized in the supine position with a vacuum cushion. All radiation treatment plans were generated in Pinnacle treatment planning system, and were delivered using 6–10 MV beams on linear accelerators. Positron emission tomography/computed tomography (PET/CT) should be obtained preferably within 4 weeks before treatment in the treatment position. Based on fluorodeoxyglucose positron emission tomography (FDG-PET) and the diagnostic CT images, we carefully contoured GTV on the planning CT images, which consisted of the known extent of disease (primary and nodal) on imaging and pathologic assessment, The internal GTV was created using a 4D-CT image. The National Comprehensive Cancer Network (NCCN) guidelines suggest the clinical target volume (CTV) included regions of presumed microscopic extent or dissemination, was expanded by 6-8mm from the internal GTV, and no prophylactic lymph node area was added [ 30 ] . The PTV margin of 5 mm was added for setup uncertainty and respiratory motion. IGRT was applied to real-time monitoring to abate set-up errors. The treatment dose was prescribed to cover 95% of the PTV. The mean lung dose (MLD), the percentage of total lung volume exceeding 20 Gy (V 20 ) and 5 Gy (V 5 ), as well as the dose constraints for normal tissues (spinal cord, esophagus, and heart) were limited appropriately according to the NCCN guidelines [ 30 ] . Chemotherapy Adjuvant concurrent or sequential chemotherapy was applied for part of patients based on the NCCN guidelines [ 30 ] , and mostly were platinum-based doublets (cisplatin combined with etoposide, vinorelbine or paclitaxel). All the doses and adjustments of the chemotherapy regimen followed the NCCN guidelines [ 30 ] . Endpoint definitions The primary observation endpoint of this study was grade ≥ 3 RP as defined in CTCAE v5.0 [ 15 ] , which was graded based on the existence and severity of the clinical symptoms, whether pneumonitis intervention is required, and on the extent of pulmonary fibrosis and accompanying symptoms. RP was diagnosed by professional radiologists and respiratory physicians on the basis of clinical symptoms and changes in CT images, the specific criteria were described in our previous review [ 31 ] . The time to RP was defined as the time interval from the start of radiation treatment to the diagnosis of RP and was calculated using Kaplan-Meier method. The secondary endpoint was overall survival (OS), which were measured from the start of radiation treatment. The follow-up evaluation was performed 1 month after the completion of thoracic RT, every 3 months for up to two years, then every 6 months for the third year and thereafter. All available follow-up documents were carefully reviewed including clinical records, chest radiographic images, follow-up clinical assessment notes and electronic records until the last follow-up or death of the patients. Statistical analysis The predictive model was built as follows: firstly, we measured the impact of clinical characteristics, dose-volume parameters and hematological indicators on the incidence of severe RP using univariable logistic regression model. Secondly, factors with p < 0.05 in univariate analyses were assessed through least absolute shrinkage and selection operator (LASSO) regression analysis to obtain the crucial severe RP-associated factors. Thirdly, a multivariate logistic regression analysis was conducted to select the severe RP-associated factors for establishing a predictive model. Finally, factors with significant predictive value in multivariate analysis were used to build the nomogram. Receiver operating characteristic (ROC) curve analysis was used to establish optimal cut points for continuous variables. The validation of the nomogram was conducted using the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve (1000 bootstrap resamples) and decision curve analysis (DCA). The ROC curves were used to estimate the discrimination ability of the nomogram and each predictor alone. Calibration curve was used to compare the predicted probability with the observed probability of severe RP. DCA was performed to illustrate the clinical usefulness of the nomogram by quantifying the net benefits at different threshold probabilities. What’s more, the Kaplan-Meier method was used to estimate OS between patients with non-RP vs. RP, and grade 1–2 RP vs. grade 3 RP. Statistical analyses were performed with R (Version 4.1.0). All tests were 2-sided, with a P value < 0.05 considered to indicate statistical significance. Results Patient characteristics A total of 351 patients received thoracic radiotherapy between April 2018 and August 2022 were enrolled and a summary of baseline characteristics was listed in Table 1 . Of all the 351 patients, 115(32.8%) didn’t experience RP, 236 (67.2%) developed RP (grade 1: n = 91, 25.9%; grade 2: n = 111, 31.6%; grade 3: n = 34, 9.7%; grade 4 and 5: n = 0), and 34 (9.7%) developed severe RP. The median interval from the start of RT to the occurrence of RP was 3.7 months (range, 0.5–28.9 months), and the median occurrence time of severe RP was 3.1 months (range, 1.0-11.6 months). Table 1 Baseline characteristics of all patients (n = 351) Characteristics Number of patients (%) Age(years), Median (IQR) 65 (58–70) Gender Male 284(80.9) Female 67(19.1) EOCG PS 0 1 2 Smoking history Yes No Lung disease No ILD COPD Emphysema Surgery Yes No FEV1%, Median (IQR) DLCO%, Median (IQR) Radiotherapy dose(cGy), Median (IQR) Fractionation, Median (IQR) 59(16.8) 271(77.2) 21(6.0) 171(48.7) 180(51.3) 226(64.4) 21(6.0) 17(4.8) 87(24.8) 94(26.8) 257(73.2) 85.1 (70.8–96.8) 91.9 (75.6–106.0) 5800 (5000–6000) 26 (25–30) Chemotherapy regimen No chemotherapy 25(7.1) Include TAX 149(42.5) Other regimens 177(50.4) Chemoradiotherapy CCRT 46(13.1) SCRT 280(79.8) RT alone 25(7.1) GTV (cm 3 ), Median (IQR) 55.1 (28.5-102.6) Total lung V 5 (%), Median (IQR) 37.8 (32.4–43.8) Total lung V 20 (%), Median (IQR) 21.1 (17.8–24.1) Total lung V 30 (%), Median (IQR) 15.0 (11.4–17.6) Total lung MLD (cGy), Median (IQR) 1112.5 (910.2-1250.1) Contralateral lung V 5 (%), Median (IQR) 19.1 (12.7–25.6) Contralateral lung V 20 (%), Median (IQR) 3.8 (1.0-7.9) Contralateral lung V 30 (%), Median (IQR) 1.3 (0.1–3.8) Contralateral lung MLD (cGy), Median (IQR) 375.0 (263.8-536.9) Ipsilateral lung V 5 (%), Median (IQR) 58.3 (50.3–68.3) Ipsilateral lung V 20 (%), Median (IQR) 39.2 (33.2–46.2) Ipsilateral lung V 30 (%), Median (IQR) 29.6 (23.5–35.3) Ipsilateral lung MLD (cGy), Median (IQR) 1891.3 (1585.6-2185.8) Pre-RT Lymphocytes (10 6 ), Median (IQR) 1.6 (1.2-2.0) Pre-RT Monocytes (10 6 ), Median (IQR) 0.5 (0.4–0.7) Pre-RT Eosinophils (10 6 ), Median (IQR) 0.1 (0.1–0.2) Pre-RT NLR, Median (IQR) 2.6 (1.8–3.7) Pre-RT dNLR, Median (IQR) 1.8 (1.3–2.5) Pre-RT MLR, Median (IQR) 0.3 (0.2–0.4) Pre-RT dMLR, Median (IQR) 0.4 (0.3–0.6) Pre-RT SIRI (10 6 ), Median (IQR) 1.3 (0.8-2.0) Post-RT Lymphocytes (10 6 ), Median (IQR) 0.8 (0.6–1.1) Post-RT Monocytes (10 6 ), Median (IQR) 0.5 (0.3–0.6) Post-RT Eosinophils (10 6 ), Median (IQR) 0.1 (0.1–0.2) Post-RT NLR, Median (IQR) 4.6 (3.1–6.9) Post-RT dNLR, Median (IQR) 2.5 (1.8–3.5) Post-RT MLR, Median (IQR) 0.5 (0.4–0.8) Post-RT dMLR, Median (IQR) 0.7 (0.5–1.1) Post-RT SIRI (10 6 ), Median (IQR) 2.0 (1.3–3.4) IQR, interquartile range; ECOG, Eastern Cooperative Oncology Group; PS, performance status; ILD, interstitial lung disease; COPD, chronic obstructive pulmonary disease; FEV1, forced expiratory volume in 1 second; DLCO, diffusing capacity of the lung for carbon monoxide; CCRT, concurrent chemoradiation therapy; SCRT, sequential chemoradiation therapy; GTV, gross target volume; V x : the percentage of the lung volume that received more than x Gy, respectively; MLD: mean lung dose; NLR: neutrophil lymphocyte ratio; dNLR: derived neutrophil lymphocyte ratio; SIRI: systemic inflammation response index . This study included 284(80.9%) men and 67(19.1%) women, with a median age of 65 years (range, 40–84 years), and 171(48.7%) patients had a history of smoking. 120 (34.2%) patients had underlying lung disease, including 21(6.0%) with ILD, 17(4.8%) with COPD and 87(24.8%) with emphysema. Totally 326 (92.9%) patients were treated with combined radiation and chemotherapy regimens, and 149 (42.5%) patients using paclitaxel regimen. 46(13.1%) patients received concurrent chemoradiotherapy and 280 (79.8%) patients received sequential chemotherapy, and 94 (26.8%) also underwent surgery. For all patients, 50–66 Gy were delivered in 1.8–2.0 Gy/fractions. Univariate, LASSO and multivariate analyses In order to investigate what factors might differ the incidence of severe RP, univariate Logistic regression analysis was firstly performed, with the optimal ROC cut-off values calculated for continuous variables, and the results are shown in Table 2 . Table 2 Univariate analysis of clinical factors in predicting severe RP With severe RP (n = 34) Without severe RP (n = 317) Univariate analysis N (%) N (%) OR (95%CI) P-value Clinical factors Age(years) > 65 24 (70.6) 156(49.2) 2.477 (1.178–5.584) 0.021 * ≤ 65 10 (29.4) 161(50.8) Sex Male 27(79.4) 257(81.1) 0.901 (0.393–2.332) 0.815 Female 7(20.6) 60(18.9) ECOG PS 2 4(11.8) 17(5.4) 2.353 (0.646–6.859) 0.146 0–1 30(88.2) 300(94.6) Smoking history Yes 15(44.1) 156(49.2) 0.815 (0.394–1.656) 0.573 No 19(55.9) 161(50.8) Lung disease ILD 9(26.5) 12(3.8) 15.734 (6.409–39.390) < 0.001 COPD 1(2.9) 16(5.0) 0.570 (0.031–2.935) 0.591 Emphysema 9(26.5) 78(24.6) 1.103 (0.470–2.386) 0.811 Surgery history Yes 3(8.8) 91(28.7) 0.240 (0.057–0.695) 0.021 * No 31(91.2) 226(71.3) Chemotherapy history Yes 31(91.2) 296(93.4) 0.733 (0.235–3.226) 0.631 No 3(8.8) 21(6.6) Chemotherapy type Paclitaxel 18(52.9) 131(41.3) 1.597 (0.785–3.279) 0.196 Others regimen 13(38.2) 165(52.1) Chemotherapy sequence CCRT 2(5.9) 44(13.9) 0.388 (0.061–1.343) 0.20 SCRT 29(85.3) 251(79.2) RT alone 3(8.8) 22(6.9) FEV1(%) < 87 21(61.8) 129(40.7) 2.651 (1.136–6.940) 0.032 * ≥87 13(38.2) 188(59.3) DLCO-SB (%) 58 23(67.6) 152(47.9) 2.270 (1.093–4.986) 0.033 * ≤ 58 11(32.4) 165(52.1) GTV (cm 3 ) >30 15(44.1) 218(68.8) 0.298 (0.137–0.646) 0.002 ** ≤30 19(55.9) 99(31.2) PTV1(GTV + 1cm) (cm 3 ) > 202 14(41.2) 201(63.4) 0.353 (0.163–0.756) 0.007 ** ≤202 20(58.8) 116(36.6) PTV2(Actual PTV) (cm 3 ) > 205 20(58.8) 221(69.7) 0.621 (0.303–1.303) 0.196 ≤205 14(41.2) 96(30.3) PTV1-PTV2 (cm 3 ) > 15 19(55.9) 101(31.9) 3.095 (1.438–6.972) 0.005 ** ≤15 15(44.1) 216(68.1) Whole lung volume (cm 3 ) > 2409 21(61.8) 272(85.8) 0.267 (0.126–0.583) 0.001 *** ≤2409 13(38.2) 45(14.2) Total lungs V 5 (%) > 41 15(44.1) 118(37.2) 1.331 (0.643–2.713) 0.432 ≤ 41 19(55.9) 199(62.8) Total lung V 20 (%) > 18 20(58.8) 239(75.4) 0.466 (0.226–0.984) 0.040 * ≤ 18 14(41.2) 78(24.6) Total lung V 30 (%) > 10 33(97.1) 276(87.1) 4.902 (1.012–88.337) 0.118 ≤ 10 1(2.9) 41(12.9) Total MLD (Gy) > 8 34(100) 276(87.1) 1.4247e + 07 (0-Inf) 0.987 ≤8 0(0) 41(12.9) Contralateral lung V 5 (%) >11 33(97.1) 254(80.1) 8.185 (1.711-146.936) 0.040 * ≤11 1(2.9) 63(19.9) Contralateral lung V 20 (%) > 4 20(58.8) 154(48.6) 1.512 (0.743–3.157) 0.259 ≤ 4 14(41.2) 163(51.4) Contralateral lung V 30 (%) > 0.3 28(82.4) 219(69.1) 2.088 (0.893–5.727) 0.114 ≥ 0.3 6(17.6) 98(30.9) Contralateral MLD (Gy) > 1.9 34(100) 266(83.9) 1.4782e + 07 (0-Inf) 0.986 ≤1.9 0(0) 51(16.1) Ipsilateral lung V 5 (%) > 62.4 18(52.9) 116(36.6) 1.949 (0.956–4.009) 0.066 . ≤62.4 16(47.1) 201(63.4) Ipsilateral lung V 20 (%) > 45 15(44.1) 87(27.4) 2.087 (1.002–4.281) 0.045 * ≤45 19(55.9) 230(72.6) Ipsilateral lung V 30 (%) > 25 28(82.4) 218(68.8) 2.119 (0.906–5.811) 0.107 ≤ 25 6(17.6) 99(31.2) Ipsilateral MLD (Gy) >19 20(58.8) 134(42.3) 1.738 (0.854–3.631) 0.131 ≤19 14(41.2) 183(57.7) Heart V 50 (%) > 6 14(41.2) 88(27.8) 1.822 (0.866–3.741) 0.105 ≤6 20(58.8) 229(72.2) MHD (Gy) > 8 21(61.8) 212(66.9) 0.800 (0.390–1.699) 0.549 ≤8 13(38.2) 105(33.1) Hematological indicators Pre-RT WBC (10 9 /L) > 6.5 23(67.6) 160(50.5) 2.052 (0.988–4.507) 0.061 . ≤ 6.5 11(32.4) 157(49.5) Pre-RT NEU (10 9 /L) > 4.9 17(50.0) 101(31.9) 2.139 (1.043–4.386) 0.037 * ≤ 4.9 17(50.0) 216(68.1) Pre-RT LYM (10 9 /L) > 1.7 8(23.5) 139(43.8) 0.394 (0.162–0.861) 0.027 * ≤ 1.7 26(76.5) 178(56.2) Pre-RT MON (10 9 /L) > 0.3 31(91.2) 248(78.2) 2.875 (0.988–12.226) 0.088 . ≤ 0.3 3(8.8) 69(21.8) Pre-RT EOS (10 9 /L) > 0.1 18(52.9) 152(47.9) 1.221 (0.600-2.504) 0.580 ≤ 0.1 16(47.1) 165(52.1) Pre-RT BASO (10 9 /L) >0.025 12(35.3) 88(27.8) 1.419 (0.655–2.946) 0.357 ≤0.025 22(64.7) 229(72.2) Pre-RT NLR > 2.8 20(58.8) 132(41.6) 2.002 (0.983–4.185) 0.058 . ≤ 2.8 14(41.2) 185(58.4) Pre-RT dNLR > 1.9 22(64.7) 133(42.0) 2.536 (1.232–5.462) 0.013 * ≤ 1.9 12(35.3) 184(58.0) Pre-RT SIRI > 1.6 18(52.9) 110(34.7) 2.117 (1.038–4.357) 0.039 * ≤ 1.6 16(47.1) 207(65.3) Pre-RT MLR > 0.3 24(70.6) 176(55.5) 1.923 (0.914–4.336) 0.096 . ≤ 0.3 10(29.4) 141(44.5) Pre-RT dMLR > 0.3 30(99.2) 231(72.9) 2.792 (1.063–9.604) 0.061 . ≤ 0.3 4(11.8) 86(27.1) Post-RT WBC (10 9 /L) > 5.9 19(55.9) 88(27.8) 3.296 (1.609–6.869) 0.001 ** ≤ 5.9 15(44.1) 229(72.2) Post-RT NEU (10 9 /L) > 5.3 15(44.1) 50(15.8) 4.216 (1.987–8.841) 1.0 7(20.6) 112(35.3) 0.475 (0.185–1.068) 0.090 . ≤ 1.0 27(79.4) 205(64.7) Post-RT MON (10 9 /L) > 0.5 21(61.8) 123(38.8) 2.548 (1.245-5.400) 0.012 * ≤ 0.5 13(38.2) 194(61.2) Post-RT EOS (10 9 /L) > 0.1 20(58.8) 245(77.3) 0.420 (0.203–0.888) 0.020 * ≤ 0.1 14(41.2) 72(22.7) Post-RT BASO (10 9 /L) >0.055 2(5.9) 5(1.6) 3.900 (0.543–18.914) 0.112 ≤0.055 32(94.1) 312(98.4) Post-RT NLR > 4.5 26(76.5) 158(49.8) 3.271 (1.498–7.930) 0.005 ** ≤ 4.5 8(23.5) 159(50.2) Post-RT dNLR > 3.3 17(50.0) 86(27.1) 2.686 (1.306–5.531) 0.007 ** ≤ 3.3 17(50.0) 231(72.9) Post-RT SIRI > 3.4 18(52.9) 70(22.1) 3.970 (1.924–8.269) 0.8 15(44.1) 79(24.9) 2.378 (1.139–4.893) 0.019 * ≤ 0.8 19(55.9) 238(75.1) Post-RT dMLR > 0.8 22(64.7) 137(43.2) 2.409 (1.170–5.186) 0.020 * ≤ 0.8 12(35.3) 180(56.8) In univariable analysis, the following variables were indicated to be significantly associated with severe RP: age > 65 years, ILD, without surgery history, FEV1 58.3Gy, GTV > 29.6 cm 3 , PTV1(GTV + 1cm) > 202cm 3 , PTV1-PTV2 > 15cm 3 , whole lung volume > 2409.1cm 3 , total lung V 20 > 18%, contralateral lung V 5 > 11%, ipsilateral lung V 20 > 45%, pre-RT NEU > 4.9×10 9 /L, pre-RT LYM > 1.7×10 9 /L, pre-RT dNLR > 1.9, pre-RT SIRI > 1.6, pre-RT NMLR > 2.8, post-RT NEU > 5.3×10 9 /L, post-RT MON > 0.5×10 9 /L, post-RT EOS > 0.1×10 9 /L, post-RT NLR > 4.5, post-dNLR > 3.3, post-RT SIRI > 3.4, post-RT MLR > 0.8 and post-RT dMLR > 0.8. To avoid overfitting, factors with P value 11%, ipsilateral V20 (IpsiV 20 ) > 45%, pre-RT dNLR > 1.9 and post-RT SIRI > 3.4 showed significant impact on the incidence of severe RP. Then multivariate logistic regression was conducted on these factors above. In multivariate analysis, ILD (OR:13.903, 95%CI:4.490-45.016, p < 0.001), ContraV 5 (OR: 15.078, 95%CI: 2.670–290.757, p = 0.013), ipsiV 20 (OR:2.344, 95%CI: 1.038–5.301, p = 0.039), pre-RT dNLR (OR:2.823, 95%CI: 1.250–6.795, p = 0.015) and post-RT SIRI (OR: 3.756, 95%CI: 1.688–8.481, p = 0.001) were independent prognosticators of severe RP (Table 3 ). These factors were then utilized in the nomogram building. Table 3 Multivariate and ROC analysis of the clinical factor, DVH parameters, hematological Indicators and nomogram model in predicting grade ≥ 3 RP Multivariate analysis ROC curve OR (95%CI) P-value AUC (95% CI) P-value Clinical factors ILD 13.903 (4.490-45.016) < 0.001 0.613 (0.537–0.689) 11% 8.925 (1.728-164.761) 0.038 0.585 (0.548–0.621) 0.008 IpsiV 20 > 45% 2.344 (1.038–5.301) 0.039 0.583 (0.495–0.672) 0.021 PBL markers Pre-RT dNLR > 1.9 2.823 (1.250–6.795) 0.015 0.614 (0.528-0.700) 0.006 Post-RT SIRI > 3.4 3.756 (1.688–8.481) 0.001 0.654 (0.566–0.743) < 0.001 Nomogram* - - 0.782 (0.701–0.864) < 0.001 Development and validation of the nomogram Based on the multivariate logistic regression coefficients, a predictive model was visually presented as a nomogram (Fig. 1 ). The ROC curves of ILD, contra V 5 > 11%, ipsiV 20 > 45%, pre-RT dNLR > 1.9, post-RT SIRI > 3.4, and the nomogram are shown in Fig. 2 A. The predictive model showed an excellent AUC of 0.782 (95%CI: 0.701–0.864) by ROC, which was much higher than each parameter alone (ILD: 0.613, 95%CI: 0.537–0.689; ContraV5 > 11%: 0.585, 95%CI: 0.548–0.621; IpsiV20:0.583, 95%CI: 0.495–0.672; Pre-RT dNLR > 1.9: 0.614, 95%CI: 0.528-0.700; Post-RT SIRI > 3.4: 0.654, 95%CI: 0.566–0.743 (Table 3 ). Also, a calibration curve showed favorable consistency between the predicted severe RP and the actual observation (Fig. 2 B), and DCA showed satisfactory positive net benefits of the nomogram among most of the threshold probabilities, indicating favorable potential clinical effect of the model (Fig. 2 C). Severe RP and survival The influence of RP on survival outcomes was further investigated in these patients. The median follow-up duration was 19.8 months (range, 1.4 to 52.9 months). At the time of data cut-off, 46 patients were lost to follow-up, and 62 patients (20.3%) had died. All patients (n = 305) were divided into the non-RP group (n = 95) and RP group (n = 210). The RP group was further subdivided into severe RP group (n = 32) and mild RP group (n = 178). As shown in Fig. 3 A, the median OS was not reached for the RP group and the non-RP group, and there was no significant difference in OS between these two groups (p = 0.334). And the median OS was 30.8 months for severe RP group, while was not reached for mild RP subgroup. Compared with patients with mild RP, patients with severe RP performed a worse OS (Fig. 3 B) (p = 0.027). Discussion At present, severe RP is one of the most important clinically relevant toxicity of thoracic radiation for patients with locally-advanced NSCLC, which not only affect the following treatment of patients, but also severely influenced the life quality and long-term survival. Thus, reducing the occurrence of severe RP is a critical goal for clinicians. While nowadays, there were still no effective method to predict the incidence of severe RP. In the present study, we collected data from 351 patients with locally-advanced NSCLC patients and after a long-term follow-up of up to 52.9 months, our data indicated that subclinical ILD, contralateral V5 > 11%, ipsilateralV20 > 45%, pre-RT dNLR > 1.9 and post-RT SIRI > 3.4 were the independent prognosticators of severe RP among patients with locally-advanced NSCLC receiving thoracic RT. The internal validation of the constructed nomogram demonstrated its superiority compared with any single hematological, dosimetric or clinical factor alone. These findings can be used to accurately identify patients with high risk for severe RP, and indicated that individualized and precise radiotherapy regimens can significantly reduce the occurrence of severe RP and can improve the prognosis of RP and the quality of life of patients. Compared to other similar researches [ 29 , 32 , 33 ] , it must be pointed out that to the best of our knowledge, our study is the first to systematically integrate clinical factors, peripheral blood biomarkers and dose-volume parameters to develop a predictive model for the occurrence of severe RP in such a large sample of patients with locally-advanced NSCLC treated with thoracic radiotherapy. Our study reported that 9.7% patients developed severe RP, which was in accordance with those reported in previous studies (about 5-11.7%) [ 34 – 36 ] . And in our study, severe RP conferred a worse overall survival comparing with mild RP (30.8m vs. NR, p = 0.027), similar conclusions were reached by other recent studies [ 37 , 38 ] , while there was no significant difference between RP and non-RP patients (20.4m vs. 17.6m, p = 0.330). It underlined that early detection and timely intervention of severe RP is of great importance, which could help prolong the survival of patients. We confirmed that patients with ILD intended to have higher risk of severe RP, it was in line with our preliminary studies [ 39 , 40 ] , and similar results were also in reported by other researches [ 23 , 41 ] . Due to similar mechanisms, lung cancer (LC) patients have a high incidence of ILD, and ILD patients are also prone to develop lung cancer [ 42 , 43 ] . Thoracic radiotherapy can be a choice for LC-ILD patients, while radiotherapy is contra-indicated in severe ILD, producing RP rates of up to 43% [ 44 ] . So clinical radiotherapy decisions must be cautious about the risk of severe RP in patients with ILD, and more conservative limits of lung dose should be used with a diagnosis or radiologic evidence of ILD, and specific measures should be taken in the early period of RP. For patients with locally advanced NSCLC treated with definitive chemo-radiotherapy, the NCCN guideline recommended lung dose–volume constraints for conventionally fractionated RT: total lung V 20 ≤ 35–40% and MLD ≤ 20 Gy [ 5 ] , while there were there is still no principle or recommendation for evidence-based medicine for the dose limits of contralateral or ipsilateral lung dose. In present study, contralateral V5(> 11%) and ipsilateral V20(> 45%) showed significant associations with severe RP incidence. Bongers’s research have shown that contraV5 were predictor for grade ≥ 3 RP [ 45 ] , while the optimal cutoff point has not been confirmed. And Zhao’s research also indicated that contralateral V5 were related to the clinical outcome of patients with severe RP (p = 0.057) [ 33 ] . One study by Ramella showed that ipsiV20 could effectively dividing patients into high-risk and low-risk groups for RP with the threshold value as 52% [ 26 ] . And Agrawal’s study found that ipsiV20 were significantly correlating with RP on univariate analysis, and mean ipsiV20 were 60% for patients with RP [ 46 ] . The threshold of ipsiV20 in our study is lower than in mentioned studies, it may because with the development of multidisciplinary therapy, systemic administration may increase the occurrence of RP, resulting in a decrease in the threshold of ipsilateral lung dose. A growing body of evidence indicates that blood-based biomarkers can be used to predict radiation-related toxicity [ 47 ] . Based on our analysis in present study, we found pre-RT dNLR greater than 1.9 and post-RT SIRI greater than 3.4 were the most significant risk hematological biomarker for the development of severe RP. For dNLR, several researches have shown its relation to worse OS and other treatment toxicities, for example, Hsiang found that elevated pre-RT dNLR is associated with worse OS and development of liver toxicity for patients with hepatocellular carcinoma after stereotactic body radiotherapy (SBRT), and dNLR ≥ 1.9 was an optimal cut-off value for determining liver toxicity risk [ 48 ] , which was exactly consistent with our findings. Cox’s research showed that an elevated pre-treatment dNLR was an independent prognostic biomarker for OS and PFS in oesophageal cancer patients treated with definitive CRT with the cutoff of 2 [ 49 ] , which was close to our data. Although the potential mechanisms are unclear, it might be due to that RP is a kind of immune-mediated hypersensitive pneumonia actually [ 50 ] , and T lymphocyte subsets play a dominant role in the cellular immune response and may be involved in RP [ 51 ] . What’s more, patients with lower lymphocyte count have been shown to have negative impact on the immune system, leading to the development of infections [ 52 ] . For SIRI, our previous study has demonstrated that pretreatment SIRI are independent predictors of OS in stage Ⅲ NSCLC [ 53 ] . As previously reported, decreased lymphocyte count after RT may be a clinical indicator in the occurrence of RP [ 29 ] , and even the severity of RP was associated with the degree of lymphocyte decrease [ 54 ] , and the potential mechanism of lymphopenia after RT might be the effect of local irradiation of circulating lymphocyte in the blood pool [ 55 ] . And in the early phase of RP, after alveolar and interstitial edema is formed, then inflammatory cells outside like monocyte-derived pulmonary macrophages and neutrophils are recruited and accumulated here to effect action [ 56 ] . Thus, the relationship between post-RT SIRI and RP are possibly related to different changes in immune cells, including the decrease of lymphocytes and the increase of neutrophils and monocytes after RT. Several limitations should be mentioned here. Firstly, due to its retrospective nature, selective bias existed in the present study, thus more prospective studies are necessary in the future. Secondly, although the internal validation showed a relatively excellent AUC (0.782) of the present predictive model, the result would be more convincing if it had been verified by an external validation, and the model needs to be further modified and verified in future studies before applied to the clinic. What’s more, there are several biomarkers including the vitronectin (VTN) and the single nucleotide polymorphisms (SNPs) have also been investigated and considered as predictors of RP recently [ 57 , 58 ] , hence from the perspective of precision medicine, a more comprehensive prediction model for severe RP combining the individual genomic information, dosimetric, hematological and clinical parameters should be developed in the future. Conclusion In conclusion, early detection and early intervention of severe RP is of great importance due to the impact on long-term outcome. In the era of multidisciplinary comprehensive treatment for locally advanced NSCLC, lung dose constrains should be more rigorous, which would exactly affect the incidence and severity of RP. Moreover, biomarkers in peripheral blood could predict severe RP in some extent, and could be used to accurately identify high-risk patients. The multivariate analysis identified the ILD, contraV 5 (> 11%), ipsiV 20 (> 45%), pre-RT (> 1.9) and post-RT SIRI (> 3.4) as independent risk factors for severe RP in patients with locally advanced NSCLC receiving thoracic RT. A predictive nomogram was built and its efficiency confirmed by internal validation. Although needing further verification, our work still has a value for identifying patients at high risk of severe RP and evaluation of treatment plans. Abbreviations NSCLC non-small cell lung cancer RT radiation therapy CCRT concurrent chemoradiotherapy RP radiation pneumonitis RILI radiation-induced lung injury OS overall survival CTCAE common Terminology Criteria for Adverse Events NCCN national comprehensive cancer network ECOG-PS eastern cooperative oncology group performance status COPD chronic obstructive pulmonary disease ILD interstitial lung disease FEV1 forced expiratory volume in 1 second DLCO diffusing capacity of the lung for carbon monoxide DVH dose-volume histogram GTV gross tumor volume CTV clinical tumor volume PTV planning tumor volume MLD mean lung dose MHD mean heart dose Vx percentage of lung/heart volume receiving ≥ x Gy PBL peripheral blood leukocyte NLR neutrophil-to-lymphocyte dNLR derived neutrophil-to-lymphocyte ratio MLR monocyte-to-lymphocyte ratio dMLR derived monocyte-to-lymphocyte ratio SIRI systemic inflammation response index WBC white blood cell NEU neutrophil LYM lymphocyte MON monocyte EOS eosinophil 3D-CRT three-dimensional conformal radiotherapy IMRT intensity-modulated radiotherapy VMAT volumetric arc therapy 4D-CT four-dimensional computed tomography PET/CT positron emission tomography/computed tomography FDG-PET fluorodeoxyglucose positron emission tomography LASSO least absolute shrinkage and selection operator ROC receiver operating characteristic AUC area under the ROC curve DCA decision curve analysis Declarations Ethical approval declaration: The study was approved by the Ethic Committee of Shanghai Pulmonary Hospital (No. L22-357). Human Ethics and Consent to Participate declarations: not applicable. Clinical trial number: not applicable. Consent to publish: not applicable. Funding: This work was supported by the 2023 Development Fund of Discipline-Department of Radiotherapy and the 2021 Shanghai Municipal Science and Technology Commission Technical Standards Project (No. 21DZ2201900). Availability of data and materials: not applicable. Conflict of interest statement: No actual or potential conflicts of interest exist. References Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024 . CA Cancer J Clin 2024; 74(1):12-49. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries . CA Cancer J Clin 2021; 71(3):209-249. Zhang Y, Vaccarella S, Morgan E, Li M, Etxeberria J, Chokunonga E, et al. 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Front Immunol 2017; 8:506. Liang B, Lu X, Liu L, Dai J, Wang L, Bi N. Synergizing the interaction of single nucleotide polymorphisms with dosiomics features to build a dual-omics model for the prediction of radiation pneumonitis . Radiother Oncol 2024; 196:110261. Yu JH, Zhao QY, Liu Y, Zhu XR, Yang ZR, Fu XL, et al. The Plasma Levels and Polymorphisms of Vitronectin Predict Radiation Pneumonitis in Patients With Lung Cancer Receiving Thoracic Radiation Therapy . Int J Radiat Oncol Biol Phys 2021; 110(3):757-765. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfigure.docx Table1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Wu","email":"","orcid":"","institution":"Department of Radiation Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Lei-Lei","middleName":"","lastName":"Wu","suffix":""},{"id":356067618,"identity":"f1f44190-b112-47f3-bb6f-bbbb57dceec9","order_by":4,"name":"Xiao-Shuai Yuan","email":"","orcid":"","institution":"Department of Radiation Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiao-Shuai","middleName":"","lastName":"Yuan","suffix":""},{"id":356067620,"identity":"0433ea55-1ab9-4e74-ae30-820658cd2570","order_by":5,"name":"Min Hu","email":"","orcid":"","institution":"Department of Radiation Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Hu","suffix":""},{"id":356067622,"identity":"43f2246b-0731-46d7-9fa6-0c0979f0b3ed","order_by":6,"name":"Jing-Jing Kang","email":"","orcid":"","institution":"Department of Radiation Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jing-Jing","middleName":"","lastName":"Kang","suffix":""},{"id":356067624,"identity":"f28f6437-4364-41c6-b117-1e7bb8f10a09","order_by":7,"name":"Chen-Xue Jiang","email":"","orcid":"","institution":"Department of Radiation Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chen-Xue","middleName":"","lastName":"Jiang","suffix":""},{"id":356067625,"identity":"53280ebf-2c31-492a-a5e6-d4413ef34560","order_by":8,"name":"Yao-Yao Zhu","email":"","orcid":"","institution":"Department of Radiation Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yao-Yao","middleName":"","lastName":"Zhu","suffix":""},{"id":356067626,"identity":"c9a96420-93af-4e21-ada7-724ca1381334","order_by":9,"name":"Shuang-Yan Yang","email":"","orcid":"","institution":"Department of Radiation Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shuang-Yan","middleName":"","lastName":"Yang","suffix":""},{"id":356067627,"identity":"92b03447-3478-45f5-9624-1610b0c8f8d7","order_by":10,"name":"Rui-Feng Zhao","email":"","orcid":"","institution":"Department of Radiation Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Rui-Feng","middleName":"","lastName":"Zhao","suffix":""},{"id":356067628,"identity":"006171d5-87b7-4ab9-af92-a5cee55149de","order_by":11,"name":"Jian Hu","email":"","orcid":"","institution":"Department of Thoracic Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Hu","suffix":""},{"id":356067629,"identity":"548134e1-6e02-4c4d-83e4-bcdea2215b7d","order_by":12,"name":"Min-Ren Hu","email":"","orcid":"","institution":"Department of Radiation Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Min-Ren","middleName":"","lastName":"Hu","suffix":""},{"id":356067630,"identity":"4d286681-26c6-443f-ae7d-288f1c04fade","order_by":13,"name":"Hui Liu","email":"","orcid":"","institution":"Department of Radiation Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Liu","suffix":""},{"id":356067631,"identity":"72e5c991-806b-4251-96c2-6a61e3d0a07e","order_by":14,"name":"Liang Liu","email":"","orcid":"","institution":"Clinical Research Unit, Institute of Clinical Science, Zhongshan Hospital of Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Liu","suffix":""},{"id":356067632,"identity":"ce901005-1064-43c5-b1e8-2c76a2f47f6e","order_by":15,"name":"Lan Zhao","email":"","orcid":"","institution":"Department of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, Tongji University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Lan","middleName":"","lastName":"Zhao","suffix":""},{"id":356067633,"identity":"e233291b-a518-4bf9-a036-88cc6923287f","order_by":16,"name":"Ya-Ping Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYNACAwYGNvbG9h8fDGzkiNfCx3P4gOSMgjRj4i2Sk0hLkOb5cDiRsPk3cgw/FxTcsWtjyDEwtjFgTmBgP3x0Az4tkjNyjKVnGDxLbmM4Y5CcY8CWx8CTlnYDnxZ+iRwDaR6Dw8lsjD0Gh3MMeIoZJHjM8Gphk8gx/g3Wwsxj2GxhIJHYQEgL0BYzkC12bGxsycwMBgaEtUj2PCuznmFwOIGNh/kY0G0JxmyE/GJwPHnz7YI/h+3l5z9sY/jx578cP/vhY3i1MDBwGDADycQGuO/wKwcB9gcgLfaEFY6CUTAKRsGIBQCrtER3btX5fQAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Radiation Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Ya-Ping","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2024-08-24 06:22:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4967531/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4967531/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66904068,"identity":"b002a7e2-a7d4-42b8-8edb-199443249144","added_by":"auto","created_at":"2024-10-17 17:37:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37186,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram predicting the occurrence of severe RP.\u003c/strong\u003e For each individual patient, five lines are drawn upward to determine the points received from the five variables in the nomogram. The sum of these points is located on the ‘‘Total Points” axis, and a line is drawn downward to determine the likelihood of this patient to have severe RP.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4967531/v1/943aa4db52a41dd73a522dd3.png"},{"id":66904071,"identity":"5be83f74-08fa-4249-b24d-593a91e8abf7","added_by":"auto","created_at":"2024-10-17 17:37:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":790808,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of the nomogram\u003c/strong\u003e. A. ROC curves of ILD, contralateral V5\u0026gt;11%, ipsilateral V20\u0026gt;45%, pre-RT dNLR\u0026gt;1.9, post-RT SIRI\u0026gt;3.4, and the predictive model. B. Calibration curves of the nomogram predicting the occurrence of severe RP. The x-axis and y-axis indicate the predicted and actual probabilities of having severe RP, respectively. C. Decision curves of the nomogram predicting the occurrence of severe RP. The x-axis shows the threshold probabilities. The y-axis measures the net benefit, which is calculated by adding the true positives and subtracting the false positives. The horizontal line along the x-axis assumes that no patient will have severe RP whereas the blue line assumes that all patients will have severe RP at a specific threshold probability. The orange line represents the net benefit of using the nomogram.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4967531/v1/5a851ed88816586328e9becb.png"},{"id":66904072,"identity":"8ca31cda-ffea-4996-a2f1-31baae68c74f","added_by":"auto","created_at":"2024-10-17 17:37:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":699802,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall survival of the patients in RP/non-RP groups and severe RP/mild RP subgroups. \u003c/strong\u003eKaplan‒Meier analysis of OS in RP vs. non-RP patients (A), severe RP vs. mild RP patients (B).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4967531/v1/9db87dc2ad1945daddfffb13.png"},{"id":66905448,"identity":"3f97eae6-0caa-4187-b25a-b3395dfd10ab","added_by":"auto","created_at":"2024-10-17 17:54:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4760054,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4967531/v1/52dc1066-e172-411b-a383-af205ae8b3fd.pdf"},{"id":66904069,"identity":"eb5dddc0-5f43-4cab-87f8-77c1cc1be789","added_by":"auto","created_at":"2024-10-17 17:37:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":171582,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-4967531/v1/90d2c559dd35145a9127f275.docx"},{"id":66904070,"identity":"15f43f74-bc8c-40b3-bd9e-24a44c8124a7","added_by":"auto","created_at":"2024-10-17 17:37:36","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18490,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4967531/v1/b83167bfcf4688e0f182c7cb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting severe radiation pneumonitis in patients with locally- advanced non-small cell lung cancer after thoracic radiotherapy: Development and internal validation of a nomogram based on the clinical, hematological and dose–volume histogram parameters","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNowadays, lung cancer is one of the most common malignancies and the leading cause of cancer-related death worldwide\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, and non-small cell lung cancer (NSCLC) accounts for most cases\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Radiation therapy (RT) has played an important role in the treatment of NSCLC for many years, especially concurrent chemoradiotherapy (CCRT) remains the current standard of care for patients with inoperable locally advanced NSCLC\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. However, the benefits of RT are occasionally disturbed by relevant adverse effects, among which radiation pneumonitis (RP) is one of the most common thoracic RP-related toxicity\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, and is one of a main dose-limiting factors that affects the efficacy of RT\u003csup\u003e[\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRP was been defined as the acute injury stage of radiation-induced lung injury (RILI)\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, and could be stratified into five grades by the clinical manifestation and imaging performance through Common Terminology Criteria for Adverse Events version 5.0 (CTCAE v. 5.0)\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. RP was been reported with an incidence rate varying from 5%-40%\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, and mild RP consisted majority of cases\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. RP can lead to chronic respiratory insufficiency, especially for severe RP, i.e., RP of grade 3 or higher, can seriously influence the quality of life of patients, and may even directly threaten the life of patients, leading to treatment-related death\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, with a mortality as high as 30%\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTherefore, early detection and intervention are crucial for the management of severe RP, and robust markers for the prediction of severe RP is of urgent needed. Various clinical, dosimetric and hematological factors have been found to be associated with the incidence of RP, the former include age, the performance status (PS), smoking status, chronic obstructive pulmonary disease (COPD), pulmonary emphysema, interstitial lung disease (ILD), pulmonary function, concurrent chemotherapy \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR20 CR21 CR22\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Among dose-volume histogram (DVH) metrics, gross tumor volume (GTV), mean lung dose (MLD), percent of the lung volume receiving\u0026thinsp;\u0026ge;\u0026thinsp;5, 20, 30Gy (V\u003csub\u003e5\u003c/sub\u003e/V\u003csub\u003e20\u003c/sub\u003e/V\u003csub\u003e30\u003c/sub\u003e) and heart dosimetric variables were reported to be related to the occurrence of RP\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. As for peripheral blood leukocyte (PBL) biomarkers, lymphocytes, neutrophil-to-lymphocyte (NLR), derived neutrophil-to-lymphocyte ratio (dNLR), monocyte-to-lymphocyte ratio (MLR) and derived monocyte-to-lymphocyte ratio (dMLR) have previously been reported to be predictors for RP\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, studies on risk factors and prediction model for severe RP, especially in patients with locally-advanced NSCLC, are limited. Therefore, in order to optimize risk stratification and predict the risk of severe RP more accurately, as well as further guide individualized treatment management decisions, we performed a retrospective study to investigate the potential risk factors for severe RP, including clinical characteristics, DVH parameters and PBL indicators, and aimed at generating a predictive model to quantify risk of severe RP in the population of locally-advanced NSCLC patients treated with thoracic RT. What\u0026rsquo;s more, we further made a long-term follow-up to track the survival status of patients, and identified the impact of severe RP on survival.