Integrating Multimodal Data for Precise Subtyping and Prognostication in Pulmonary Mucinous Adenocarcinoma

Integrating Multimodal Data for Precise Subtyping and Prognostication in Pulmonary Mucinous Adenocarcinoma | 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 Integrating Multimodal Data for Precise Subtyping and Prognostication in Pulmonary Mucinous Adenocarcinoma Weiwei Yang, Chen Chen, Lue Li, Zehao Chen, Ziyi Chen, Hanbo Le, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7827699/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Pulmonary mucinous adenocarcinoma (PMA) represents a rare lung adenocarcinoma subtype, characterized by a lacks of comprehensive pathological classification and prognostic factors. In this study, we introduce a multimodal machine learning framework aimed at improving the accuracy of PMA subtyping and predicting the prognosis of PMA patients. Materials and methods This retrospective study enrolled 175 surgically resected primary PMA cases and demographic, histopathological, CT imaging, and genomic data of patients were collected. LASSO regularized logistic regression model were utilized for histological classification, and Cox proportional hazards model were employed for survival prediction, with internal validaition. Results Pure mucinous adenocarcinoma presented a higher prevalence of smaller tumor size, lower lobe localization, absence of lymph node metastasis, STAS, early pathological stage, CEA ≤ 5ng/mL, and EGFR E19 mutation ( P < 0.001, < 0.001, < 0.001, = 0.001, < 0.001, 0.002, 0.001, respectively) compared to mix mucinous or mucin secretion adenocarcinoma. A machine learning-derived nomogram achieved discriminative accuracy (training AUC = 0.810; validation AUC = 0.785) with excellent calibration. Multivariate Cox modeling identified higher CEA levels, indistinct margin, and EGFR E19 mutation as independent prognostic factors in PMA ( P = 0.043, 0.014, 0.044, respectively). Moreover, Kaplan-Meier curve revealed significantly different outcomes between low and high risk groups stratified upon the nomogram score ( P < 0.0001). Conclusions Pulmonary pure mucinous adenocarcinoma exhibited lower malignancy compared to the mixed mucinous and mucin secretion type. The nomogram model developed and validated in this study exhibited outstanding efficacy in predicting histological subtype and survival of PMA, offering valuable .guidance for clinicians in diagnosis and treatment decision-making. Machine learning nomogram histological characteristics prognosis pulmonary mucinous adenocarcinoma Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Novelty and Impact Pulmonary pure mucinous adenocarcinoma exhibited lower malignancy compared to the mixed mucinous or mucin secretion type. The developed nomogram model demonstrated exceptional efficacy in predicting histological subtype of PMA, while the prognostic nomagram model effectively predicted PMAsurvival. This study offers insights into the potential pathological classification of PMA, highlighting the pure mucinous type as distinct entity with reduced malignancy. Additionally, this study introduces a practical diagnostic and prognosis predictive tool for PMA and identifies independent prognostic factors for the disease. Background Lung cancer poses a significant global health burden, being the most prevalent cancer and the primary cause of cancer-related mortality worldwide[ 1 , 2 ]. Pulmonary mucinous adenocarcinoma (PMA), a rare subtype comprising less than 1% of non-small cell lung carcinomas (NSCLC), is characterized by the presence of malignant epithelial cells with extracellular mucin pools. Analysis of 2018 U.S. cancer registry data at the population level revealed that PMA constinuted 0.15% of lung cancer diagnoses, underscoring its epidemiological rarity[ 3 ]. PMA typically presents with nonspecific clinical symptoms that can overlap with those of inflammatory and metastatic conditions, leading to diagnostic complexities. While population- based studies have indicated similar survival outcomes between PMA and non-mucinous adenocarcinomas, emerging data suggest potential variations in treatment responses and recurrence patterns, highlighting