The risk of symptomatic radiation pneumonitis in small cell lung cancer patients following sequential immune-chemotherapy and radiotherapy: a multicenter retrospective cohort study

The risk of symptomatic radiation pneumonitis in small cell lung cancer patients following sequential immune-chemotherapy and radiotherapy: a multicenter retrospective cohort study | 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 The risk of symptomatic radiation pneumonitis in small cell lung cancer patients following sequential immune-chemotherapy and radiotherapy: a multicenter retrospective cohort study Yuanyuan Liu, Jinghao Zhang, Miao Zhang, Wenbin Wu, Hui Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7114664/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Dec, 2025 Read the published version in Radiation Oncology → Version 1 posted 8 You are reading this latest preprint version Abstract Background: Immune checkpoint inhibitors (ICIs) plus thoracic radiotherapy may magnify the radiation pneumonitis (RP) risk. Data on the risk for symptomatic RP in small cell lung cancer (SCLC) patients following radiotherapy after induction immune-chemotherapy are limited. Patients and methods : This multicenter retrospective study included patients with SCLC from two hospitals who started thoracic intensity-modulated radiation therapy (IMRT) or volumetric modulated arc therapy (VMAT) between April 1, 2022 and March 31, 2025. The primary endpoint was grade 2 or worse (grade 2+) RP according to the Common Terminology Criteria for Adverse Events v5.0. Logistic regression analyses and receiver operating characteristic (ROC) analysis were used to assess the correlated parameters with grade 2+ RP. Results : A total of 443 patients were reviewed. The median follow-up period was 3 months after radiotherapy. In detail, 87 [19.6%], 35 [7.9%], and 6 [1.4%] developed grade 2, grade 3, and grade 4 RP in this cohort, respectively. No patients recorded grade 0 or 5 RP. On multivariable analysis, male, concurrent chemoradiotherapy (CCRT), VO2max and FEV1 significantly predicted the incidence of grade 2+ RP, with odd ratios (OR) and 95% confidence interval (CI) of 2.408 (1.406-4.125), 2.249 (1.193-4.240), 0.897 (0.828-0.972) and 0.369 (0.161-0.846), respectively(all P 0.05, respectively). Furthermore, the area under the ROC curve established using these parameters was 0.766 (95% CI: 0.718-0.815) for predicting the occurrence of grade 2+ RP. Conclusions : Prior immune-chemotherapy (ranging from 0 to 4 cycles) is not correlated with the onset of grade 2+ RP. The incidence of grade 2+ RP was relatively high in this multicenter study, and its risk increased remarkably at decreased VO 2 max and FEV 1 . Male and CCRT were independent risk factor of grade 2+ RP. Our findings can aid in RP risk prediction and radiotherapy planning. Radiation pneumonitis (RP) Intensity-modulated radiation therapy (IMRT) Volumetric modulated arc therapy (VMAT) Serplulimab Concurrent chemoradiotherapy (CCRT) Small cell lung cancer (SCLC) VO2 max Forced expiratory volume in one second (FEV1) Figures Figure 1 Figure 2 Figure 3 Introduction Understanding the potential risk factors of radiation pneumonitis (RP) is important for radiotherapy planning. Immune checkpoint inhibitors (ICIs) plus thoracic radiotherapy may magnify the risk of symptomatic radiation pneumonitis (RP). In the era of immunotherapy, the major concerns regarding radiotherapy for lung cancer include the potential aggregated incidence and degree of RP following ICIs. Data on the incidence of RP in patients with small cell lung cancer (SCLC) following sequential immune-chemotherapy and radiotherapy are limited. In addition, whether cardiopulmonary exercise testing(CPET) information plays a role in predicting the incidence of RP following thoracic radiotherapy is unclear. Potential predictive factors for RP include clinical, physical and dosimetric factors such as mean lung dose (MLD), V 5/20 (percentage of the lung volume receiving 5, 20 Gy). Pulmonary function variables, tumor location, smoking history, emphysema, interstitial lung disease, and concurrent chemotherapy might also be correlated with the incidence of RP. Therefore, the V5, V10, MLD were strictly restrained in the present cohort with the aim to diminish the incidence and severity of RP. For patients with locally advanced NSCLC treated with definitive chemo-radiotherapy, the NCCN recommended lung dose–volume constraints for conventionally fractionated radiation therapy as follows: V 20 ≤35%, V 5 ≤ 65%, and MLD ≤20 Gy [1]. We performed a multicenter cohort study. The initial aim was to investigate the potential risk of prior immune-chemotherapy on the incidence of grade 2+ RP. The second aim was to validate the performance of CPET to accurately predict the risk of symptomatic (grade 2+) RP, which might be more reliable than normal lung function test. We collected data of the clinical factors, dosimetric parameters, and the occurrence of grade 2+ RP in SCLC patients, attempting to investigate the necessity of CBET before RT. Materials and methods Patient Eligibility A retrospective study was conducted on the patients pathologically diagnosed with SCLC treated with thoracic radiotherapy at the Department of Radiotherapy, Xuzhou Central Hospital and Xuzhou First People's Hospital between April 1, 2022, and March 31, 2025. The patients with Eastern Cooperative Oncology Group (ECOG) performance status of 0 to 2, receiving thoracic radiotherapy with no cause of death other than RP within 3 months after radiotherapy, were included. Exclusion criteria were as follows: acute immunotherapy-associated pneumonitis, patients who did not finish the entire radiotherapy due to reasons other than radiation-related complications, missing data, and radiotherapy interruption for more than one week. Ethical approval (XZXY-LK-20230427-070) was obtained from the Ethical Review Committee of Xuzhou Central Hospital, Medical School of Southeast University. Informed consent of each patient was not required due to the retrospective nature of this study. The data were presented anonymously for privacy concern. The variables including patients’ age, gender, pulmonary comorbidities, diabetes mellitus (DM), percutaneous coronary intervention (PCI) for coronary heart disease (CHD), TNM staging, immune-chemotherapy cycles, total radiotherapy dosage, and dose per fraction were collected. Physical examination, chest and abdominal computed tomography (CT), brain magnetic resonance imaging (MRI), bone emission computed tomography (ECT) and fluorodeoxyglucose positron emission tomography-CT were performed to obtain detailed data for staging, which was determined using the 8th edition of the tumor, node, and metastasis classification of lung cancer[ 2 ]. Immune-chemotherapy before radiotherapy The first-choice chemotherapy regimen included intravenous etoposide (100 mg/m 2 of body surface area) of on days 1–5. Patients received either of cisplatin (75 mg/m 2 of body surface area) and etoposide (chemotherapy only), or intravenous serplulimab (4.5 mg/kg of body weight) plus cisplatin and etoposide (immune-chemotherapy) every 3 weeks for up to 12 weeks. Thereafter, they were treated with concurrent or sequential radiotherapy after 1–4 courses of induction immune-chemotherapy, respectively. The concurrent chemoradiotherapy (CCRT) regimens included cisplatin plus etoposide. The interval between induction chemotherapy/ immune-chemotherapy and radiotherapy was about 2 weeks. Radiotherapy All patients underwent a planning CT scan when immobilized in a supine position as reported [ 3 ]. Treatment technique included fixed beam coplanar intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) techniques [ 4 , 5 ]. Anisotropic analytical algorithm (version 10.0) was used for dose calculation. Vx was defined as the percentage of total lung volume receiving equal to or greater than the× Gy radiation dosage. RT was delivered with a total dose of 20–60 Gy, at 1.5, 2 or 2.5 Gy per fraction, 5 days per week. Treatment planning was performed using an ADAC Pinnacle™ (Philips Medical Systems) system. Treatment consisted of 6 or 10 MV photon thoracic IMRT using a Siemens Artiste (Oncology Care Systems, Siemens Medical Solutions, CA, USA) digital linear accelerator. The target volumes were set by experienced radiation oncologists focused on lung cancer. If the lung dose exceeded the safety range (V20 ≤ 30%, V5 ≤ 60%, MLD ≤ 17Gy), the total dose would be appropriately reduced. Assessment of RP and follow-up When patients complained of dyspnea, chest tightness, or fatigue, a chest thin-slice CT was performed during the RT period. Otherwise, the CT would be carried out 1–2 weeks after the RT. Thereafter, patients were reevaluated every month after treatment to check the physical status and thoracic CT performed at each follow-up visit. The diagnoses of RILI including RP and radiation fibrosis are made by exclusion using clinical assessment and radiological findings. The following factors were collected: age, gender, ECOG performance status, target tumor locations, smoking status, normal pulmonary function test such as forced expiratory volume in one second (FEV1) and the diffusing capacity of the lungs for carbon monoxide (DLCO), respiratory comorbidity (history of chronic lung diseases, including chronic obstructive pulmonary diseases, chronic bronchitis, interstitial lung disease, and pulmonary tuberculosis), CPET parameters such as VO 2max , TNM staging, cycles of immune-chemotherapy before RT, and radiotherapy technique, and dosimetric data including the prescription dose, fractions, V5, V20, and MLD of the total lung. Enrolled SCLC patients receiving thoracic radiotherapy were divided into symptomatic (grade 2+) RP and asymptomatic (grade 0–1) RP groups, and independent prognostic factors were determined using univariate and multivariate logistic regression analyses. The primary endpoint was grade 2 or worse (grade 2+) RP, which was confirmed by experienced radiation oncologists and pulmonologists based on the manifestation and radiological findings on thin-slice CT images of the patients and then graded from 1 to 5 according to the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0 [ 6 ]. Grade 1: No symptoms, only clinical or imaging changes, and no treatment is required; Grade 2: Mild symptoms, limited work-related daily activities, need drug treatment; Grade 3: Severe symptoms, personal daily activities are limited, requiring oxygen inhalation; Grade 4: Life-threatening respiratory symptoms that require urgent treatment; Grade 5: Causing the death of the patient. Grade 0 is defined as no symptom or radiographic change. Statistical Analysis Binary logistic regression was performed for univariate and multivariate analysis to assess the relationship between risk factors and grade 2 + RP. The significant factors with P < 0.20 in univariate analysis were applied to multivariate logistic regression analysis by using forward stepwise (likelihood ratio) method. The chi-square test and t-test were employed to compare the baseline characteristics between the two groups. All statistical analyses were performed using IBM SPSS statistics version 25.0 (IBM Corp, New York, NY; formerly SPSS Inc., Chicago, IL). Independent risk factors (OR > 1 and P < 0.05 in multivariate logistic regression) of grade ≥ 2 RP were ultimately revealed. The OR and 95% CI of the potential risk factors revealed by multivariate regression were depicted using MedCalc software (MedCalc, Version 20.015, MedCalc Software Ltd.). The receiver operating characteristic (ROC) curve and the value of the area under the curve (AUC) were further obtained to analysis the predictive efficacy of the risk factors. All tests were two-sided. P < 0.05 was considered statistically significant. Results Patient characteristics and incidence of grade 2 + RP Finally, a total of 443 patients with SCLC were eligible and the data were further analyzed, including 161(36.3%) male and 282 (63.7%) female patients, with a median age of (57.2 ± 12.2) years (range, 26–87). A detailed flowchart of patient selection was shown in Fig. 1 . Antibiotics and intravenous methylprednisolone were used to treat grade 2 + RP which was diagnosed by complaints and thin-slice CT images. All the RP patients showed alleviation of the severity of pneumonitis. In total, 128 (28.9%) patients were diagnosed with grade 2 + RP. The median interval from the completion of radiotherapy to the appearance of grade 2 + RP was 49 days (range, 35–105 days). In detail, 87 [19.6%], 35 [7.9%], and 6 [1.4%] developed grade 2, grade 3, and grade 4 RP in this cohort, respectively. No grade 0 or grade 5 RP was recorded. Baseline characteristics of the patients with grade 1 RP (n = 315) and those with grade 2 + RP (n = 128) were summarized in Table 1 and Table 2 . Radiotherapy was discontinued urgently in one (0.2%) patient because of RP during the treatment period. Table 1 Univariate and multivariate logistic analysis of the baseline characteristics of the patients diagnosed with small cell lung cancer (n = 443) Characteristics Total (n = 443) Grad 1 RP (n = 315) Grade 2 + RP (n = 128) Univariate analysis Multivariate analysis Odds ratio (95% CI) P-value Odds ratio (95% CI) P-value Age, year, mean (SD) 57.2 (12.2) 57.5 (11.9) 56.6 (12.8) 0.994 (0.978–1.011) 0.497 0.968 (0.944–0.992) 0.010 Gender: Female/Male, N (%) 282(63.7)/ 161(36.3) 214(67.9)/ 101(32.1) 68(53.1)/ 60(46.9) 1.870 (1.228–2.846) 0.004 2.408 (1.406–4.125) 0.001 Body mass index, kg/m 2 , mean (SD) 23.9 (3.4) 24.0 (3.4) 23.6 (3.4) 0.964 (0.907–1.025) 0.238 0.971 (0.903–1.044) 0.420 Diabetes mellitus, No/ Yes, N (%) 415(93.7)/ 28(6.3) 298(94.6)/ 17(5.4) 117(91.4)/ 11(8.6) 1.648 (0.749–3.624) 0.214 2.386 (0.920–6.188) 0.074 PCI for CHD, No/ Yes, N (%) 409(92.3)/ 34(7.7) 296(94.0)/ 19(6.0) 113(88.3)/ 15(11.7) 2.068 (1.106–4.210) 0.045 2.236 (0.898–5.564) 0.084 ECOG PS before radiotherapy: 0/1/2, N (%) 153(34.5)/ 241(54.4)/ 49(11.1) 117(37.1)/ 172(54.6)/ 26(8.3) 36(28.1)/ 69(53.9)/ 23(18.0) ECOG 0 vs.1 1.304 (0.818–2.079) 0.265 0.645 (0.344–1.210) 0.172 ECOG 0 vs. 2 2.875 (1.465–5.641) 0.002 1.161 (0.449–3.002) 0.759 Distribution of the target lesion, Upper /Lower / Middle Lobe, N (%) 191(43.1)/ 219(49.4)/ 33(7.4) 135(42.9)/ 157(49.8)/ 23(7.3) 56(43.8)/ 62(48.4) /10(7.8) Upper vs. Lower lob 0.952 (0.620–1.461) 0.822 1.814 (0.709–1.979) 0.519 Upper vs. Middle lobe 1.048 (0.469–2.345) 0.909 1.027 (0.399–2.643) 0.956 Size of the target tumor, cm 3.4 ± 1.8 3.5 ± 1.7 3.4 ± 1.8 0.971 (0.863–1.092) 0.620 0.967 (0.824–1.136) 0.686 TNM staging # : 1/2/3/4, N (%) 28(6.3)/ 86(19.4)/ 292(65.9)/ 37(8.4) 18(5.7)/ 55(17.5)/ 217(68.9)/ 25(7.9) 10(7.8)/ 31(24.2)/ 75(58.6)/ 12(9.4) Stage 1 vs. 2 0.821 (0.612–1.101) 0.189 1.059 (0.352–3.183) 0.919 Stage 1 vs. 3 0.519 (0.190–1.421) 0.202 Stage 1 vs. 4 0.571 (0.152–2.140) 0.405 Previous immune-chemotherapy*, None/1 /2 /3 /4 courses, N (%) 114(25.7)/ 22(5.0)/ 132(29.8)/ 53(12.0)/ 122(27.5) 91(28.9)/ 17(5.4)/ 85(27.0)/ 37(11.7)/ 85(27.0) 23(18.0)/ 5(3.9)/ 47(36.7)/ 16(12.5)/ 37(28.9) None vs. 1 1.131 (0.986–1.298) 0.079 0.507 (0.129–1.986) 0.330 None vs. 2 1.987 (1.000-3.947) 0.050 None vs. 3 1.237 (0.510-3.000) 0.638 None vs. 4 1.163 (0.550–2.460) 0.692 Concurrent chemo-radiotherapy*, No/ Yes, N (%) 130(29.3)/ 313(70.7) 109(34.6)/ 206(65.4) 21(16.4)/ 107(83.6) 2.696 (1.599–4.545) < 0.001 2.249 (1.193–4.240) 0.012 Thoracic radiation dose (Gy), mean (SD) 48.3 (8.2) 48.7 (7.60 47.3 (9.6) 0.981 (0.957–1.005) 0.123 1.004 (0.948–1.063) 0.898 Fraction daily, 1.5/2.0/2.5 Gy, N (%) 65/239/139 45/175/95 20/64/44 1.126 (0.605–2.093) 0.709 0.735 (0.326–1.656) 0.458 V5 (%), mean (SD) 51.0 (5.0) 50.9 (4.9) 51.5 (5.3) 1.024 (0.983–1.067) 0.249 1.031 (0.983–1.181) 0.211 V20 (%), mean (SD) 25.0 (5.3) 25.3 (5.0) 24.4 (6.0) 0.972 (0.936–1.008) 0.128 0.964 (0.898–1.035) 0.310 MLD, Gy 15.8 (2.9) 15.9 (2.8) 15.6 (3.0) 0.966 (0.899–1.039) 0.357 1.009 (0.894–1.138) 0.884 Radiotherapy procedure, IMRT/ VMAT, N (%) 269(60.7)/ 174(39.9) 187(59.4)/ 128(40.6) 82(64.1)/ 46(35.9) 0.820 (0.536–1.254) 0.359 0.971 (0.592–1.593) 0.907 Data are reported as N (%) or mean ± standard deviation (SD). *Carboplatin plus Etoposide. Abbreviations: CI, confidence interval; COPD, chronic obstructive pulmonary disease; ECOG-PS, Eastern Cooperative Oncology Group performance status; MLD, mean lung dose of the lung; V20, percentage of the lung volume received at least 20 Gy; V5, percentage of the lung volume received at least 5 Gy. # According to the 8th edition of the AJCC/TNM staging system for lung cancer. Table 2 Univariate and multivariate logistic analysis of the lung function and cardiopulmonary exercise testing parameters (n = 443) Variables Total (n = 443) Grad 1 RP (n = 315) Grade 2 + RP (n = 128) Univariate analysis Multivariate analysis Odds ratio (95% CI) P-value Odds ratio (95% CI) P-value Pulmonary comorbidity COPD, No/ Yes, N (%) 384(86.7)/ 59(13.3) 274(87.0)/ 41(13.0) 110(85.9)/18(14.1) 1.094 (0.602–1.986) 0.769 1.152 (0.548–2.424) 0.709 Bronchiectasis, No/ Yes, N (%) 416(93.9)/ 27(6.1) 297(94.3)/ 18(5.7) 119(93.0)/ 9(7.0) 1.248 (0.545–2.856) 0.600 1.562 (0.552–4.422) 0.401 Asthma, No/ Yes, N (%) 417(94.1)/ 26(5.9) 298(94.6)/ 17(5.4) 119(93.0)/ 9(7.0) 1.326 (0.575–3.057) 0.508 1.311 (0.466–3.692) 0.608 Pneumoconiosis, No/ Yes, N (%) 430(97.1)/ 13(2.9) 309(98.1)/ 6(1.9) 121(94.5)/ 7(5.5) 2.979 (0.981–9.045) 0.054 1.562 (0.552–4.422) 0.401 Never/ Current or former smoker, N (%) 198(44.7)/ 245(55.3) 147(46.7)/ 168(53.3) 51(39.8)/ 77(60.2) 1.321 (0.870–2.005) 0.191 0.976 (0.457–2.083) 0.950 Pack-year of cigarette smoking 16.7 (22.9) 16.2 (22.7) 18.0 (23.3) 1.003 (0.995–1.012) 0.442 0.999 (0.984–1.015) 0.948 WR peak (W) 105.9 (32.1) 107.1 (31.8) 103.1 (33.0) 0.996 (0.990–1.003) 0.244 1.007 (0.995–1.019) 0.267 VO 2 peak (mL/min) 1145.2 (334.5) 1152.3 (328.0) 1127.6 (350.8) 1.000 (0.999-1.000 ) 0.479 0.999 (0.998–1.001) 0.247 VO 2 peak/HR (mL/beat) 9.0 (2.7) 9.1 (2.70 8.9 (2.5) 0.947 (0.901–1.503) 0.507 0.976 (0.859–1.110) 0.712 VO₂ max, mL/(min * kg) 18.8 (4.0) 19.4 (3.9) 17.4 (3.9) 0.872 (0.823–0.924) < 0.001 0.897 (0.828–0.972) 0.008 AT (mL/min) 819.2 (322.7) 825.8 (350.0) 803.0 (243.1) 1.000 (0.999-1.000) 0.502 1.000 (0.999–1.001) 0.872 ATVO 2 /kg (mL/[min * kg]) 13.5 (9.4) 13.6 (9.1) 13.6 (9.3) 1.001 (0.979–1.023) 0.936 1.003 (0.977–1.029) 0.819 LVEF 59.8 (7.8) 60.3 (7.6) 58.6 (8.2) 0.972 (0.948–0.998) 0.035 0.976 (0.945–1.009) 0.150 FEV1, L 1.2 (0.4) 1.3 (0.5) 1.1 (0.4) 0.284 (0.154–0.522) < 0.001 0.369 (0.161–0.846) 0.019 FEV1/FVC (%) 82.6 (12.2) 82.8 (11.1) 81.9 (13.5) 0.995 (0.978–1.011) 0.520 1.005 (0.984–1.027) 0.650 DLCO (%) 76.3 (9.6) 76.4 (9.2) 76.1 (10.7) 0.997 (0.976–1.018) 0.782 1.021 (0.992–1.051) 0.149 Data are reported as N (%) or mean ± standard deviation. Abbreviations: CI, confidence interval; FEV 1 , forced expiratory volume in 1 s; FVC, forced vital capacity; DLCO, diffusing capacity of the lungs for carbon monoxide. Univariate and multivariate logistic regression of the parameters The results of the univariate analysis were reported in Tables 1 and 2 . Potential factors predicting grade 2 + RP were identified as follows: male, PCI for CHD, ECOG score, VO 2 max , FEV1, and CCRT (P < 0.05, respectively). Multivariate analysis indicated that age, body mass index, smoking status and pack-year of smoking consumption, diabetes, pulmonary comorbidities including COPD, bronchiectasis, asthma and pneumoconiosis, target tumor location, prior chemotherapy or immune-chemotherapy, dosimetric factors such as lung V5, V20 and MLD, radiotherapy technique (IMRT vs. VMAT) were not independent predictors of grade 2 + RP (all P < 0.05). However, on multivariable analysis, male, VO 2 max, FEV 1 and CCRT significantly predicted the incidence of grade 2 + RP (P < 0.05, respectively). In detail, male and CCRT were positively correlated with the occurrence of grade 2 + RP; whereas VO 2 max and FEV 1 were negatively associated with the onset of grade 2 + RP. The odd ratios (OR) and 95% confidence interval (CI) calculated by multivariate logistic regression for male, CCRT, VO 2 max and FEV 1 were 2.408 (1.406–4.125), 2.249 (1.193–4.240), 0.897 (0.828–0.972) and 0.369 (0.161–0.846), respectively, as shown in Fig. 2 . Furthermore, the receiver operating characteristic (ROC) curve and the value of the area under the curve (AUC) were obtained to analysis the predictive efficacy of these correlated factors. The AUC of the ROC curve established using selected parameters including age, gender, diabetes, PCI, cycles of previous immune-chemotherapy before radiotherapy, concurrent chemo-radiotherapy, IMRT vs. VMAT, VO₂ max, FEV 1 , and DLCO (%) was 0.766 (95% CI: 0.718–0.815) for predicting the occurrence of grade 2 + RP, as shown in Fig. 3 . Discussion Radiation-induced lung injury (RILI) is one of the main dose-limiting toxicities in radiation therapy (RT) for lung cancer. Approximately 10–20% of patients show signs of RILI [ 7 ]. Technological advancements as well as patient selection based on the potential risk factors in RT have helped to reduce RILI. Predicting patients at risk for RILI need to be further improved. RP is diagnosed based on the CT imaging findings of parenchymal changes, typical manifestations after exclusion of acute infection or embolism, heart failure, drug-induced pneumonitis, and pseudo-progression of the tumor [ 8 ]. To minimize the risk of grade 2 + RP when delivering 4 Gy per fraction as hypofractionated RT at either 60 Gy or 72 Gy, it is advisable to maintain lung V5 < 41.3%, V20 < 17.7% and MLD < 10.6 Gy, which can also be considered as lower-priority constraints [ 9 ]. Generally, RP develops at 4 weeks following conventionally fractionated therapy [ 6 ]. Signs of pulmonary infection include a unilateral or bilateral lung opacity appearing prior to completion of radiation, tree-in-bud opacities, and cavitation. RT-related necrosis and local recurrence can also manifest as cavitation, but it generally occur at a later interval following completion of radiotherapy. RP was diagnosed when lung opacities were located within the radiation portal. Moreover, indications for thoracic RT are expanding and the incidence of serious pulmonary complications has decreased following the advances in radiation delivery techniques. More sophisticated techniques of conformal RT technologies such as IMRT, VMAT, stereotactic body radiation therapy (SBRT) or stereotactic ablative radiotherapy (SABR), and stereotactic radio-surgery (SRS) may be associated with a lower incidence of RILI compared with standard, three-dimensional conformal RT [ 6 ]. It is reported that risk factors for RILI including V20 ≥ 30%, V5 ≥ 65%, MLD > 20 Gy, and target tumor located in lower lobe of the patients [ 6 ]. In this study, grade 2 + RP occurred in 28.9% of these SCLC patients, whereas grade 4 RP was uncommon (1.4%). After prior immune-chemotherapy, the median time to onset of grade 2 + RP was mainly within 3 months since initiation of radiotherapy (approximately 90%). Factors associated with higher risk of grade 2 + RP were identified. As shown in this study, normal lung function tests as well as CPET could be considered as a potential indicator for RP for patient selection and radiotherapy planning. The CPET have constituted a significant step in evaluating lung function during radiotherapy and useful predictive tools to avoid severe radiation-associated complications or toxicity. These results warrant further study and validation in large populations before the recommendation or consensus of CPET in clinical practice or trial planning. Nevertheless, clinical benefit with immune-chemotherapy was maintained in patients who experienced grade 2 + RP, and most achieved resolution of this event, suggesting the risk of grade 2 + RP should not deter use of immune-chemotherapy regimen in eligible patients with SCLC. Treatment for RP is needed only for symptomatic patients. Mild symptoms can be treated with inhaled steroids, whereas subacute to moderate RP with impaired lung function require oral corticosteroids. Patients who do not tolerate or are refractory to steroids can be considered using immunosuppressive agents. Improvements in radiation technique, early diagnosis and appropriate treatment will lead to lower rates of RP and an overall good prognosis. CCRT is the first-line treatment for patients with limited-stage SCLC. The pooled incidence of CCRT-induced grade 3–5 RP in unresectable NSCLC patients was estimated to be 3.62%-7.85% using platinum-based doublet chemotherapy, with incidence varying in different studies [ 10 ]. Another study reported that the pooled incidence of grade ≥ 3 pneumonitis was 3.28%-6.34%, while the incidence of grade 5 (fatal) pneumonitis was 0.29%-0.88% [ 11 ]. A systematic review showed that the overall rate of symptomatic RP for patients with lung cancer undergoing CCRT was 29.8%, with fatal pneumonitis in 1.9%. Factors predictive of grade 2 + RP were V20 and carboplatin plus paclitaxel chemotherapy. Predictors of fatal pneumonitis were daily dose > 2 Gy, V20, and lower-lobe tumor location [ 12 ]. Thoracic radiotherapy decisions in patients with interstitial lung disease (ILD) are complex due to concerns about severe or even fatal RP. Another review showed that the median overall incidence of grade 3 + RP after thoracic radiotherapy for treatment of lung cancer was 19.7% (range 8–46%); in addition, the RP incidence was greater in patients undergoing conventional radical radiotherapy (median 31.8%) compared with particle beam therapy- or stereotactic ablative radiotherapy (median 12.5%) [ 13 ]. Meanwhile, the median rate of grade 5 RP was 11.9% (range 0–60%). The presence of ILD was an independent predictor of severe RP, whereas V5, V10, V20 and MLD were the most common dosimetric predictors for severe RP, which need to be strictly constrained [ 13 ]. Furthermore, patients with lung cancer associated with ILD have a poor prognosis, which may be ascribed to the high risk of severe and even fatal RP. Careful patient selection and strict radiation dosage constraints should be utilized regularly. In addition, DM is an important risk factor for RP in chest tumor patients undergoing RT [ 14 ]. Another study reported that, besides