\u003c/p\u003e"},{"header":"Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003ePatients\u003c/h2\u003e\n \u003cp\u003eThis retrospective study was approved by the ethics committee of Shanghai Pulmonary Hospital, Shanghai, China. We retrospectively reviewed the medical charts of patients with locally-advanced NSCLC receiving thoracic radiotherapy (total dose\u0026thinsp;\u0026gt;\u0026thinsp;50 Gy and single dose 2 Gy) between April 2018 and August 2022. The inclusion criteria were as follows: (1) histologically or cytologically proven stage Ⅲ NSCLC, (2) with an Eastern Cooperative Oncology Group performance status (ECOG PS) of 0\u0026ndash;2, (3) patients received thoracic radiotherapy from 2018 to 2022, (5) no previous thoracic radiation therapy.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eDefinition of clinical, DVH and PBL factors\u003c/h2\u003e\n \u003cp\u003eWe retrospectively collected clinical, dosimetric and hematological factors for analysis. The clinical factors included age, gender, ECOG PS, smoking status, presence of underlying lung disease (ILD, COPD and pulmonary emphysema), surgery history, total RT dose, chemotherapy history, regimen and sequence, and pulmonary function, which including forced expiratory volume in 1 second (FEV1) and diffusing capacity of the lung for carbon monoxide (DLCO). Pulmonary function testing was done immediately before RT.\u003c/p\u003e\n \u003cp\u003eThe dosimetric factors were collected as follows: GTV, planning tumor volume (PTV), total/contralateral/ipsilateral lung MLD and V\u003csub\u003e5\u003c/sub\u003e/\u003csub\u003e20\u003c/sub\u003e/\u003csub\u003e30\u003c/sub\u003e, mean heart dose (MHD) and heart V\u003csub\u003e50\u003c/sub\u003e. V\u003csub\u003ex\u003c/sub\u003e was defined as the percentage of lung/heart volume receiving\u0026thinsp;\u0026ge;\u0026thinsp;x Gy. DVH data were obtained from electronic radiation treatment planning documents.\u003c/p\u003e\n \u003cp\u003eWhat\u0026rsquo;s more, we collected hematological factors both pre-radiotherapy (pre-RT) and post-radiotherapy (post-RT), and the latter were defined as the data at one month after the completion of radiotherapy. The collected hematological indicators were composed of white blood cell (WBC) count and its classification count, including the absolute count of neutrophils (NEU), lymphocytes (LYM), monocytes (MON) and eosinophils (EOS), as well as neutrophil-to-lymphocyte ratio (NLR), derived neutrophil-to-lymphocyte ratio (dNLR), monocyte-to-lymphocyte ratio (MLR), derived monocyte-to-lymphocyte ratio (dMLR) and systemic inflammation response index (SIRI). The NLR, dNLR, MLR, dMLR and SIRI were calculated as follows: \u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\u003cimg 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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eRadiotherapy\u003c/h2\u003e\n \u003cp\u003eThree-dimensional conformal radiotherapy (3D-CRT), intensity-modulated radiotherapy (IMRT) or volumetric arc therapy (VMAT) techniques were used to deliver radiotherapy courses. What\u0026rsquo;s more, four-dimensional computed tomography (4D-CT) scanning of the whole lung was performed to measure intrafraction respiratory movement and create an internal target volume to compensate respiratory motion, with CT scans at intervals of 2.5\u0026ndash;5.0 mm and each patient immobilized in the supine position with a vacuum cushion. All radiation treatment plans were generated in Pinnacle treatment planning system, and were delivered using 6\u0026ndash;10 MV beams on linear accelerators.\u003c/p\u003e\n \u003cp\u003ePositron emission tomography/computed tomography (PET/CT) should be obtained preferably within 4 weeks before treatment in the treatment position. Based on fluorodeoxyglucose positron emission tomography (FDG-PET) and the diagnostic CT images, we carefully contoured GTV on the planning CT images, which consisted of the known extent of disease (primary and nodal) on imaging and pathologic assessment, The internal GTV was created using a 4D-CT image. The National Comprehensive Cancer Network (NCCN) guidelines suggest the clinical target volume (CTV) included regions of presumed microscopic extent or dissemination, was expanded by 6-8mm from the internal GTV, and no prophylactic lymph node area was added\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. The PTV margin of 5 mm was added for setup uncertainty and respiratory motion. IGRT was applied to real-time monitoring to abate set-up errors. The treatment dose was prescribed to cover 95% of the PTV. The mean lung dose (MLD), the percentage of total lung volume exceeding 20 Gy (V\u003csub\u003e20\u003c/sub\u003e) and 5 Gy (V\u003csub\u003e5\u003c/sub\u003e), as well as the dose constraints for normal tissues (spinal cord, esophagus, and heart) were limited appropriately according to the NCCN guidelines\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eChemotherapy\u003c/h2\u003e\n \u003cp\u003eAdjuvant concurrent or sequential chemotherapy was applied for part of patients based on the NCCN guidelines\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e, and mostly were platinum-based doublets (cisplatin combined with etoposide, vinorelbine or paclitaxel). All the doses and adjustments of the chemotherapy regimen followed the NCCN guidelines\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eEndpoint definitions\u003c/h2\u003e\n \u003cp\u003eThe primary observation endpoint of this study was grade\u0026thinsp;\u0026ge;\u0026thinsp;3 RP as defined in CTCAE v5.0\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, which was graded based on the existence and severity of the clinical symptoms, whether pneumonitis intervention is required, and on the extent of pulmonary fibrosis and accompanying symptoms. RP was diagnosed by professional radiologists and respiratory physicians on the basis of clinical symptoms and changes in CT images, the specific criteria were described in our previous review\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. The time to RP was defined as the time interval from the start of radiation treatment to the diagnosis of RP and was calculated using Kaplan-Meier method.\u003c/p\u003e\n \u003cp\u003eThe secondary endpoint was overall survival (OS), which were measured from the start of radiation treatment. The follow-up evaluation was performed 1 month after the completion of thoracic RT, every 3 months for up to two years, then every 6 months for the third year and thereafter. All available follow-up documents were carefully reviewed including clinical records, chest radiographic images, follow-up clinical assessment notes and electronic records until the last follow-up or death of the patients.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eThe predictive model was built as follows: firstly, we measured the impact of clinical characteristics, dose-volume parameters and hematological indicators on the incidence of severe RP using univariable logistic regression model. Secondly, factors with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analyses were assessed through least absolute shrinkage and selection operator (LASSO) regression analysis to obtain the crucial severe RP-associated factors. Thirdly, a multivariate logistic regression analysis was conducted to select the severe RP-associated factors for establishing a predictive model. Finally, factors with significant predictive value in multivariate analysis were used to build the nomogram. Receiver operating characteristic (ROC) curve analysis was used to establish optimal cut points for continuous variables.\u003c/p\u003e\n \u003cp\u003eThe validation of the nomogram was conducted using the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve (1000 bootstrap resamples) and decision curve analysis (DCA). The ROC curves were used to estimate the discrimination ability of the nomogram and each predictor alone. Calibration curve was used to compare the predicted probability with the observed probability of severe RP. DCA was performed to illustrate the clinical usefulness of the nomogram by quantifying the net benefits at different threshold probabilities.\u003c/p\u003e\n \u003cp\u003eWhat\u0026rsquo;s more, the Kaplan-Meier method was used to estimate OS between patients with non-RP vs. RP, and grade 1\u0026ndash;2 RP vs. grade 3 RP.\u003c/p\u003e\n \u003cp\u003eStatistical analyses were performed with R (Version 4.1.0). All tests were 2-sided, with a P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered to indicate statistical significance.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eA total of 351 patients received thoracic radiotherapy between April 2018 and August 2022 were enrolled and a summary of baseline characteristics was listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Of all the 351 patients, 115(32.8%) didn\u0026rsquo;t experience RP, 236 (67.2%) developed RP (grade 1: n\u0026thinsp;=\u0026thinsp;91, 25.9%; grade 2: n\u0026thinsp;=\u0026thinsp;111, 31.6%; grade 3: n\u0026thinsp;=\u0026thinsp;34, 9.7%; grade 4 and 5: n\u0026thinsp;=\u0026thinsp;0), and 34 (9.7%) developed severe RP. The median interval from the start of RT to the occurrence of RP was 3.7 months (range, 0.5\u0026ndash;28.9 months), and the median occurrence time of severe RP was 3.1 months (range, 1.0-11.6 months).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of all patients (n\u0026thinsp;=\u0026thinsp;351)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of patients (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65 (58\u0026ndash;70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e Male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e284(80.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67(19.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEOCG PS\u003c/p\u003e \u003cp\u003e0\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003eSmoking history\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eLung disease\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eILD\u003c/p\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003cp\u003eEmphysema\u003c/p\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eFEV1%, Median (IQR)\u003c/p\u003e \u003cp\u003eDLCO%, Median (IQR)\u003c/p\u003e \u003cp\u003eRadiotherapy dose(cGy), Median (IQR)\u003c/p\u003e \u003cp\u003eFractionation, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59(16.8)\u003c/p\u003e \u003cp\u003e271(77.2)\u003c/p\u003e \u003cp\u003e21(6.0)\u003c/p\u003e \u003cp\u003e171(48.7)\u003c/p\u003e \u003cp\u003e180(51.3)\u003c/p\u003e \u003cp\u003e226(64.4)\u003c/p\u003e \u003cp\u003e21(6.0)\u003c/p\u003e \u003cp\u003e17(4.8)\u003c/p\u003e \u003cp\u003e87(24.8)\u003c/p\u003e \u003cp\u003e94(26.8)\u003c/p\u003e \u003cp\u003e257(73.2)\u003c/p\u003e \u003cp\u003e85.1 (70.8\u0026ndash;96.8)\u003c/p\u003e \u003cp\u003e91.9 (75.6\u0026ndash;106.0)\u003c/p\u003e \u003cp\u003e5800 (5000\u0026ndash;6000)\u003c/p\u003e \u003cp\u003e26 (25\u0026ndash;30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e No chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(7.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e Include TAX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149(42.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e Other regimens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e177(50.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemoradiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46(13.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e280(79.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRT alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(7.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGTV (cm\u003csup\u003e3\u003c/sup\u003e), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.1 (28.5-102.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal lung V\u003csub\u003e5\u003c/sub\u003e (%), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.8 (32.4\u0026ndash;43.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal lung V\u003csub\u003e20\u003c/sub\u003e (%), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.1 (17.8\u0026ndash;24.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal lung V\u003csub\u003e30\u003c/sub\u003e (%), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.0 (11.4\u0026ndash;17.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal lung MLD (cGy), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1112.5 (910.2-1250.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContralateral lung V\u003csub\u003e5\u003c/sub\u003e (%), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.1 (12.7\u0026ndash;25.