the need for further molecular stratification[ 4 , 5 ]. Specifically, the three-dimensional distribution patterns of acinar/papillary components and mucin deposition density are associated with the risk of lymph vascular invasion and chemotherapy response rates in PMA. These findings highlight the importance of incoporating assessments of tumor heterogeneity into PMA prognostic frameworks and therapeutic decision-making algorithms[ 6 ]. Furthermore, different pathological variants of PMA may exhibit distinct association with various clinical characteristics. Therefore, a comprehensive analysis of factors across the pathological subgroups of PMA is crucial for optimizing both diagnosis and treatment strategies. In 2015, the World Health Organization (WHO) established a classification system for mucinous adenocarcinoma of the lung, which includes mucinous adenocarcinoma in situ (AIS), mucinous minimally invasive adenocarcinoma (MIA), invasive mucinous adenocarcinoma (IMA), mixed invasive mucinous and non-mucinous adenocarcinoma, and colloid adenocarcinoma, albeit with a weak recommendation and low-quality evidence[ 7 , 8 ]. A detailed pathological subtype classification for PMA is currently lacking. Our previous research has delineated the pathological features of PMA based on the WHO criteria, enabling a more refined classification into pure mucinous, mixed mucinous, and mucin-secreting subtypes[ 9 ]. Moreover, the correlation between PMA subtypes and clinical characters remains uncertain, and there is a need to identify further prognostic factors for PMA patients. Therefore, investigating predictive factors for PMA is of considerable clinical significance for improving disease management.。 Recently, machine learning (ML), a subset of artificial intelligence (AI), has been employed to extract valuable information and identify elusive risk factors to enhance prediction accuracy by improving the handling of missing data and nonlinear parameters in cancer research[ 10 , 11 ]. Numerous studies have demonstrated the benefits of ML in diagnosis and predicting prognosis in various types of lung cancers: Gould et al. constructed an ML based model to enhance the accuracy of NSCLC diagnosis using routing clinical and laboratory parameters[ 12 ]; Zeng et al. developed a nomogram model for predicting survival in surgically treated limited-stage small cell lung cancer patients by incorporating demographic and clinical variables[ 10 ]; and Yang et al. Identified risk factors derived from genomic features, clinical profiles, and demographics using ML models, offering valuable insights to aid clinicians in devising personalized treatment strategies[ 13 ]. While an increasing number of studies have integrated pathology, radiology and genomics to developed a predicted model in types of lung cancer, research on prediction model for PMA remains limited. This study employs machine learning techniques to analyze 175 cases of primary PMA with the goal of exploring the relationship between PMA subtypes and clincal characteristics, identifying predictive factors for diagnosis and survival in PMA patients, and assessing the effectiveness of the predictive models. Thus, in clinical practice, specific clinical features can serve as predictive markers for determining potential pathological subtypes and estimating survival outcomes in PMA patients. Materials and methods Patients The study cohort was derived from the prospectively maintained electronic medical record (EMR) system of Zhoushan Hospital, Zhejiang Province, China. This retrospective analysis enrolled 175 patients diagnosed with PMA who underwent surgical resection at the Department of Thoracic Surgery, Zhoushan Hospital between January 2012 and December 2023. The pathological specimens were examined by two senior pathologists with expertise in lung cancer. The inclusion criteria consisted of: (1) histologically verified PMA; (2) prior surgical interventions like lobectomy, segmentectomy, or wedge resection with mediastinal lymph node staging; (3) comprehensive TNM staging data; (4) follow-up information on mortality. The study cohort consisted of 25 deaths (14.3%) and 150 survivors (85.7%). Exclusion criteria included: (1) prior treatments before surgery; (2) personal history of lung cancer; (3) coexistence of other lung cancer subtypes with PMA; (4) multiple nodules; and (5) lack of EGFR-targeted drug treatment for various reasons. 