the known dosimetric factors, DM was the most important risk factor of RP incidence after concomitant chemoradiotherapy, and the risk was tripled compared to patients without DM [ 15 ]. The use of modern radiation techniques, such as IMRT, is crucial to meet restrictive radiotherapy dosage constraints to lower the incidence of RP. Chemoradiotherapy plus ICIs is the standard of care for patients with SCLC. A few studies revealed that the addition of ICIs to CRT was associated with an increased risk of pneumonitis. A retrospective cohort assembled using the Surveillance, Epidemiology, and End Results-Medicare database showed that radiation therapy (RT) and ICIs used for NSCLC patient were, at most, additive rather than synergistic in causing pneumonitis [ 16 ]. ICIs combined with radiotherapy for solid tumors can produce respiratory adverse effects, including cough, pneumonia, and upper respiratory tract infections. A meta-analysis showed that the addition of neoadjuvant and adjuvant ICIs was not significantly associated with increased treatment-related deaths, but it increased the incidence of grade 3–4 ICIs-related adverse events and treatment discontinuation [ 17 ]. The pooled rate of grade 2 + pneumonitis for chemoradiotherapy plus ICIs for the treatment of locally advanced NSCLC was significantly higher than that for chemoradiotherapy alone but not that of grade 3 + or grade 5. In addition, compared with chemoradiotherapy alone, durvalumab consolidation after chemoradiotherapy appears to be associated with a higher incidence of moderate pneumonitis and chemoradiotherapy plus PD-1 inhibitors with an increased risk of severe pneumonitis [ 18 ]. Another meta-analysis revealed that adding ICIs to the conventional treatment for solid tumors significantly increased pneumonitis regardless of the mechanisms of ICIs and cancer type [ 19 ]. Furthermore, an updated meta-analysis assess the risk of respiratory adverse effects in patients with solid tumors treated with immune checkpoint inhibitors (PD-1, PD-L1 and CTLA-4 inhibitors) plus radiotherapy [ 20 ]. The combination of ICIs with radiotherapy may further increase the risk of ICIs-pneumonitis or RP. Early detection and management of pneumonitis in patients receiving RT and/or ICIs are crucial for improving outcomes. It is reported that identifying high-risk patients through interdisciplinary predictive models, radiomics, and biomarkers may help tailor treatment strategies and minimize pneumonitis [ 21 ]. A review of trials regarding combined CCRT plus concomitant immunotherapy followed by immunotherapy maintenance in patients with stage III NSCLC showed that for single-agent immunotherapy with CCRT, pneumonitis occurred in 33% of patients with 7% having grade 3–5. Double concomitant immunotherapy (PD-1 and CTLA4) plus CCRT was associated with excessive pneumonitis-related mortality of 16% [ 22 ]. Addition of single-agent immunotherapy to CCRT is manageable in selected patients. A network meta-analysis showed no significant difference between the incidence of pneumonitis in concurrent ICIs with radiotherapy and radiotherapy followed by ICIs. The radiotherapy following ICIs (ICIs-first arm) exhibited higher incidence of any grade pneumonitis compared with concurrent- and radiotherapy-first arms [ 23 ]. A systematic review investigated the use of pulmonary function tests (PFT) and other parameters to predict and mitigate RP, thereby improving RT planning. Patients with RP and COPD generally exhibited poorer overall survival. Notably, FEV1 and DLCO declined 24 months post-radiotherapy, while forced vital capacity (FVC) stayed stable. In the majority of studies, age over 60, tumors located in the lower part of the lung, and low FEV1 before RT were associated with a higher risk of RP. Dosimetric factors (V5, V20, and MLD) and metabolic imaging emerged as significant predictors of RP risk [ 24 ]. A clinical checklist blending patient and tumor characteristics, PFT results, and dosimetric criteria was proposed for assessing RP risk. This approach should guide individualized management to enhance RP prevention strategies. Preexisting interstitial lung disease and thoracic radiation are major risk factors for ICIs-related pneumonitis [ 25 ], although the mechanisms of injury are still not fully understood. Thoracic radiation increases the risk for ICIs-pneumonitis and may synergize with preexisting interstitial lung diseases (ILD) to worsen toxicity. Preexisting ILD and thoracic radiation may increase the risk for the future development of ICIs-pneumonitis. ICIs-related pneumonitis and RP are challenging to diagnose. Treatment naive patients had higher incidence of grade 1–4 pneumonitis compared with previously treated patients [ 26 ]. Patients with NSCLC may develop pneumonitis after thoracic radiotherapy and ICIs. A report systematically compared CT features of radiotherapy- versus ICIs-pneumonitis, and radiotherapy- versus ICIs-pneumonitis exhibit distinct spatial features on CT. A study identified similarities and differences in pneumonitis morphology on CT scans among pneumonitis due to radiotherapy alone, ICIs alone, and the combination of both [ 27 ]. Patients who have bilateral CT changes involving at least three lobes were more likely to have ICIs-pneumonitis, whereas those with unilateral CT changes with sharp borders are more likely to have RP. After RT and/or ICIs, severe pneumonitis is associated with bilateral and multifocal CT changes. IMRT is associated with a lower rate of grade 2 + RP compared to three-dimensional conformal radiotherapy (3D-CRT) in patients with NSCLC who received CCRT followed by durvalumab [ 28 ]. The treatment of RP should be multidisciplinary, the uncertainty of whether pneumonitis is drug versus radiation-induced, and the importance risk stratification [ 29 ]. A consensus reported that an equivalent to 60 mg oral prednisone per day is a typical initial regimen for uncomplicated RP. In addition, initial steroid dose should be administered for duration of 2 weeks, followed by a gradual, weekly taper. For severe pneumonitis, intravenous methylprednisolone is recommended for 3 days prior to initiating oral corticosteroids. ICIs combined with etoposide-platinum are recommended as standard first-line therapy for SCLC [ 30 ]. Serplulimab (Shanghai Henlius Biotech, Inc.), an anti-PD-1 antibody, received its first approval on 25 Mar 2022 in China for the treatment of adult patients with advanced unresectable or metastatic microsatellite instability-high (MSI-H) solid tumors that have failed to respond to previous standard treatments [ 31 ]. A randomized clinical trial (ASTRUM-005) showed that serplulimab plus chemotherapy improved the overall survival compared with chemotherapy alone in patients with previously untreated extensive-stage SCLC [ 32 ]. Drug combination regimens for SCLC include adebrelimab, atezolizumab, durvalumab, durvalumab plus tremelimumab, ipilimumab, pembrolizumab, serplulimab, benmelstobart plus anlotinib, tislelizumab, tiragolumab plus atezolizumab and toripalimab in combination with chemotherapy. A network meta-analysis showed that PD-1/PD-L1 inhibitors-based combinations are associated with significant survival improvement for treatment-naïve extensive-stage SCLC patients. Meanwhile, benmelstobart plus anlotinib with chemotherapy yielded better survival benefit versus chemotherapy alone or other ICIs and chemotherapy [ 30 ]. Despite technical developments in treatment delivery, RILT remains a crucial problem in thoracic radiotherapy. To prevent this toxicity in the future and individualize patient treatment, objective measures of pulmonary toxicity are needed. Clinically based RILT scores have their limitations, and more objective measures such as pulmonary functions tests (PFTs) might help to improve treatment strategies [ 33 ]. Pulmonary function declines after RT in a dose-dependent manner. Studies using prospective data regarding parameters predicting changes in PFT results are limited. Analyses of high-quality data are therefore urgently needed to improve individualization of advanced radiation therapy. Limited information is available to support the role of CPET in understanding RT intolerance in SCLC patients. CPET is suited to quantify exercise tolerance in people with chronic respiratory disease, and it could be utilized to evaluate the maximal patient effort, peak rate of oxygen consumption, ventilatory reserve, and exertional dyspnea [ 34 ]. Consequently, noninvasive CPET offers an opportunity to improve diagnostic accuracy of RT intolerance and facilitate dosage and regimen decisions of radiotherapy. Interstitial lung diseases (ILDs) represent decreased lung function and impaired gas exchange, primarily characterized by alveolar and interstitial inflammation and/or fibrosis. Pathophysiological mechanisms responsible for reduced exercise tolerance include altered respiratory mechanics, impaired gas exchange, cardiovascular abnormalities and peripheral muscle dysfunction [ 35 ]. CPET is increasingly used for patients with ILD, because exercise performance or dyspnoea on exertion cannot reliably be predicted by resting pulmonary function tests. CPET can unmask anomalies in the integrated functions of the respiratory, cardiovascular, metabolic, peripheral muscle and neurosensory systems in ILDs [ 36 ]. Additionally, patient-reported outcomes may be discordant to severity of illness as assessed by objective parameters. Severity of illness by patient report versus CPET was frequently discordant [ 37 ]. Mortality tracked more closely with the objective data, highlighting the importance of relying not only on patient report, but also objective data when risk stratifying patients with heart failure. Theoretically, multiple risk factors can be combined to accurately predict the incidence of RP. RP still occurred in 128 cases in this retrospective cohort. In the present study, VO 2max and FEV 1 showed significant association with grade 2 + RP. Therefore, CPET might be used to predict the development of RP. Additionally, the optimal patient selection threshold was accordingly determined. Finally, the limitations of the present cohort study should also be indicated. Besides the small sample size and the retrospective nature of this study, the notable bias in gender also has an effect on the statistical results. Moreover, the different experience of the radiotherapy operators also affects the results. Therefore, the findings of the present study should be interpreted with caution. More and better evidence are warranted to further verify the role of CPET in predicting the onset of RP. Conclusions This study illustrated that CPET parameters were correlated with the incidence of grade 2 + RP after sequential immune-chemotherapy and thoracic RT in patients with SCLC. Prior immune-chemotherapy (ranging from 0 to 4 cycles) is not correlated with the onset of grade 2 + RP. Male and CCRT are associated with increased risk of severe RP, whereas higher VO 2 max and FEV1 are correlated with lower incidence of grade 2 + RP. Well-designed, prospective studies are needed to validate these occasional findings before incorporating CPET into RT-related clinical practice or trial planning guidelines. Declarations Ethics approval and consent to participate The study was approved by the Ethics Committee of Xuzhou Central Hospital. The study was reviewed and approved by Southeast University School of Medicine Institutional Review Board. All study participants, or their legal guardian, provided informed consent prior to study. The data were presented anonymously for patients’ privacy concern. The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are investigated and resolved. Competing interests The authors declare that they have no competing interests. Funding This study was financially supported by Pengcheng Talent-Medical Youth Reserve Talent Training Project (XWRCHT20220018) for Yuanyuan Liu. Author Contribution Y-YL, J-HZ, W-BW and MZ wrote this paper. MZ, HZ and H-TY contributed to the screening and selection of the reports from the databases. All authors contributed to preparation of the data used in this paper. All authors read and approved the final manuscript. Acknowledgements The authors would like to thank all the reviewers for their constructive advices. 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Thoracic cancer 2024, 15 (15):1246-1262. Lee A: Serplulimab: First Approval . Drugs 2022, 82 (10):1137-1141. Cheng Y, Han L, Wu L, Chen J, Sun H, Wen G, Ji Y, Dvorkin M, Shi J, Pan Z et al : Effect of First-Line Serplulimab vs Placebo Added to Chemotherapy on Survival in Patients With Extensive-Stage Small Cell Lung Cancer: The ASTRUM-005 Randomized Clinical Trial . Jama 2022, 328 (12):1223-1232. Niezink AGH, de Jong RA, Muijs CT, Langendijk JA, Widder J: Pulmonary Function Changes After Radiotherapy for Lung or Esophageal Cancer: A Systematic Review Focusing on Dose-Volume Parameters . The oncologist 2017, 22 (10):1257-1264. Stickland MK, Neder JA, Guenette JA, O'Donnell DE, Jensen D: Using Cardiopulmonary Exercise Testing to Understand Dyspnea and Exercise Intolerance in Respiratory Disease . Chest 2022, 161 (6):1505-1516. Bonini M, Fiorenzano G: Exertional dyspnoea in interstitial lung diseases: the clinical utility of cardiopulmonary exercise testing . 