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContralateral lung V\u003csub\u003e20\u003c/sub\u003e (%), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8 (1.0-7.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContralateral lung V\u003csub\u003e30\u003c/sub\u003e (%), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3 (0.1\u0026ndash;3.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContralateral lung MLD (cGy), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e375.0 (263.8-536.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIpsilateral lung V\u003csub\u003e5\u003c/sub\u003e (%), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.3 (50.3\u0026ndash;68.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIpsilateral lung V\u003csub\u003e20\u003c/sub\u003e (%), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.2 (33.2\u0026ndash;46.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIpsilateral lung V\u003csub\u003e30\u003c/sub\u003e (%), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.6 (23.5\u0026ndash;35.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIpsilateral lung MLD (cGy), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1891.3 (1585.6-2185.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-RT Lymphocytes (10\u003csup\u003e6\u003c/sup\u003e), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6 (1.2-2.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-RT Monocytes (10\u003csup\u003e6\u003c/sup\u003e), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5 (0.4\u0026ndash;0.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-RT Eosinophils (10\u003csup\u003e6\u003c/sup\u003e), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1 (0.1\u0026ndash;0.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-RT NLR, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6 (1.8\u0026ndash;3.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-RT dNLR, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8 (1.3\u0026ndash;2.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-RT MLR, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3 (0.2\u0026ndash;0.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-RT dMLR, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4 (0.3\u0026ndash;0.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-RT SIRI (10\u003csup\u003e6\u003c/sup\u003e), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3 (0.8-2.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-RT Lymphocytes (10\u003csup\u003e6\u003c/sup\u003e), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8 (0.6\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-RT Monocytes (10\u003csup\u003e6\u003c/sup\u003e), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5 (0.3\u0026ndash;0.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-RT Eosinophils (10\u003csup\u003e6\u003c/sup\u003e), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1 (0.1\u0026ndash;0.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-RT NLR, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.6 (3.1\u0026ndash;6.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-RT dNLR, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5 (1.8\u0026ndash;3.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-RT MLR, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5 (0.4\u0026ndash;0.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-RT dMLR, Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7 (0.5\u0026ndash;1.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-RT SIRI (10\u003csup\u003e6\u003c/sup\u003e), Median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0 (1.3\u0026ndash;3.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eIQR, interquartile range; ECOG, Eastern Cooperative Oncology Group; PS, performance status; ILD, interstitial lung disease; COPD, chronic obstructive pulmonary disease; FEV1, forced expiratory volume in 1 second; DLCO, diffusing capacity of the lung for carbon monoxide; CCRT, concurrent chemoradiation therapy; SCRT, sequential chemoradiation therapy; GTV, gross target volume; V\u003csub\u003ex\u003c/sub\u003e: the percentage of the lung volume that received more than x Gy, respectively; MLD: mean lung dose; NLR: neutrophil lymphocyte ratio; dNLR: derived neutrophil lymphocyte ratio; SIRI: systemic inflammation response index .\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis study included 284(80.9%) men and 67(19.1%) women, with a median age of 65 years (range, 40\u0026ndash;84 years), and 171(48.7%) patients had a history of smoking. 120 (34.2%) patients had underlying lung disease, including 21(6.0%) with ILD, 17(4.8%) with COPD and 87(24.8%) with emphysema. Totally 326 (92.9%) patients were treated with combined radiation and chemotherapy regimens, and 149 (42.5%) patients using paclitaxel regimen. 46(13.1%) patients received concurrent chemoradiotherapy and 280 (79.8%) patients received sequential chemotherapy, and 94 (26.8%) also underwent surgery.\u003c/p\u003e \u003cp\u003eFor all patients, 50\u0026ndash;66 Gy were delivered in 1.8\u0026ndash;2.0 Gy/fractions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate, LASSO and multivariate analyses\u003c/h2\u003e \u003cp\u003eIn order to investigate what factors might differ the incidence of severe RP, univariate Logistic regression analysis was firstly performed, with the optimal ROC cut-off values calculated for continuous variables, and the results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate analysis of clinical factors in predicting severe RP\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWith severe RP (n\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWithout severe RP (n\u0026thinsp;=\u0026thinsp;317)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (70.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e156(49.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.477 (1.178\u0026ndash;5.584)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e161(50.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27(79.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e257(81.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.901 (0.393\u0026ndash;2.332)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60(18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECOG PS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17(5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.353 (0.646\u0026ndash;6.859)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(88.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e300(94.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e156(49.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.815 (0.394\u0026ndash;1.656)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19(55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e161(50.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eILD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12(3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.734 (6.409\u0026ndash;39.390)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16(5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.570 (0.031\u0026ndash;2.935)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.591\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmphysema\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78(24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.103 (0.470\u0026ndash;2.386)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91(28.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.240 (0.057\u0026ndash;0.695)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.021 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31(91.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e226(71.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31(91.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e296(93.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.733 (0.235\u0026ndash;3.226)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21(6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePaclitaxel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e131(41.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.597 (0.785\u0026ndash;3.279)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e165(52.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy sequence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44(13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.388 (0.061\u0026ndash;1.343)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(85.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e251(79.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRT alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22(6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEV1(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21(61.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129(40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.651 (1.136\u0026ndash;6.940)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.032 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e188(59.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLCO-SB (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e13(38.2)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e78(24.6)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e2.524 (0.986\u0026ndash;6.990)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e0.060 .\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e21(61.8)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e239(75.4)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDVH parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRT dose (Gy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(67.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e152(47.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.270 (1.093\u0026ndash;4.986)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.033 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e165(52.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGTV (cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e218(68.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.298 (0.137\u0026ndash;0.646)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002 **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19(55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99(31.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTV1(GTV\u0026thinsp;+\u0026thinsp;1cm) (cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e201(63.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.353 (0.163\u0026ndash;0.756)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007 **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116(36.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTV2(Actual PTV) (cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e221(69.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.621 (0.303\u0026ndash;1.303)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96(30.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTV1-PTV2 (cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19(55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101(31.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.095 (1.438\u0026ndash;6.972)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005 **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e216(68.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhole lung volume (cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21(61.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e272(85.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.267 (0.126\u0026ndash;0.583)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;2409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45(14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal lungs V\u003csub\u003e5\u003c/sub\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118(37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.331 (0.643\u0026ndash;2.713)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19(55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e199(62.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal lung V\u003csub\u003e20\u003c/sub\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e239(75.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.466 (0.226\u0026ndash;0.984)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.040 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78(24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal lung V\u003csub\u003e30\u003c/sub\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33(97.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e276(87.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.902 (1.012\u0026ndash;88.337)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41(12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal MLD (Gy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e276(87.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4247e\u0026thinsp;+\u0026thinsp;07 (0-Inf)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41(12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContralateral lung V\u003csub\u003e5\u003c/sub\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33(97.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e254(80.