175 were ultimately included. The study was approved by the Ethics Review Committee of Zhoushan Hospital (2025052-01). with informed consent waived due to its retrospective nature. Pathological evaluation Surgically resected tissue specimens were immediately fixed in 10% neutral formalin and underwent systematic processing involving sequential 5-mm transverse sectioning for thorough histological assenssment. Full-thickness tumor sampling was performed to ensure complete histomorphological anaysis. Two board-certified pathologists conducted independent blinded evaluation of hematoxylin and eosin-stained sections. The histopathological parameters encompassed PMA subclassification: the pure mucinous type, characterized by tumors composed entirely of mucinous cells; the mixed mucinous type, defined by tumors containing > 10% mucinous cells along with other histological components (e.g., leptic, papillary, acinar, or solid patterns); and the mucin secretion type, refering to tumors with > 10% extracellular mucin deposition but devoid of identifiable mucinous cells[ 9 ], as shown in Fig. 1 . CT examination and assessment High-resolution CT (HRCT) scan was conducted preoperational by using a 16-row multislice CT scanner with the lung window settings of follows: level = -500/width = 1500 HU, and were independently interpreted by two board-certified radiologists. The CT characters of pulmonary lesions evaluated comprised lobulation, spiculated edges, vacuole signs, pleural traction, alveolar collapse, margin clarity, mixed density, air bronchogram, and vascular convergence signs. The CT features specific to PMA subtypes were detailed in Fig. 1 . Immunohistochemistry Immunohistochemical staining was performed using 3,3'-diaminobenzidine chromogen staining. Two independent pathologists conducted blinded semiquantitative assessments using light microscopy (Olympus BX53) recording both the intensity and spatial distribution patterns of immunoreactivity. Cases shoing weak or negative staining were categorized as low expression, while those displaying strong staining were categorized as high expression.The antibody panel included established pulmonary adenocarcinoma markers: P63 (clone BC4A4), CK7 (OV-TL 12/30), Ki-67 (MIB-1), Napsin A (TMU-Ad02), and TTF-1 (8G7G3/1). EGFR mutations Paraffin-embedded tissues were employed for the detection of epidermal growth factor receptor ( EGFR ) mutations in histologically confirmed patients. Nucleic acid was extracted from 2–5 sections by using QIAamp DNA FFPE tissue Kit(Qiagen), followed by spectrophotometric quantification of DNA concentration and purity (A260/A280 ratio ≥ 1.8) using the Quawell Q3000 spectrophotometer (Quawell Technology). EGFR mutations were detected on ABI 7500 Real-Time PCR system (ABI) utilizing a commercial kit based on the amplification refractory mutation system (Yuanqi Diagnostics, Shanghai, China), following the manufacturer’s instructions. Follow-up Longitudinal outcome data were collected through semi-annual structured telephone interviews, standardized clinical assessments, or the regional population death registry maintained by Zhoushan Municipal Center for Disease Control and Prevention (CDC). The observation window spanned from the date of curative resection to either mortality events or the predefined study termination point (September 30, 2024), with overall survival (OS) serving as the primary outcome measure. The cohort demonstrated exceptional data integrity, achieving 100% follow-up completeness without any loss to follow-up. Statistical analysis Statistical analyses were performed using SPSS 29.0 (IBM Corp., Armonk, NY) and R 4.4.1 (R Foundation). Machine learning implementation involved LASSO regression (glmnet package) for feature selection of pathological subtypes, followed by predictive model construction. Discriminative ability was assessed using time-dependent ROC analysis (time ROC package) with area under the curve(AUC) calculations at 