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Cite Share Download PDF Status: Published Journal Publication published 05 Dec, 2025 Read the published version in Radiation Oncology → Version 1 posted Editorial decision: Revision requested 14 Oct, 2025 Reviews received at journal 14 Sep, 2025 Reviewers agreed at journal 03 Sep, 2025 Reviewers agreed at journal 07 Aug, 2025 Reviewers invited by journal 07 Aug, 2025 Editor assigned by journal 14 Jul, 2025 Submission checks completed at journal 13 Jul, 2025 First submitted to journal 13 Jul, 2025 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7114664","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":499039316,"identity":"3c58bc7b-633c-410d-bb4c-1779b6d83be3","order_by":0,"name":"Yuanyuan Liu","email":"","orcid":"","institution":"Xuzhou Central Hospital, Xuzhou Clinical College of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Liu","suffix":""},{"id":499039318,"identity":"c5aea609-6df5-414e-97f4-d1ec701c8f3b","order_by":1,"name":"Jinghao Zhang","email":"","orcid":"","institution":"Xuzhou Central 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17:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7114664/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7114664/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13014-025-02774-w","type":"published","date":"2025-12-05T15:57:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88951826,"identity":"c4486937-3331-43a3-a306-0863c097298e","added_by":"auto","created_at":"2025-08-13 05:55:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":918974,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow chart of data collection\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7114664/v1/6cc7204805891b714928ea43.png"},{"id":88952221,"identity":"4682e6ba-9e7b-4481-9241-8cb3edf18aba","added_by":"auto","created_at":"2025-08-13 06:03:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1392291,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate analysis on the predictive factors of RP\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7114664/v1/ef1f079fb12b528094b4eb52.png"},{"id":88950353,"identity":"4dece328-8734-4ece-83b2-dafff8177598","added_by":"auto","created_at":"2025-08-13 05:47:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":600989,"visible":true,"origin":"","legend":"\u003cp\u003eROC of the predictors of RP\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7114664/v1/a62f03bd89ca77b88379c70d.png"},{"id":97723807,"identity":"0d09e39e-a0cf-433f-865b-b087d44e084a","added_by":"auto","created_at":"2025-12-08 16:07:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7326047,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7114664/v1/d1fe3234-6eb9-424d-bfa5-460a86349baa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The risk of symptomatic radiation pneumonitis in small cell lung cancer patients following sequential immune-chemotherapy and radiotherapy: a multicenter retrospective cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUnderstanding the potential risk factors of radiation pneumonitis (RP) is important for radiotherapy planning. Immune checkpoint inhibitors (ICIs) plus thoracic radiotherapy may magnify the risk of symptomatic radiation pneumonitis (RP). In the era of immunotherapy, the major concerns regarding radiotherapy for lung cancer include the potential aggregated incidence and degree of RP following ICIs. Data on the incidence of RP in patients with small cell lung cancer (SCLC) following sequential immune-chemotherapy and radiotherapy are limited. In addition, whether cardiopulmonary exercise testing(CPET) information plays a role in predicting the incidence of RP following thoracic radiotherapy is unclear.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePotential predictive factors for RP include clinical, physical and dosimetric factors such as mean lung dose (MLD), V\u003csub\u003e5/20\u003c/sub\u003e (percentage of the lung volume receiving 5, 20 Gy). Pulmonary function variables, tumor location, smoking history, emphysema, interstitial lung disease, and concurrent chemotherapy might also be correlated with the incidence of RP. Therefore, the V5, V10, MLD were strictly restrained in the present cohort with the aim to diminish the incidence and severity of RP. For patients with locally advanced NSCLC treated with definitive chemo-radiotherapy, the NCCN recommended lung dose–volume constraints for conventionally fractionated radiation therapy as follows: V 20 ≤35%, V 5 ≤ 65%, and MLD ≤20 Gy [1].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe performed a multicenter cohort study. The initial aim was to investigate the potential risk of prior immune-chemotherapy on the incidence of grade 2+ RP. The second aim was to validate the performance of CPET to accurately predict the risk of symptomatic (grade 2+) RP, which might be more reliable than normal lung function test. We collected data of the clinical factors, dosimetric parameters, and the occurrence of grade 2+ RP in SCLC patients, attempting to investigate the necessity of CBET before RT.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003ePatient Eligibility\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA retrospective study was conducted on the patients pathologically diagnosed with SCLC treated with thoracic radiotherapy at the Department of Radiotherapy, Xuzhou Central Hospital and Xuzhou First People's Hospital between April 1, 2022, and March 31, 2025. The patients with Eastern Cooperative Oncology Group (ECOG) performance status of 0 to 2, receiving thoracic radiotherapy with no cause of death other than RP within 3 months after radiotherapy, were included. Exclusion criteria were as follows: acute immunotherapy-associated pneumonitis, patients who did not finish the entire radiotherapy due to reasons other than radiation-related complications, missing data, and radiotherapy interruption for more than one week. Ethical approval (XZXY-LK-20230427-070) was obtained from the Ethical Review Committee of Xuzhou Central Hospital, Medical School of Southeast University. Informed consent of each patient was not required due to the retrospective nature of this study. The data were presented anonymously for privacy concern.\u003c/p\u003e\u003cp\u003eThe variables including patients\u0026rsquo; age, gender, pulmonary comorbidities, diabetes mellitus (DM), percutaneous coronary intervention (PCI) for coronary heart disease (CHD), TNM staging, immune-chemotherapy cycles, total radiotherapy dosage, and dose per fraction were collected. Physical examination, chest and abdominal computed tomography (CT), brain magnetic resonance imaging (MRI), bone emission computed tomography (ECT) and fluorodeoxyglucose positron emission tomography-CT were performed to obtain detailed data for staging, which was determined using the 8th edition of the tumor, node, and metastasis classification of lung cancer[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eImmune-chemotherapy before radiotherapy\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe first-choice chemotherapy regimen included intravenous etoposide (100 mg/m\u003csup\u003e2\u003c/sup\u003e of body surface area) of on days 1\u0026ndash;5. Patients received either of cisplatin (75 mg/m\u003csup\u003e2\u003c/sup\u003e of body surface area) and etoposide (chemotherapy only), or intravenous serplulimab (4.5 mg/kg of body weight) plus cisplatin and etoposide (immune-chemotherapy) every 3 weeks for up to 12 weeks. Thereafter, they were treated with concurrent or sequential radiotherapy after 1\u0026ndash;4 courses of induction immune-chemotherapy, respectively. The concurrent chemoradiotherapy (CCRT) regimens included cisplatin plus etoposide. The interval between induction chemotherapy/ immune-chemotherapy and radiotherapy was about 2 weeks.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRadiotherapy\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll patients underwent a planning CT scan when immobilized in a supine position as reported [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Treatment technique included fixed beam coplanar intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) techniques [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Anisotropic analytical algorithm (version 10.0) was used for dose calculation. Vx was defined as the percentage of total lung volume receiving equal to or greater than the\u0026times; Gy radiation dosage. RT was delivered with a total dose of 20\u0026ndash;60 Gy, at 1.5, 2 or 2.5 Gy per fraction, 5 days per week. Treatment planning was performed using an ADAC Pinnacle\u0026trade; (Philips Medical Systems) system. Treatment consisted of 6 or 10 MV photon thoracic IMRT using a Siemens Artiste (Oncology Care Systems, Siemens Medical Solutions, CA, USA) digital linear accelerator. The target volumes were set by experienced radiation oncologists focused on lung cancer. If the lung dose exceeded the safety range (V20\u0026thinsp;\u0026le;\u0026thinsp;30%, V5\u0026thinsp;\u0026le;\u0026thinsp;60%, MLD\u0026thinsp;\u0026le;\u0026thinsp;17Gy), the total dose would be appropriately reduced.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssessment of RP and follow-up\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhen patients complained of dyspnea, chest tightness, or fatigue, a chest thin-slice CT was performed during the RT period. Otherwise, the CT would be carried out 1\u0026ndash;2 weeks after the RT. Thereafter, patients were reevaluated every month after treatment to check the physical status and thoracic CT performed at each follow-up visit. The diagnoses of RILI including RP and radiation fibrosis are made by exclusion using clinical assessment and radiological findings. The following factors were collected: age, gender, ECOG performance status, target tumor locations, smoking status, normal pulmonary function test such as forced expiratory volume in one second (FEV1) and the diffusing capacity of the lungs for carbon monoxide (DLCO), respiratory comorbidity (history of chronic lung diseases, including chronic obstructive pulmonary diseases, chronic bronchitis, interstitial lung disease, and pulmonary tuberculosis), CPET parameters such as VO\u003csub\u003e2max\u003c/sub\u003e, TNM staging, cycles of immune-chemotherapy before RT, and radiotherapy technique, and dosimetric data including the prescription dose, fractions, V5, V20, and MLD of the total lung. Enrolled SCLC patients receiving thoracic radiotherapy were divided into symptomatic (grade 2+) RP and asymptomatic (grade 0\u0026ndash;1) RP groups, and independent prognostic factors were determined using univariate and multivariate logistic regression analyses.\u003c/p\u003e\u003cp\u003eThe primary endpoint was grade 2 or worse (grade 2+) RP, which was confirmed by experienced radiation oncologists and pulmonologists based on the manifestation and radiological findings on thin-slice CT images of the patients and then graded from 1 to 5 according to the Common Terminology Criteria for Adverse Events (CTCAE) version 5.0 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Grade 1: No symptoms, only clinical or imaging changes, and no treatment is required; Grade 2: Mild symptoms, limited work-related daily activities, need drug treatment; Grade 3: Severe symptoms, personal daily activities are limited, requiring oxygen inhalation; Grade 4: Life-threatening respiratory symptoms that require urgent treatment; Grade 5: Causing the death of the patient. Grade 0 is defined as no symptom or radiographic change.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eBinary logistic regression was performed for univariate and multivariate analysis to assess the relationship between risk factors and grade 2\u0026thinsp;+\u0026thinsp;RP. The significant factors with P\u0026thinsp;\u0026lt;\u0026thinsp;0.20 in univariate analysis were applied to multivariate logistic regression analysis by using forward stepwise (likelihood ratio) method. The chi-square test and t-test were employed to compare the baseline characteristics between the two groups. All statistical analyses were performed using IBM SPSS statistics version 25.0 (IBM Corp, New York, NY; formerly SPSS Inc., Chicago, IL). Independent risk factors (OR\u0026thinsp;\u0026gt;\u0026thinsp;1 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in multivariate logistic regression) of grade\u0026thinsp;\u0026ge;\u0026thinsp;2 RP were ultimately revealed. The OR and 95% CI of the potential risk factors revealed by multivariate regression were depicted using MedCalc software (MedCalc, Version 20.015, MedCalc Software Ltd.). The receiver operating characteristic (ROC) curve and the value of the area under the curve (AUC) were further obtained to analysis the predictive efficacy of the risk factors. All tests were two-sided. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003ePatient characteristics and incidence of grade 2\u0026thinsp;+\u0026thinsp;RP\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFinally, a total of 443 patients with SCLC were eligible and the data were further analyzed, including 161(36.3%) male and 282 (63.7%) female patients, with a median age of (57.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.2) years (range, 26\u0026ndash;87). A detailed flowchart of patient selection was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Antibiotics and intravenous methylprednisolone were used to treat grade 2\u0026thinsp;+\u0026thinsp;RP which was diagnosed by complaints and thin-slice CT images. All the RP patients showed alleviation of the severity of pneumonitis. In total, 128 (28.9%) patients were diagnosed with grade 2\u0026thinsp;+\u0026thinsp;RP. The median interval from the completion of radiotherapy to the appearance of grade 2\u0026thinsp;+\u0026thinsp;RP was 49 days (range, 35\u0026ndash;105 days). In detail, 87 [19.6%], 35 [7.9%], and 6 [1.4%] developed grade 2, grade 3, and grade 4 RP in this cohort, respectively. No grade 0 or grade 5 RP was recorded. Baseline characteristics of the patients with grade 1 RP (n\u0026thinsp;=\u0026thinsp;315) and those with grade 2\u0026thinsp;+\u0026thinsp;RP (n\u0026thinsp;=\u0026thinsp;128) were summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003eand\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Radiotherapy was discontinued urgently in one (0.2%) patient because of RP during the treatment period.\u003c/p\u003e\u003cp\u003e\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\u003eUnivariate and multivariate logistic analysis of the baseline characteristics of the patients diagnosed with small cell lung cancer (n\u0026thinsp;=\u0026thinsp;443)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;443)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGrad 1 RP (n\u0026thinsp;=\u0026thinsp;315)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGrade 2\u0026thinsp;+\u0026thinsp;RP (n\u0026thinsp;=\u0026thinsp;128)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOdds ratio (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOdds ratio (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, year, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57.2 (12.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57.5 (11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56.6 (12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.994 (0.978\u0026ndash;1.011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.968 (0.944\u0026ndash;0.992)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender: Female/Male, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e282(63.7)/ 161(36.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e214(67.9)/ 101(32.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68(53.1)/ 60(46.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.870 (1.228\u0026ndash;2.846)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.408 (1.406\u0026ndash;4.125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody mass index, kg/m\u003csup\u003e2\u003c/sup\u003e, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.9 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.0 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.6 (3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.964 (0.907\u0026ndash;1.025)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.971 (0.903\u0026ndash;1.044)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.420\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes mellitus, No/ Yes, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e415(93.7)/ 28(6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e298(94.6)/ 17(5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e117(91.4)/ 11(8.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.648 (0.749\u0026ndash;3.624)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.386 (0.920\u0026ndash;6.188)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePCI for CHD, No/ Yes, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e409(92.3)/ 34(7.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e296(94.0)/ 19(6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e113(88.3)/ 15(11.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.068 (1.106\u0026ndash;4.210)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.236 (0.898\u0026ndash;5.564)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eECOG PS before radiotherapy: 0/1/2, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e153(34.5)/ 241(54.4)/ 49(11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e117(37.1)/ 172(54.6)/ 26(8.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36(28.1)/ 69(53.9)/ 23(18.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eECOG 0 vs.1\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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.304 (0.818\u0026ndash;2.079)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.645 (0.344\u0026ndash;1.210)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.172\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eECOG 0 vs. 2\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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.875 (1.465\u0026ndash;5.641)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.161 (0.449\u0026ndash;3.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.759\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistribution of the target lesion, Upper /Lower / Middle Lobe, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e191(43.1)/ 219(49.4)/ 33(7.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e135(42.9)/ 157(49.8)/ 23(7.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56(43.8)/ 62(48.4) /10(7.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper vs. Lower lob\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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.952 (0.620\u0026ndash;1.461)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.814 (0.709\u0026ndash;1.979)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.519\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper vs. Middle lobe\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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.048 (0.469\u0026ndash;2.345)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.027 (0.399\u0026ndash;2.643)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.956\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize of the target tumor, cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.971 (0.863\u0026ndash;1.092)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.967 (0.824\u0026ndash;1.136)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.686\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNM staging\u003csup\u003e#\u003c/sup\u003e: 1/2/3/4, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28(6.3)/ 86(19.4)/ 292(65.9)/ 37(8.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18(5.7)/ 55(17.5)/ 217(68.9)/ 25(7.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10(7.8)/ 31(24.2)/ 75(58.6)/ 12(9.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage 1 vs. 2\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=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.821 (0.612\u0026ndash;1.101)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.059 (0.352\u0026ndash;3.183)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.919\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage 1 vs. 3\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=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.519 (0.190\u0026ndash;1.421)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.202\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage 1 vs. 4\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=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.571 (0.152\u0026ndash;2.140)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.405\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious immune-chemotherapy*, None/1 /2 /3 /4 courses, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e114(25.7)/ 22(5.0)/ 132(29.8)/ 53(12.0)/ 122(27.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e91(28.9)/ 17(5.4)/ 85(27.0)/ 37(11.7)/ 85(27.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23(18.0)/ 5(3.9)/ 47(36.7)/ 16(12.5)/ 37(28.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone vs. 1\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=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e1.131 (0.986\u0026ndash;1.298)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.507 (0.129\u0026ndash;1.986)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.330\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone vs. 2\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=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.987 (1.000-3.947)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone vs. 3\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=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.237 (0.510-3.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.638\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone vs. 4\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=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.163 (0.550\u0026ndash;2.460)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.692\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConcurrent chemo-radiotherapy*, No/ Yes, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130(29.3)/ 313(70.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109(34.6)/ 206(65.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21(16.4)/ 107(83.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.696 (1.599\u0026ndash;4.545)\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\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.249 (1.193\u0026ndash;4.240)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThoracic radiation dose (Gy), mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48.3 (8.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.7 (7.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.3 (9.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.981 (0.957\u0026ndash;1.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.004 (0.948\u0026ndash;1.063)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.898\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFraction daily, 1.5/2.0/2.5 Gy, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65/239/139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45/175/95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20/64/44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.126 (0.605\u0026ndash;2.093)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.709\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.735 (0.326\u0026ndash;1.656)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.458\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eV5 (%), mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51.0 (5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50.9 (4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.5 (5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.024 (0.983\u0026ndash;1.067)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.031 (0.983\u0026ndash;1.181)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.211\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eV20 (%), mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25.0 (5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.3 (5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.4 (6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.972 (0.936\u0026ndash;1.008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.964 (0.898\u0026ndash;1.035)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.310\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMLD, Gy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.8 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.9 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.6 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.966 (0.899\u0026ndash;1.039)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.009 (0.894\u0026ndash;1.138)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.884\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRadiotherapy procedure, IMRT/ VMAT, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e269(60.7)/ 174(39.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e187(59.4)/ 128(40.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82(64.1)/ 46(35.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.820 (0.536\u0026ndash;1.254)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.971 (0.592\u0026ndash;1.593)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.907\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eData are reported as N (%) or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e*Carboplatin plus Etoposide.