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.185 (1.711-146.936)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.040 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63(19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContralateral lung V\u003csub\u003e20\u003c/sub\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e154(48.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.512 (0.743\u0026ndash;3.157)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e163(51.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContralateral lung V\u003csub\u003e30\u003c/sub\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28(82.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e219(69.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.088 (0.893\u0026ndash;5.727)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98(30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContralateral MLD (Gy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e266(83.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4782e\u0026thinsp;+\u0026thinsp;07 (0-Inf)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51(16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIpsilateral lung V\u003csub\u003e5\u003c/sub\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 62.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116(36.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.949 (0.956\u0026ndash;4.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.066 .\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;62.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e201(63.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIpsilateral lung V\u003csub\u003e20\u003c/sub\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87(27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.087 (1.002\u0026ndash;4.281)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.045 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19(55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e230(72.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIpsilateral lung V\u003csub\u003e30\u003c/sub\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28(82.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e218(68.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.119 (0.906\u0026ndash;5.811)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99(31.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIpsilateral MLD (Gy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e134(42.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.738 (0.854\u0026ndash;3.631)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e183(57.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart V\u003csub\u003e50\u003c/sub\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88(27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.822 (0.866\u0026ndash;3.741)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e229(72.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMHD (Gy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21(61.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e212(66.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.800 (0.390\u0026ndash;1.699)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105(33.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHematological indicators\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-RT WBC (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(67.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e160(50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.052 (0.988\u0026ndash;4.507)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.061 .\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e157(49.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-RT NEU (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101(31.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.139 (1.043\u0026ndash;4.386)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.037 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e216(68.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-RT LYM (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e139(43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.394 (0.162\u0026ndash;0.861)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.027 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(76.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e178(56.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-RT MON (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31(91.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e248(78.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.875 (0.988\u0026ndash;12.226)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.088 .\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69(21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-RT EOS (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e152(47.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.221 (0.600-2.504)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.580\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e165(52.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-RT BASO (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88(27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.419 (0.655\u0026ndash;2.946)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.357\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22(64.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e229(72.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-RT NLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e132(41.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.002 (0.983\u0026ndash;4.185)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.058 .\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e185(58.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-RT dNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22(64.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e133(42.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.536 (1.232\u0026ndash;5.462)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.013 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e184(58.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-RT SIRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e110(34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.117 (1.038\u0026ndash;4.357)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.039 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e207(65.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-RT MLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(70.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e176(55.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.923 (0.914\u0026ndash;4.336)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.096 .\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141(44.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-RT dMLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(99.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e231(72.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.792 (1.063\u0026ndash;9.604)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.061 .\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86(27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-RT WBC (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19(55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88(27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.296 (1.609\u0026ndash;6.869)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001 **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e229(72.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-RT NEU (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50(15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.216 (1.987\u0026ndash;8.841)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19(55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e267(84.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-RT LYM (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112(35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.475 (0.185\u0026ndash;1.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.090 .\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27(79.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e205(64.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-RT MON (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21(61.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e123(38.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.548 (1.245-5.400)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e194(61.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-RT EOS (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e245(77.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.420 (0.203\u0026ndash;0.888)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.020 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72(22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-RT BASO (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5(1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.900 (0.543\u0026ndash;18.914)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(94.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e312(98.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-RT NLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(76.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e158(49.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.271 (1.498\u0026ndash;7.930)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005 **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(23.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e159(50.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-RT dNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86(27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.686 (1.306\u0026ndash;5.531)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007 **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e231(72.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-RT SIRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70(22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.970 (1.924\u0026ndash;8.269)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e247(77.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-RT MLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79(24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.378 (1.139\u0026ndash;4.893)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19(55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e238(75.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-RT dMLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22(64.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137(43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.409 (1.170\u0026ndash;5.186)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.020 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e180(56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn univariable analysis, the following variables were indicated to be significantly associated with severe RP: age\u0026thinsp;\u0026gt;\u0026thinsp;65 years, ILD, without surgery history, FEV1\u0026thinsp;\u0026lt;\u0026thinsp;87.4%, RT dose\u0026thinsp;\u0026gt;\u0026thinsp;58.3Gy, GTV\u0026thinsp;\u0026gt;\u0026thinsp;29.6 cm\u003csup\u003e3\u003c/sup\u003e, PTV1(GTV\u0026thinsp;+\u0026thinsp;1cm)\u0026thinsp;\u0026gt;\u0026thinsp;202cm\u003csup\u003e3\u003c/sup\u003e, PTV1-PTV2\u0026thinsp;\u0026gt;\u0026thinsp;15cm\u003csup\u003e3\u003c/sup\u003e, whole lung volume\u0026thinsp;\u0026gt;\u0026thinsp;2409.1cm\u003csup\u003e3\u003c/sup\u003e, total lung V\u003csub\u003e20\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;18%, contralateral lung V\u003csub\u003e5\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;11%, ipsilateral lung V\u003csub\u003e20\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;45%, pre-RT NEU\u0026thinsp;\u0026gt;\u0026thinsp;4.9\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, pre-RT LYM\u0026thinsp;\u0026gt;\u0026thinsp;1.7\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, pre-RT dNLR\u0026thinsp;\u0026gt;\u0026thinsp;1.9, pre-RT SIRI\u0026thinsp;\u0026gt;\u0026thinsp;1.6, pre-RT NMLR\u0026thinsp;\u0026gt;\u0026thinsp;2.8, post-RT NEU\u0026thinsp;\u0026gt;\u0026thinsp;5.3\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, post-RT MON\u0026thinsp;\u0026gt;\u0026thinsp;0.5\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, post-RT EOS\u0026thinsp;\u0026gt;\u0026thinsp;0.1\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L, post-RT NLR\u0026thinsp;\u0026gt;\u0026thinsp;4.5, post-dNLR\u0026thinsp;\u0026gt;\u0026thinsp;3.3, post-RT SIRI\u0026thinsp;\u0026gt;\u0026thinsp;3.4, post-RT MLR\u0026thinsp;\u0026gt;\u0026thinsp;0.8 and post-RT dMLR\u0026thinsp;\u0026gt;\u0026thinsp;0.8.\u003c/p\u003e \u003cp\u003eTo avoid overfitting, factors with P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were put into LASSO analysis to screen out the crucial predictors (supplementary Fig.