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviations: CI, confidence interval; COPD, chronic obstructive pulmonary disease; ECOG-PS, Eastern Cooperative Oncology Group performance status; MLD, mean lung dose of the lung; V20, percentage of the lung volume received at least 20 Gy; V5, percentage of the lung volume received at least 5 Gy.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e#\u003c/sup\u003eAccording to the 8th edition of the AJCC/TNM staging system for lung cancer.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\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 and multivariate logistic analysis of the lung function and cardiopulmonary exercise testing parameters (n\u0026thinsp;=\u0026thinsp;443)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" 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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;443)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGrad 1 RP (n\u0026thinsp;=\u0026thinsp;315)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eGrade 2\u0026thinsp;+\u0026thinsp;RP (n\u0026thinsp;=\u0026thinsp;128)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOdds ratio (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOdds ratio (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePulmonary comorbidity\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\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD, No/ Yes, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e384(86.7)/ 59(13.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e274(87.0)/ 41(13.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e110(85.9)/18(14.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.094 (0.602\u0026ndash;1.986)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.152 (0.548\u0026ndash;2.424)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.709\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBronchiectasis, No/ Yes, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e416(93.9)/ 27(6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e297(94.3)/ 18(5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e119(93.0)/ 9(7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.248 (0.545\u0026ndash;2.856)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.562 (0.552\u0026ndash;4.422)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.401\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsthma, No/ Yes, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e417(94.1)/ 26(5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e298(94.6)/ 17(5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e119(93.0)/ 9(7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.326 (0.575\u0026ndash;3.057)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.311 (0.466\u0026ndash;3.692)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.608\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePneumoconiosis, No/ Yes, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e430(97.1)/ 13(2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e309(98.1)/ 6(1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e121(94.5)/ 7(5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.979 (0.981\u0026ndash;9.045)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.562 (0.552\u0026ndash;4.422)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.401\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever/ Current or former smoker, N (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e198(44.7)/ 245(55.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e147(46.7)/ 168(53.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51(39.8)/ 77(60.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.321 (0.870\u0026ndash;2.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.976 (0.457\u0026ndash;2.083)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.950\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePack-year of cigarette smoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16.7 (22.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16.2 (22.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.0 (23.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.003 (0.995\u0026ndash;1.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.999 (0.984\u0026ndash;1.015)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.948\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWR peak (W)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e105.9 (32.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e107.1 (31.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e103.1 (33.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.996 (0.990\u0026ndash;1.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.007 (0.995\u0026ndash;1.019)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.267\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVO\u003csub\u003e2\u003c/sub\u003e peak (mL/min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1145.2 (334.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1152.3 (328.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1127.6 (350.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.000 (0.999-1.000 )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.479\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.999 (0.998\u0026ndash;1.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.247\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVO\u003csub\u003e2\u003c/sub\u003e peak/HR (mL/beat)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.0 (2.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.1 (2.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.9 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.947 (0.901\u0026ndash;1.503)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.507\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.976 (0.859\u0026ndash;1.110)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.712\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVO₂ max, mL/(min\u003csub\u003e*\u003c/sub\u003ekg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18.8 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.4 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.4 (3.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.872 (0.823\u0026ndash;0.924)\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\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.897 (0.828\u0026ndash;0.972)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAT (mL/min)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e819.2 (322.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e825.8 (350.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e803.0 (243.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.000 (0.999-1.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000 (0.999\u0026ndash;1.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.872\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eATVO\u003csub\u003e2\u003c/sub\u003e/kg (mL/[min\u003csub\u003e*\u003c/sub\u003ekg])\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.5 (9.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.6 (9.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.6 (9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.001 (0.979\u0026ndash;1.023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.003 (0.977\u0026ndash;1.029)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.819\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLVEF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e59.8 (7.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60.3 (7.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58.6 (8.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.972 (0.948\u0026ndash;0.998)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.976 (0.945\u0026ndash;1.009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFEV1, L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.2 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.3 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.1 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.284 (0.154\u0026ndash;0.522)\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\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.369 (0.161\u0026ndash;0.846)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFEV1/FVC (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e82.6 (12.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e82.8 (11.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e81.9 (13.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.995 (0.978\u0026ndash;1.011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.005 (0.984\u0026ndash;1.027)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.650\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDLCO (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e76.3 (9.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76.4 (9.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e76.1 (10.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.997 (0.976\u0026ndash;1.018)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.021 (0.992\u0026ndash;1.051)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.149\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eData are reported as N (%) or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviations: CI, confidence interval; FEV\u003csub\u003e1\u003c/sub\u003e, forced expiratory volume in 1 s; FVC, forced vital capacity; DLCO, diffusing capacity of the lungs for carbon monoxide.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eUnivariate and multivariate logistic regression of the parameters\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe results of the univariate analysis were reported in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Potential factors predicting grade 2\u0026thinsp;+\u0026thinsp;RP were identified as follows: male, PCI for CHD, ECOG score, VO\u003csub\u003e2 max\u003c/sub\u003e, FEV1, and CCRT (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively). Multivariate analysis indicated that age, body mass index, smoking status and pack-year of smoking consumption, diabetes, pulmonary comorbidities including COPD, bronchiectasis, asthma and pneumoconiosis, target tumor location, prior chemotherapy or immune-chemotherapy, dosimetric factors such as lung V5, V20 and MLD, radiotherapy technique (IMRT vs. VMAT) were not independent predictors of grade 2\u0026thinsp;+\u0026thinsp;RP (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, on multivariable analysis, male, VO\u003csub\u003e2\u003c/sub\u003emax, FEV\u003csub\u003e1\u003c/sub\u003e and CCRT significantly predicted the incidence of grade 2\u0026thinsp;+\u0026thinsp;RP (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively). In detail, male and CCRT were positively correlated with the occurrence of grade 2\u0026thinsp;+\u0026thinsp;RP; whereas VO\u003csub\u003e2\u003c/sub\u003emax and FEV\u003csub\u003e1\u003c/sub\u003e were negatively associated with the onset of grade 2\u0026thinsp;+\u0026thinsp;RP. The odd ratios (OR) and 95% confidence interval (CI) calculated by multivariate logistic regression for male, CCRT, VO\u003csub\u003e2\u003c/sub\u003emax and FEV\u003csub\u003e1\u003c/sub\u003e were 2.408 (1.406\u0026ndash;4.125), 2.249 (1.193\u0026ndash;4.240), 0.897 (0.828\u0026ndash;0.972) and 0.369 (0.161\u0026ndash;0.846), respectively, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFurthermore, the receiver operating characteristic (ROC) curve and the value of the area under the curve (AUC) were obtained to analysis the predictive efficacy of these correlated factors. The AUC of the ROC curve established using selected parameters including age, gender, diabetes, PCI, cycles of previous immune-chemotherapy before radiotherapy, concurrent chemo-radiotherapy, IMRT vs. VMAT, VO₂ max, FEV\u003csub\u003e1\u003c/sub\u003e, and DLCO (%) was 0.766 (95% CI: 0.718\u0026ndash;0.815) for predicting the occurrence of grade 2\u0026thinsp;+\u0026thinsp;RP, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eRadiation-induced lung injury (RILI) is one of the main dose-limiting toxicities in radiation therapy (RT) for lung cancer. Approximately 10\u0026ndash;20% of patients show signs of RILI [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Technological advancements as well as patient selection based on the potential risk factors in RT have helped to reduce RILI. Predicting patients at risk for RILI need to be further improved.\u003c/p\u003e\u003cp\u003eRP is diagnosed based on the CT imaging findings of parenchymal changes, typical manifestations after exclusion of acute infection or embolism, heart failure, drug-induced pneumonitis, and pseudo-progression of the tumor [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. To minimize the risk of grade 2\u0026thinsp;+\u0026thinsp;RP when delivering 4 Gy per fraction as hypofractionated RT at either 60 Gy or 72 Gy, it is advisable to maintain lung V5\u0026thinsp;\u0026lt;\u0026thinsp;41.3%, V20\u0026thinsp;\u0026lt;\u0026thinsp;17.7% and MLD\u0026thinsp;\u0026lt;\u0026thinsp;10.6 Gy, which can also be considered as lower-priority constraints [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGenerally, RP develops at 4 weeks following conventionally fractionated therapy [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Signs of pulmonary infection include a unilateral or bilateral lung opacity appearing prior to completion of radiation, tree-in-bud opacities, and cavitation. RT-related necrosis and local recurrence can also manifest as cavitation, but it generally occur at a later interval following completion of radiotherapy. RP was diagnosed when lung opacities were located within the radiation portal. Moreover, indications for thoracic RT are expanding and the incidence of serious pulmonary complications has decreased following the advances in radiation delivery techniques. More sophisticated techniques of conformal RT technologies such as IMRT, VMAT, stereotactic body radiation therapy (SBRT) or stereotactic ablative radiotherapy (SABR), and stereotactic radio-surgery (SRS) may be associated with a lower incidence of RILI compared with standard, three-dimensional conformal RT [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. It is reported that risk factors for RILI including V20\u0026thinsp;\u0026ge;\u0026thinsp;30%, V5\u0026thinsp;\u0026ge;\u0026thinsp;65%, MLD\u0026thinsp;\u0026gt;\u0026thinsp;20 Gy, and target tumor located in lower lobe of the patients [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In this study, grade 2\u0026thinsp;+\u0026thinsp;RP occurred in 28.9% of these SCLC patients, whereas grade 4 RP was uncommon (1.4%). After prior immune-chemotherapy, the median time to onset of grade 2\u0026thinsp;+\u0026thinsp;RP was mainly within 3 months since initiation of radiotherapy (approximately 90%).\u003c/p\u003e\u003cp\u003eFactors associated with higher risk of grade 2\u0026thinsp;+\u0026thinsp;RP were identified. As shown in this study, normal lung function tests as well as CPET could be considered as a potential indicator for RP for patient selection and radiotherapy planning. The CPET have constituted a significant step in evaluating lung function during radiotherapy and useful predictive tools to avoid severe radiation-associated complications or toxicity. These results warrant further study and validation in large populations before the recommendation or consensus of CPET in clinical practice or trial planning. Nevertheless, clinical benefit with immune-chemotherapy was maintained in patients who experienced grade 2\u0026thinsp;+\u0026thinsp;RP, and most achieved resolution of this event, suggesting the risk of grade 2\u0026thinsp;+\u0026thinsp;RP should not deter use of immune-chemotherapy regimen in eligible patients with SCLC.\u003c/p\u003e\u003cp\u003eTreatment for RP is needed only for symptomatic patients. Mild symptoms can be treated with inhaled steroids, whereas subacute to moderate RP with impaired lung function require oral corticosteroids. Patients who do not tolerate or are refractory to steroids can be considered using immunosuppressive agents. Improvements in radiation technique, early diagnosis and appropriate treatment will lead to lower rates of RP and an overall good prognosis.\u003c/p\u003e\u003cp\u003eCCRT is the first-line treatment for patients with limited-stage SCLC. The pooled incidence of CCRT-induced grade 3\u0026ndash;5 RP in unresectable NSCLC patients was estimated to be 3.62%-7.85% using platinum-based doublet chemotherapy, with incidence varying in different studies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Another study reported that the pooled incidence of grade\u0026thinsp;\u0026ge;\u0026thinsp;3 pneumonitis was 3.28%-6.34%, while the incidence of grade 5 (fatal) pneumonitis was 0.29%-0.88% [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A systematic review showed that the overall rate of symptomatic RP for patients with lung cancer undergoing CCRT was 29.8%, with fatal pneumonitis in 1.9%. Factors predictive of grade 2\u0026thinsp;+\u0026thinsp;RP were V20 and carboplatin plus paclitaxel chemotherapy. Predictors of fatal pneumonitis were daily dose\u0026thinsp;\u0026gt;\u0026thinsp;2 Gy, V20, and lower-lobe tumor location [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Thoracic radiotherapy decisions in patients with interstitial lung disease (ILD) are complex due to concerns about severe or even fatal RP. Another review showed that the median overall incidence of grade 3\u0026thinsp;+\u0026thinsp;RP after thoracic radiotherapy for treatment of lung cancer was 19.7% (range 8\u0026ndash;46%); in addition, the RP incidence was greater in patients undergoing conventional radical radiotherapy (median 31.8%) compared with particle beam therapy- or stereotactic ablative radiotherapy (median 12.5%) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Meanwhile, the median rate of grade 5 RP was 11.9% (range 0\u0026ndash;60%). The presence of ILD was an independent predictor of severe RP, whereas V5, V10, V20 and MLD were the most common dosimetric predictors for severe RP, which need to be strictly constrained [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Furthermore, patients with lung cancer associated with ILD have a poor prognosis, which may be ascribed to the high risk of severe and even fatal RP. Careful patient selection and strict radiation dosage constraints should be utilized regularly. In addition, DM is an important risk factor for RP in chest tumor patients undergoing RT [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Another study reported that, besides the known dosimetric factors, DM was the most important risk factor of RP incidence after concomitant chemoradiotherapy, and the risk was tripled compared to patients without DM [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The use of modern radiation techniques, such as IMRT, is crucial to meet restrictive radiotherapy dosage constraints to lower the incidence of RP.\u003c/p\u003e\u003cp\u003eChemoradiotherapy plus ICIs is the standard of care for patients with SCLC. A few studies revealed that the addition of ICIs to CRT was associated with an increased risk of pneumonitis. A retrospective cohort assembled using the Surveillance, Epidemiology, and End Results-Medicare database showed that radiation therapy (RT) and ICIs used for NSCLC patient were, at most, additive rather than synergistic in causing pneumonitis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. ICIs combined with radiotherapy for solid tumors can produce respiratory adverse effects, including cough, pneumonia, and upper respiratory tract infections. A meta-analysis showed that the addition of neoadjuvant and adjuvant ICIs was not significantly associated with increased treatment-related deaths, but it increased the incidence of grade 3\u0026ndash;4 ICIs-related adverse events and treatment discontinuation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The pooled rate of grade 2\u0026thinsp;+\u0026thinsp;pneumonitis for chemoradiotherapy plus ICIs for the treatment of locally advanced NSCLC was significantly higher than that for chemoradiotherapy alone but not that of grade 3\u0026thinsp;+\u0026thinsp;or grade 5. In addition, compared with chemoradiotherapy alone, durvalumab consolidation after chemoradiotherapy appears to be associated with a higher incidence of moderate pneumonitis and chemoradiotherapy plus PD-1 inhibitors with an increased risk of severe pneumonitis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Another meta-analysis revealed that adding ICIs to the conventional treatment for solid tumors significantly increased pneumonitis regardless of the mechanisms of ICIs and cancer type [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Furthermore, an updated meta-analysis assess the risk of respiratory adverse effects in patients with solid tumors treated with immune checkpoint inhibitors (PD-1, PD-L1 and CTLA-4 inhibitors) plus radiotherapy [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe combination of ICIs with radiotherapy may further increase the risk of ICIs-pneumonitis or RP. Early detection and management of pneumonitis in patients receiving RT and/or ICIs are crucial for improving outcomes. It is reported that identifying high-risk patients through interdisciplinary predictive models, radiomics, and biomarkers may help tailor treatment strategies and minimize pneumonitis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. A review of trials regarding combined CCRT plus concomitant immunotherapy followed by immunotherapy maintenance in patients with stage III NSCLC showed that for single-agent immunotherapy with CCRT, pneumonitis occurred in 33% of patients with 7% having grade 3\u0026ndash;5. Double concomitant immunotherapy (PD-1 and CTLA4) plus CCRT was associated with excessive pneumonitis-related mortality of 16% [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Addition of single-agent immunotherapy to CCRT is manageable in selected patients. A network meta-analysis showed no significant difference between the incidence of pneumonitis in concurrent ICIs with radiotherapy and radiotherapy followed by ICIs. The radiotherapy following ICIs (ICIs-first arm) exhibited higher incidence of any grade pneumonitis compared with concurrent- and radiotherapy-first arms [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA systematic review investigated the use of pulmonary function tests (PFT) and other parameters to predict and mitigate RP, thereby improving RT planning. Patients with RP and COPD generally exhibited poorer overall survival. Notably, FEV1 and DLCO declined 24 months post-radiotherapy, while forced vital capacity (FVC) stayed stable. In the majority of studies, age over 60, tumors located in the lower part of the lung, and low FEV1 before RT were associated with a higher risk of RP. Dosimetric factors (V5, V20, and MLD) and metabolic imaging emerged as significant predictors of RP risk [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. A clinical checklist blending patient and tumor characteristics, PFT results, and dosimetric criteria was proposed for assessing RP risk. This approach should guide individualized management to enhance RP prevention strategies. Preexisting interstitial lung disease and thoracic radiation are major risk factors for ICIs-related pneumonitis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], although the mechanisms of injury are still not fully understood. Thoracic radiation increases the risk for ICIs-pneumonitis and may synergize with preexisting interstitial lung diseases (ILD) to worsen toxicity. Preexisting ILD and thoracic radiation may increase the risk for the future development of ICIs-pneumonitis.\u003c/p\u003e\u003cp\u003eICIs-related pneumonitis and RP are challenging to diagnose. Treatment naive patients had higher incidence of grade 1\u0026ndash;4 pneumonitis compared with previously treated patients [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Patients with NSCLC may develop pneumonitis after thoracic radiotherapy and ICIs. A report systematically compared CT features of radiotherapy- versus ICIs-pneumonitis, and radiotherapy- versus ICIs-pneumonitis exhibit distinct spatial features on CT. A study identified similarities and differences in pneumonitis morphology on CT scans among pneumonitis due to radiotherapy alone, ICIs alone, and the combination of both [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Patients who have bilateral CT changes involving at least three lobes were more likely to have ICIs-pneumonitis, whereas those with unilateral CT changes with sharp borders are more likely to have RP. After RT and/or ICIs, severe pneumonitis is associated with bilateral and multifocal CT changes. IMRT is associated with a lower rate of grade 2\u0026thinsp;+\u0026thinsp;RP compared to three-dimensional conformal radiotherapy (3D-CRT) in patients with NSCLC who received CCRT followed by durvalumab [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The treatment of RP should be multidisciplinary, the uncertainty of whether pneumonitis is drug versus radiation-induced, and the importance risk stratification [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A consensus reported that an equivalent to 60 mg oral prednisone per day is a typical initial regimen for uncomplicated RP. In addition, initial steroid dose should be administered for duration of 2 weeks, followed by a gradual, weekly taper. For severe pneumonitis, intravenous methylprednisolone is recommended for 3 days prior to initiating oral corticosteroids.