\u0026nbsp;1), where ILD, contralateral lung V5 (ContraV\u003csub\u003e5\u003c/sub\u003e)\u0026thinsp;\u0026gt;\u0026thinsp;11%, ipsilateral V20 (IpsiV\u003csub\u003e20\u003c/sub\u003e)\u0026thinsp;\u0026gt;\u0026thinsp;45%, pre-RT dNLR\u0026thinsp;\u0026gt;\u0026thinsp;1.9 and post-RT SIRI\u0026thinsp;\u0026gt;\u0026thinsp;3.4 showed significant impact on the incidence of severe RP. Then multivariate logistic regression was conducted on these factors above. In multivariate analysis, ILD (OR:13.903, 95%CI:4.490-45.016, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ContraV\u003csub\u003e5\u003c/sub\u003e (OR: 15.078, 95%CI: 2.670\u0026ndash;290.757, p\u0026thinsp;=\u0026thinsp;0.013), ipsiV\u003csub\u003e20\u003c/sub\u003e (OR:2.344, 95%CI: 1.038\u0026ndash;5.301, p\u0026thinsp;=\u0026thinsp;0.039), pre-RT dNLR (OR:2.823, 95%CI: 1.250\u0026ndash;6.795, p\u0026thinsp;=\u0026thinsp;0.015) and post-RT SIRI (OR: 3.756, 95%CI: 1.688\u0026ndash;8.481, p\u0026thinsp;=\u0026thinsp;0.001) were independent prognosticators of severe RP (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These factors were then utilized in the nomogram building.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate and ROC analysis of the clinical factor, DVH parameters, hematological Indicators and nomogram model in predicting grade\u0026thinsp;\u0026ge;\u0026thinsp;3 RP\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eROC curve\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eILD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.903 (4.490-45.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.613 (0.537\u0026ndash;0.689)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDVH parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContraV\u003csub\u003e5\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.925 (1.728-164.761)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.585 (0.548\u0026ndash;0.621)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIpsiV\u003csub\u003e20\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.344 (1.038\u0026ndash;5.301)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.583 (0.495\u0026ndash;0.672)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePBL markers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-RT dNLR\u0026thinsp;\u0026gt;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.823 (1.250\u0026ndash;6.795)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.614 (0.528-0.700)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-RT SIRI\u0026thinsp;\u0026gt;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.756 (1.688\u0026ndash;8.481)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.654 (0.566\u0026ndash;0.743)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNomogram*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.782 (0.701\u0026ndash;0.864)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment and validation of the nomogram\u003c/h2\u003e \u003cp\u003eBased on the multivariate logistic regression coefficients, a predictive model was visually presented as a nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The ROC curves of ILD, contra V\u003csub\u003e5\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;11%, ipsiV\u003csub\u003e20\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;45%, pre-RT dNLR\u0026thinsp;\u0026gt;\u0026thinsp;1.9, post-RT SIRI\u0026thinsp;\u0026gt;\u0026thinsp;3.4, and the nomogram are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA. The predictive model showed an excellent AUC of 0.782 (95%CI: 0.701\u0026ndash;0.864) by ROC, which was much higher than each parameter alone (ILD: 0.613, 95%CI: 0.537\u0026ndash;0.689; ContraV5\u0026thinsp;\u0026gt;\u0026thinsp;11%: 0.585, 95%CI: 0.548\u0026ndash;0.621; IpsiV20:0.583, 95%CI: 0.495\u0026ndash;0.672; Pre-RT dNLR\u0026thinsp;\u0026gt;\u0026thinsp;1.9: 0.614, 95%CI: 0.528-0.700; Post-RT SIRI\u0026thinsp;\u0026gt;\u0026thinsp;3.4: 0.654, 95%CI: 0.566\u0026ndash;0.743 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Also, a calibration curve showed favorable consistency between the predicted severe RP and the actual observation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), and DCA showed satisfactory positive net benefits of the nomogram among most of the threshold probabilities, indicating favorable potential clinical effect of the model (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSevere RP and survival\u003c/h2\u003e \u003cp\u003eThe influence of RP on survival outcomes was further investigated in these patients. The median follow-up duration was 19.8 months (range, 1.4 to 52.9 months). At the time of data cut-off, 46 patients were lost to follow-up, and 62 patients (20.3%) had died. All patients (n\u0026thinsp;=\u0026thinsp;305) were divided into the non-RP group (n\u0026thinsp;=\u0026thinsp;95) and RP group (n\u0026thinsp;=\u0026thinsp;210). The RP group was further subdivided into severe RP group (n\u0026thinsp;=\u0026thinsp;32) and mild RP group (n\u0026thinsp;=\u0026thinsp;178). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, the median OS was not reached for the RP group and the non-RP group, and there was no significant difference in OS between these two groups (p\u0026thinsp;=\u0026thinsp;0.334). And the median OS was 30.8 months for severe RP group, while was not reached for mild RP subgroup. Compared with patients with mild RP, patients with severe RP performed a worse OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) (p\u0026thinsp;=\u0026thinsp;0.027).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAt present, severe RP is one of the most important clinically relevant toxicity of thoracic radiation for patients with locally-advanced NSCLC, which not only affect the following treatment of patients, but also severely influenced the life quality and long-term survival. Thus, reducing the occurrence of severe RP is a critical goal for clinicians. While nowadays, there were still no effective method to predict the incidence of severe RP. In the present study, we collected data from 351 patients with locally-advanced NSCLC patients and after a long-term follow-up of up to 52.9 months, our data indicated that subclinical ILD, contralateral V5\u0026thinsp;\u0026gt;\u0026thinsp;11%, ipsilateralV20\u0026thinsp;\u0026gt;\u0026thinsp;45%, pre-RT dNLR\u0026thinsp;\u0026gt;\u0026thinsp;1.9 and post-RT SIRI\u0026thinsp;\u0026gt;\u0026thinsp;3.4 were the independent prognosticators of severe RP among patients with locally-advanced NSCLC receiving thoracic RT. The internal validation of the constructed nomogram demonstrated its superiority compared with any single hematological, dosimetric or clinical factor alone. These findings can be used to accurately identify patients with high risk for severe RP, and indicated that individualized and precise radiotherapy regimens can significantly reduce the occurrence of severe RP and can improve the prognosis of RP and the quality of life of patients.\u003c/p\u003e \u003cp\u003eCompared to other similar researches\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e, it must be pointed out that to the best of our knowledge, our study is the first to systematically integrate clinical factors, peripheral blood biomarkers and dose-volume parameters to develop a predictive model for the occurrence of severe RP in such a large sample of patients with locally-advanced NSCLC treated with thoracic radiotherapy.\u003c/p\u003e \u003cp\u003eOur study reported that 9.7% patients developed severe RP, which was in accordance with those reported in previous studies (about 5-11.7%) \u003csup\u003e[\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. And in our study, severe RP conferred a worse overall survival comparing with mild RP (30.8m vs. NR, p\u0026thinsp;=\u0026thinsp;0.027), similar conclusions were reached by other recent studies\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e, while there was no significant difference between RP and non-RP patients (20.4m vs. 17.6m, p\u0026thinsp;=\u0026thinsp;0.330). It underlined that early detection and timely intervention of severe RP is of great importance, which could help prolong the survival of patients.\u003c/p\u003e \u003cp\u003eWe confirmed that patients with ILD intended to have higher risk of severe RP, it was in line with our preliminary studies \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e, and similar results were also in reported by other researches\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Due to similar mechanisms, lung cancer (LC) patients have a high incidence of ILD, and ILD patients are also prone to develop lung cancer\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Thoracic radiotherapy can be a choice for LC-ILD patients, while radiotherapy is contra-indicated in severe ILD, producing RP rates of up to 43%\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. So clinical radiotherapy decisions must be cautious about the risk of severe RP in patients with ILD, and more conservative limits of lung dose should be used with a diagnosis or radiologic evidence of ILD, and specific measures should be taken in the early period of RP.\u003c/p\u003e \u003cp\u003eFor patients with locally advanced NSCLC treated with definitive chemo-radiotherapy, the NCCN guideline recommended lung dose\u0026ndash;volume constraints for conventionally fractionated RT: total lung V\u003csub\u003e20\u003c/sub\u003e\u0026thinsp;\u0026le;\u0026thinsp;35\u0026ndash;40% and MLD\u0026thinsp;\u0026le;\u0026thinsp;20 Gy\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, while there were there is still no principle or recommendation for evidence-based medicine for the dose limits of contralateral or ipsilateral lung dose. In present study, contralateral V5(\u0026gt;\u0026thinsp;11%) and ipsilateral V20(\u0026gt;\u0026thinsp;45%) showed significant associations with severe RP incidence. Bongers\u0026rsquo;s research have shown that contraV5 were predictor for grade\u0026thinsp;\u0026ge;\u0026thinsp;3 RP\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e, while the optimal cutoff point has not been confirmed. And Zhao\u0026rsquo;s research also indicated that contralateral V5 were related to the clinical outcome of patients with severe RP (p\u0026thinsp;=\u0026thinsp;0.057)\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. One study by Ramella showed that ipsiV20 could effectively dividing patients into high-risk and low-risk groups for RP with the threshold value as 52%\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. And Agrawal\u0026rsquo;s study found that ipsiV20 were significantly correlating with RP on univariate analysis, and mean ipsiV20 were 60% for patients with RP\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. The threshold of ipsiV20 in our study is lower than in mentioned studies, it may because with the development of multidisciplinary therapy, systemic administration may increase the occurrence of RP, resulting in a decrease in the threshold of ipsilateral lung dose.\u003c/p\u003e \u003cp\u003eA growing body of evidence indicates that blood-based biomarkers can be used to predict radiation-related toxicity \u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. Based on our analysis in present study, we found pre-RT dNLR greater than 1.9 and post-RT SIRI greater than 3.4 were the most significant risk hematological biomarker for the development of severe RP. For dNLR, several researches have shown its relation to worse OS and other treatment toxicities, for example, Hsiang found that elevated pre-RT dNLR is associated with worse OS and development of liver toxicity for patients with hepatocellular carcinoma after stereotactic body radiotherapy (SBRT), and dNLR\u0026thinsp;\u0026ge;\u0026thinsp;1.9 was an optimal cut-off value for determining liver toxicity risk\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e, which was exactly consistent with our findings. Cox\u0026rsquo;s research showed that an elevated pre-treatment dNLR was an independent prognostic biomarker for OS and PFS in oesophageal cancer patients treated with definitive CRT with the cutoff of 2\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e, which was close to our data. Although the potential mechanisms are unclear, it might be due to that RP is a kind of immune-mediated hypersensitive pneumonia actually\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e, and T lymphocyte subsets play a dominant role in the cellular immune response and may be involved in RP\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. What\u0026rsquo;s more, patients with lower lymphocyte count have been shown to have negative impact on the immune system, leading to the development of infections\u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e. For SIRI, our previous study has demonstrated that pretreatment SIRI are independent predictors of OS in stage Ⅲ NSCLC\u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e. As previously reported, decreased lymphocyte count after RT may be a clinical indicator in the occurrence of RP\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, and even the severity of RP was associated with the degree of lymphocyte decrease\u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e, and the potential mechanism of lymphopenia after RT might be the effect of local irradiation of circulating lymphocyte in the blood pool\u003csup\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e. And in the early phase of RP, after alveolar and interstitial edema is formed, then inflammatory cells outside like monocyte-derived pulmonary macrophages and neutrophils are recruited and accumulated here to effect action\u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/sup\u003e. Thus, the relationship between post-RT SIRI and RP are possibly related to different changes in immune cells, including the decrease of lymphocytes and the increase of neutrophils and monocytes after RT.