\u003c/p\u003e\u003cp\u003eICIs combined with etoposide-platinum are recommended as standard first-line therapy for SCLC [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Serplulimab (Shanghai Henlius Biotech, Inc.), an anti-PD-1 antibody, received its first approval on 25 Mar 2022 in China for the treatment of adult patients with advanced unresectable or metastatic microsatellite instability-high (MSI-H) solid tumors that have failed to respond to previous standard treatments [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. \u003cb\u003eA\u003c/b\u003e randomized clinical trial (ASTRUM-005) showed that serplulimab plus chemotherapy improved the overall survival compared with chemotherapy alone in patients with previously untreated extensive-stage SCLC [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Drug combination regimens for SCLC include adebrelimab, atezolizumab, durvalumab, durvalumab plus tremelimumab, ipilimumab, pembrolizumab, serplulimab, benmelstobart plus anlotinib, tislelizumab, tiragolumab plus atezolizumab and toripalimab in combination with chemotherapy. A network meta-analysis showed that PD-1/PD-L1 inhibitors-based combinations are associated with significant survival improvement for treatment-na\u0026iuml;ve extensive-stage SCLC patients. Meanwhile, benmelstobart plus anlotinib with chemotherapy yielded better survival benefit versus chemotherapy alone or other ICIs and chemotherapy [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite technical developments in treatment delivery, RILT remains a crucial problem in thoracic radiotherapy. To prevent this toxicity in the future and individualize patient treatment, objective measures of pulmonary toxicity are needed. Clinically based RILT scores have their limitations, and more objective measures such as pulmonary functions tests (PFTs) might help to improve treatment strategies [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Pulmonary function declines after RT in a dose-dependent manner. Studies using prospective data regarding parameters predicting changes in PFT results are limited. Analyses of high-quality data are therefore urgently needed to improve individualization of advanced radiation therapy. Limited information is available to support the role of CPET in understanding RT intolerance in SCLC patients. CPET is suited to quantify exercise tolerance in people with chronic respiratory disease, and it could be utilized to evaluate the maximal patient effort, peak rate of oxygen consumption, ventilatory reserve, and exertional dyspnea [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Consequently, noninvasive CPET offers an opportunity to improve diagnostic accuracy of RT intolerance and facilitate dosage and regimen decisions of radiotherapy.\u003c/p\u003e\u003cp\u003eInterstitial lung diseases (ILDs) represent decreased lung function and impaired gas exchange, primarily characterized by alveolar and interstitial inflammation and/or fibrosis. Pathophysiological mechanisms responsible for reduced exercise tolerance include altered respiratory mechanics, impaired gas exchange, cardiovascular abnormalities and peripheral muscle dysfunction [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. CPET is increasingly used for patients with ILD, because exercise performance or dyspnoea on exertion cannot reliably be predicted by resting pulmonary function tests. CPET can unmask anomalies in the integrated functions of the respiratory, cardiovascular, metabolic, peripheral muscle and neurosensory systems in ILDs [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Additionally, patient-reported outcomes may be discordant to severity of illness as assessed by objective parameters. Severity of illness by patient report versus CPET was frequently discordant [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Mortality tracked more closely with the objective data, highlighting the importance of relying not only on patient report, but also objective data when risk stratifying patients with heart failure.\u003c/p\u003e\u003cp\u003eTheoretically, multiple risk factors can be combined to accurately predict the incidence of RP. RP still occurred in 128 cases in this retrospective cohort. In the present study, VO\u003csub\u003e2max\u003c/sub\u003e and FEV\u003csub\u003e1\u003c/sub\u003eshowed significant association with grade 2\u0026thinsp;+\u0026thinsp;RP. Therefore, CPET might be used to predict the development of RP. Additionally, the optimal patient selection threshold was accordingly determined.\u003c/p\u003e\u003cp\u003eFinally, the limitations of the present cohort study should also be indicated. Besides the small sample size and the retrospective nature of this study, the notable bias in gender also has an effect on the statistical results. Moreover, the different experience of the radiotherapy operators also affects the results. Therefore, the findings of the present study should be interpreted with caution. More and better evidence are warranted to further verify the role of CPET in predicting the onset of RP.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study illustrated that CPET parameters were correlated with the incidence of grade 2\u0026thinsp;+\u0026thinsp;RP after sequential immune-chemotherapy and thoracic RT in patients with SCLC. Prior immune-chemotherapy (ranging from 0 to 4 cycles) is not correlated with the onset of grade 2\u0026thinsp;+\u0026thinsp;RP. Male and CCRT are associated with increased risk of severe RP, whereas higher VO\u003csub\u003e2 max\u003c/sub\u003e and FEV1 are correlated with lower incidence of grade 2\u0026thinsp;+\u0026thinsp;RP. Well-designed, prospective studies are needed to validate these occasional findings before incorporating CPET into RT-related clinical practice or trial planning guidelines.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003e The study was approved by the Ethics Committee of Xuzhou Central Hospital. The study was reviewed and approved by Southeast University School of Medicine Institutional Review Board. All study participants, or their legal guardian, provided informed consent prior to study. The data were presented anonymously for patients’ privacy concern. The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are investigated and resolved.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was financially supported by Pengcheng Talent-Medical Youth Reserve Talent Training Project (XWRCHT20220018) for Yuanyuan Liu.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eY-YL, J-HZ, W-BW and MZ wrote this paper. MZ, HZ and H-TY contributed to the screening and selection of the reports from the databases. All authors contributed to preparation of the data used in this paper. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe authors would like to thank all the reviewers for their constructive advices.\u003c/p\u003e\n\u003ch2\u003eAvailability of data\u003c/h2\u003e\n\u003cp\u003eThe data are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEttinger DS, Wood DE, Aisner DL, Akerley W, Bauman JR, Bharat A, Bruno DS, Chang JY, Chirieac LR, D\u0026apos;Amico TA\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eNon-Small Cell Lung Cancer, Version 3.2022, NCCN Clinical Practice Guidelines in Oncology\u003c/strong\u003e. \u003cem\u003eJournal of the National Comprehensive Cancer Network : JNCCN \u003c/em\u003e2022, \u003cstrong\u003e20\u003c/strong\u003e(5):497-530.\u003c/li\u003e\n\u003cli\u003eTendler S, Grozman V, Lewensohn R, Tsakonas G, Viktorsson K, De Petris L: \u003cstrong\u003eValidation of the 8th TNM classification for small-cell lung cancer in a retrospective material from Sweden\u003c/strong\u003e. \u003cem\u003eLung cancer \u003c/em\u003e2018, \u003cstrong\u003e120\u003c/strong\u003e:75-81.\u003c/li\u003e\n\u003cli\u003eLi F, Liu H, Wu H, Liang S, Xu Y: \u003cstrong\u003eRisk factors for radiation pneumonitis in lung cancer patients with subclinical interstitial lung disease after thoracic radiation therapy\u003c/strong\u003e. \u003cem\u003eRadiation oncology \u003c/em\u003e2021, \u003cstrong\u003e16\u003c/strong\u003e(1):70.\u003c/li\u003e\n\u003cli\u003eSun H, Liu C, Zhang J, Yang G, Han D, Liu T, Huang W, Li B: \u003cstrong\u003eTwice-daily thoracic radiotherapy by intensity-modulated radiation therapy (IMRT) compared with simultaneous integrated boost IMRT (SIB-IMRT) with concurrent chemotherapy for patients with limited-stage small cell lung cancer. 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\u003cstrong\u003e10\u003c/strong\u003e(13):e019864.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"radiation-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"raon","sideBox":"Learn more about [Radiation Oncology](http://ro-journal.biomedcentral.com/)","snPcode":"13014","submissionUrl":"https://submission.nature.com/new-submission/13014/3","title":"Radiation Oncology","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Radiation pneumonitis (RP), Intensity-modulated radiation therapy (IMRT), Volumetric modulated arc therapy (VMAT), Serplulimab, Concurrent chemoradiotherapy (CCRT), Small cell lung cancer (SCLC), VO2 max, Forced expiratory volume in one second (FEV1) ","lastPublishedDoi":"10.21203/rs.3.rs-7114664/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7114664/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u0026nbsp;\u003c/strong\u003eImmune checkpoint inhibitors (ICIs) plus thoracic radiotherapy may magnify the radiation pneumonitis (RP) risk. Data on the risk for symptomatic RP in small cell lung cancer (SCLC) patients following radiotherapy after induction immune-chemotherapy are limited.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatients and methods\u003c/strong\u003e: This multicenter retrospective study included patients with SCLC from two hospitals who started thoracic intensity-modulated radiation therapy (IMRT) or volumetric modulated arc therapy (VMAT) between April 1, 2022 and March 31, 2025. The primary endpoint was grade 2 or worse (grade 2+) RP according to the Common Terminology Criteria for Adverse Events v5.0. Logistic regression analyses and receiver operating characteristic (ROC) analysis were used to assess the correlated parameters with grade 2+ RP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e:\u0026nbsp;A total of 443 patients were reviewed. The median follow-up period was 3 months after radiotherapy. In detail, 87 [19.6%], 35 [7.9%], and 6 [1.4%] developed grade 2, grade 3, and grade 4 RP in this cohort, respectively. No patients recorded grade 0 or 5 RP. On multivariable analysis, male, concurrent chemoradiotherapy (CCRT), VO2max and FEV1 significantly predicted the incidence of grade 2+ RP, with odd ratios (OR) and 95% confidence interval (CI) of 2.408 (1.406-4.125), 2.249 (1.193-4.240), 0.897 (0.828-0.972) and 0.369 (0.161-0.846), respectively(all P\u0026lt;0.05); whereas prior immune-chemotherapy, preexisting pulmonary co-morbidities and smoking history were not significant predictors (P\u0026gt; 0.05, respectively). Furthermore, the area under the ROC curve established using these parameters was 0.766 (95% CI: 0.718-0.815) for predicting the occurrence of grade 2+ RP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e:\u0026nbsp;Prior immune-chemotherapy (ranging from 0 to 4 cycles) is not correlated with the onset of grade 2+ RP. The incidence of grade 2+ RP was relatively high in this multicenter study, and its risk increased remarkably at decreased VO\u003csub\u003e2\u003c/sub\u003e max and FEV\u003csub\u003e1\u003c/sub\u003e. Male and CCRT were independent risk factor of grade 2+ RP. Our findings can aid in RP risk prediction and radiotherapy planning.\u003c/p\u003e","manuscriptTitle":"The risk of symptomatic radiation pneumonitis in small cell lung cancer patients following sequential immune-chemotherapy and radiotherapy: a multicenter retrospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-13 05:47:13","doi":"10.21203/rs.3.rs-7114664/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-14T14:40:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-14T22:31:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"339226283491659240632558208776665552377","date":"2025-09-03T15:23:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"90678962446416354286313782502724329134","date":"2025-08-07T17:14:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-07T09:51:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-14T05:25:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-14T02:17:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Radiation Oncology","date":"2025-07-13T16:57:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"radiation-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"raon","sideBox":"Learn more about [Radiation Oncology](http://ro-journal.biomedcentral.com/)","snPcode":"13014","submissionUrl":"https://submission.nature.com/new-submission/13014/3","title":"Radiation Oncology","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aab43f90-0082-4654-875e-03b5a845df68","owner":[],"postedDate":"August 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T16:00:24+00:00","versionOfRecord":{"articleIdentity":"rs-7114664","link":"https://doi.org/10.1186/s13014-025-02774-w","journal":{"identity":"radiation-oncology","isVorOnly":false,"title":"Radiation Oncology"},"publishedOn":"2025-12-05 15:57:22","publishedOnDateReadable":"December 5th, 2025"},"versionCreatedAt":"2025-08-13 05:47:13","video":"","vorDoi":"10.1186/s13014-025-02774-w","vorDoiUrl":"https://doi.org/10.1186/s13014-025-02774-w","workflowStages":[]},"version":"v1","identity":"rs-7114664","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7114664","identity":"rs-7114664","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}