\u003c/p\u003e \u003cp\u003eSeveral limitations should be mentioned here. Firstly, due to its retrospective nature, selective bias existed in the present study, thus more prospective studies are necessary in the future. Secondly, although the internal validation showed a relatively excellent AUC (0.782) of the present predictive model, the result would be more convincing if it had been verified by an external validation, and the model needs to be further modified and verified in future studies before applied to the clinic. What\u0026rsquo;s more, there are several biomarkers including the vitronectin (VTN) and the single nucleotide polymorphisms (SNPs) have also been investigated and considered as predictors of RP recently\u003csup\u003e[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e, hence from the perspective of precision medicine, a more comprehensive prediction model for severe RP combining the individual genomic information, dosimetric, hematological and clinical parameters should be developed in the future.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, early detection and early intervention of severe RP is of great importance due to the impact on long-term outcome. In the era of multidisciplinary comprehensive treatment for locally advanced NSCLC, lung dose constrains should be more rigorous, which would exactly affect the incidence and severity of RP. Moreover, biomarkers in peripheral blood could predict severe RP in some extent, and could be used to accurately identify high-risk patients. The multivariate analysis identified the ILD, contraV\u003csub\u003e5\u003c/sub\u003e (\u0026gt;\u0026thinsp;11%), ipsiV\u003csub\u003e20\u003c/sub\u003e (\u0026gt;\u0026thinsp;45%), pre-RT (\u0026gt;\u0026thinsp;1.9) and post-RT SIRI (\u0026gt;\u0026thinsp;3.4) as independent risk factors for severe RP in patients with locally advanced NSCLC receiving thoracic RT. A predictive nomogram was built and its efficiency confirmed by internal validation. Although needing further verification, our work still has a value for identifying patients at high risk of severe RP and evaluation of treatment plans.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNSCLC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon-small cell lung cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eradiation therapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCCRT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econcurrent chemoradiotherapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eradiation pneumonitis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRILI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eradiation-induced lung injury\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eoverall survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCTCAE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecommon Terminology Criteria for Adverse Events\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCCN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enational comprehensive cancer network\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eECOG-PS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eeastern cooperative oncology group performance status\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003echronic obstructive pulmonary disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eILD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterstitial lung disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFEV1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eforced expiratory volume in 1 second\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDLCO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ediffusing capacity of the lung for carbon monoxide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDVH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edose-volume histogram\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGTV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003egross tumor volume\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCTV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eclinical tumor volume\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePTV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eplanning tumor volume\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emean lung dose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMHD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emean heart dose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVx\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epercentage of lung/heart volume receiving\u0026thinsp;\u0026ge;\u0026thinsp;x Gy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePBL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eperipheral blood leukocyte\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eneutrophil-to-lymphocyte\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003edNLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ederived neutrophil-to-lymphocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emonocyte-to-lymphocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003edMLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ederived monocyte-to-lymphocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSIRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esystemic inflammation response index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ewhite blood cell\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNEU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eneutrophil\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLYM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elymphocyte\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMON\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emonocyte\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eeosinophil\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e3D-CRT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethree-dimensional conformal radiotherapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIMRT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eintensity-modulated radiotherapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVMAT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003evolumetric arc therapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e4D-CT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efour-dimensional computed tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePET/CT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epositron emission tomography/computed tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDG-PET\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efluorodeoxyglucose positron emission tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLASSO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eleast absolute shrinkage and selection operator\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earea under the ROC curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edecision curve analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval declaration:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethic Committee of Shanghai Pulmonary Hospital\u0026nbsp;(No. L22-357).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the 2023 Development Fund of Discipline-Department of Radiotherapy and the 2021 Shanghai Municipal Science and Technology Commission Technical Standards Project (No. 21DZ2201900).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo actual or potential conflicts of interest exist.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Giaquinto AN, Jemal A. \u003cstrong\u003eCancer statistics, 2024\u003c/strong\u003e. \u003cem\u003eCA Cancer J Clin \u003c/em\u003e2024; 74(1):12-49.\u003c/li\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. \u003cstrong\u003eGlobal Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries\u003c/strong\u003e. \u003cem\u003eCA Cancer J Clin \u003c/em\u003e2021; 71(3):209-249.\u003c/li\u003e\n\u003cli\u003eZhang Y, Vaccarella S, Morgan E, Li M, Etxeberria J, Chokunonga E, et al. \u003cstrong\u003eGlobal variations in lung cancer incidence by histological subtype in 2020: a population-based study\u003c/strong\u003e. \u003cem\u003eLancet Oncol \u003c/em\u003e2023; 24(11):1206-1218.\u003c/li\u003e\n\u003cli\u003eAuperin A, Le Pechoux C, Rolland E, Curran WJ, Furuse K, Fournel P, et al. 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15(10):1624-1635.\u003c/li\u003e\n\u003cli\u003eKainthola A, Haritwal T, Tiwari M, Gupta N, Parvez S, Tiwari M, et al. \u003cstrong\u003eImmunological Aspect of Radiation-Induced Pneumonitis, Current Treatment Strategies, and Future Prospects\u003c/strong\u003e. \u003cem\u003eFront Immunol \u003c/em\u003e2017; 8:506.\u003c/li\u003e\n\u003cli\u003eLiang B, Lu X, Liu L, Dai J, Wang L, Bi N. \u003cstrong\u003eSynergizing the interaction of single nucleotide polymorphisms with dosiomics features to build a dual-omics model for the prediction of radiation pneumonitis\u003c/strong\u003e. \u003cem\u003eRadiother Oncol \u003c/em\u003e2024; 196:110261.\u003c/li\u003e\n\u003cli\u003eYu JH, Zhao QY, Liu Y, Zhu XR, Yang ZR, Fu XL, et al. \u003cstrong\u003eThe Plasma Levels and Polymorphisms of Vitronectin Predict Radiation Pneumonitis in Patients With Lung Cancer Receiving Thoracic Radiation Therapy\u003c/strong\u003e. \u003cem\u003eInt J Radiat Oncol Biol Phys \u003c/em\u003e2021; 110(3):757-765.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Non-small cell lung cancer (NSCLC), Radiotherapy, Radiation pneumonia, Dosimetric parameter, Peripheral blood biomarkers.","lastPublishedDoi":"10.21203/rs.3.rs-4967531/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4967531/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSevere radiation pneumonitis (grade≥3 RP) remains an important dose-limiting toxicity after thoracic radiotherapy (RT). This study aimed to investigate risk factors for severe RP in patients with locally-advanced non-small cell lung cancer (NSCLC) after thoracic RT, develop a prediction model to identify high-risk groups and investigate impact of severe RP on overall survival (OS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe retrospectively collected clinical, hematological and dosimetric factors from 351 \u0026nbsp;stage-Ⅲ NSCLC patients after thoracic RT between 2018 and 2022. The primary endpoint was development of severe RP. The secondary endpoint was OS. Logistic regression and least absolute shrinkage and selection operator (LASSO) regression analysis were used to identify risk factors of severe RP. \u0026nbsp;Nomogram was generated based on multivariate regression coefficients. Area under the ROC curve (AUC), calibration curve, and decision curve analysis (DCA) were conducted to validate the model. After a long-term follow-up, OS of patients with RP vs. non-RP and mild RP vs. severe RP groups was analyzed by Kaplan‒Meier method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eILD (p\u0026lt;0.001), \u0026nbsp;percentage of contralateral lung volume receiving≥5Gy (contraV\u003csub\u003e5\u003c/sub\u003e) (P=0.013), percentage of ipsilateral lung volume receiving≥20Gy (ipsiV\u003csub\u003e20\u003c/sub\u003e)(P=0.039), pre-RT derived neutrophil lymphocyte ratio (dNLR) (P=0.015) and post-RT systemic inflammation response index (SIRI) (p=0.001) were showed to be independent predictors of severe RP and were included in the nomogram. ROC curves revealed the AUC of the nomogram was 0.782. Calibration curves showed favorable consistency, and DCA showed satisfactory positive net benefits of the model. Median follow-up time was 19.8 months (1.4-52.9 months), and cases who developed severe RP showed shorter OS than those developed mild RP (P=0.027).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe identified that ILD, contraV\u003csub\u003e5\u003c/sub\u003e(\u0026gt;11%), ipsiV\u003csub\u003e20\u003c/sub\u003e(\u0026gt;45%), pre-RT dNLR (\u0026gt;1.9) and post-RT SIRI (\u0026gt;3.4) could predict severe RP among patients with locally-advanced NSCLC receiving thoracic RT. Combining these indicators, a nomogram was first built and validated, showing its potential value in clinical practice.\u003c/p\u003e","manuscriptTitle":"Predicting severe radiation pneumonitis in patients with locally- advanced non-small cell lung cancer after thoracic radiotherapy: Development and internal validation of a nomogram based on the clinical, hematological and dose–volume histogram parameters","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-17 17:37:31","doi":"10.21203/rs.3.rs-4967531/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"17855a43-779b-48d2-b9f4-0722dae5d8a8","owner":[],"postedDate":"October 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-17T17:37:33+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-17 17:37:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4967531","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4967531","identity":"rs-4967531","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}