3- and 5-year postoperative intervals. Model calibration was verified using bootstrap-corrected calibration curves (rms package), while clinical utility was assessed via decision curve analysis(DCA) (dca.r package). Survival curves were generated using Kaplan-Meier methodology with log-rank testing. Prognostic factor identification comprised a two-stage process: initial screening using univariate Cox regression ( P < 0.05 threshold), followed by multivariate Cox proportional hazards modeling of selected variables. The final predictors were integrated into nomograms (rms package) with 1000-resample internal validation. All analyses adhered to two-tailed testing principles, with statistical significance defined as P 60 years), sex (male/female), and smoking history (Never and/Current). Detailed records were maintained regarding pathological features, lymph node metastasis, and designated surgical locations. Histopathological assessment focused on quantitatively evaluating predominant architectural patterns, such as papillary, micropapillary, acinar, and solid subtypes, with proportional quantification of each component presented as a percentage of total tumor area. CT imaging features included tumor lobulation, spiculation, vacuolation, pleural traction, alveolar collapse, margin clarity, mixed density, air bronchogram sign, and vascular convergence sign. Immunohistochemical profiling encompassed the expression levels of molecules such as P63, CK7, Ki-67, Napsin A, and TTF-1. Genetic mutation characteristics involved alterations in exons EGFR E19 , and EGFR E21 of EGFR . Additionally, data about circulating serum carcinoembryonic antigen (CEA) levels and spread through air paces (STAS) were gathered (Table 1 ). Table 1 Clinicopathological and CT characters between pulmonary mucinous adenocarcinoma with pure mucinous, mixed mucinous and mucin secretion Characteristics N (%) Pure mucinous Mixed mucinous Mucin secretion P -value Age ≤ 60 61 (34.8%) 37(32.7%) 11(30.6%) 13(50.0%) 0.230 > 60 114 (65.2%) 76(67.3%) 25(69.4%) 13(50.0%) Sex Male 70(40.0%) 40(35.4%) 25(69.4%) 13(50.0%) 0.243 Female 105(60.0%) 73(64.6%) 11(30.6%) 13(50.0%) Smoking status Never and 141(80.6%) 92(81.4%) 31(86.1%) 18(69.2%) 0.219 Current 34(19.4%) 21(18.6%) 5(13.9%) 8(30.8%) Tumor size ≤ 2.0cm 125(71.4%) 90(79.6%) 22(61.1%) 13(50.0%) 0.003 ab > 2.0cm 50(18.6%) 23(20.4%) 14(38.9%) 13(50.0%) Lymph node metastasis No 154(88.0%) 109(96.5%) 28(77.8%) 17(65.4%) < 0.001 ab* Yes 21(12.0%) 4(3.5%) 8(22.2%) 9(34.6%) STAS No 160(91.4%) 110(97.3%) 29(80.6%) 21(80.8%) 0.001 ab* Yes 15(8.6%) 3(2.7%) 7(19.4%) 5(19.2%) Tumor location Upper and middle lobe 80(45.7%) 39(34.5%) 25(69.4%) 16(61.5%) < 0.001 ab* Lower lobe 95(54.3%) 74(65.5%) 11(30.6%) 10(38.5%) Pathological stage Ⅰ 144(82.3%) 103(89.4%) 24(66.7%) 17(65.4%) 5ng/mL 25(18.6%) 8(7.1%) 10(27.8%) 7(26.9%) CT characteristics Margin Clear 126(72.0%) 81(71.7%) 24(66.7%) 21(80.8%) 0.499 Indistinct 49(28.0%) 32(28.3%) 12(33.3%) 5(19.2%) Mixed density No 115(65.7%) 68(60.2%) 27(75.0%) 20(76.9%) 0.116 Yes 60(34.3%) 45(39.2%) 9(25.0%) 6(23.1%) Vacuole sign No 144(82.3%) 90(79.6%) 29(80.6%) 25(96.2%) 0.174 Yes 31(17.7%) 23(20.4%) 7(19.4%) 1(3.8%) Plural traction No 147(84.0%) 100(88.5%) 26(72.2%) 21(80.8%) 0.056 a Yes 28(16.0%) 13(11.5%) 10(27.8%) 5(19.2%) Lobulation No 109(62.3%) 75(66.4%) 18(50.0%) 16(61.5%) 0.210 Yes 66(37.7%) 38(33.6%) 18(50.0%) 10(38.5%) Air bronchogram No 172(98.3%) 111(98.2%) 35(97.2%) 26(100%) 1.000 Yes 3(1.7%) 2(1.8%) 1(2.8%) 0(0%) Spiculated margin No 111(63.4%) 76(67.3%) 16(44.4%) 19(73.1%) 0.026 ac* Yes 64(36.6%) 37(32.7%) 20(55.6%) 7(26.9%) Immunohistochemistry(n = 125) p63(n = 125) Low expression 99(79.2%) 72(80.9%) 14(70.0%) 13(81.3%) 0.294 High expression 26(20.8%) 17(19.1%) 6(30.0%) 3(18.7%) CK7(n = 125) Low expression 28(22.4%) 18(20.2%) 7(35.0%) 3(18.7%) 0.201 High expression 97(77.6%) 71(79.8%) 13(65.0%) 13(81.3%) Ki-67(n = 125) Low expression 20(19.0%) 18(20.2%) 1(5.0%) 1(6.3%) 0.092 High expression 105(81.0%) 71(79.8%) 19(95.0%) 15(93.7%) Napsin A(n = 125) Low expression 90(72.0%) 65(73.0%) 13(65.0%) 12(75.0%) 0.716 High expression 35(28.0%) 24(27.0%) 7(35.0%) 4(25.0%) TTF-1(n = 125) Low expression 40(32.0%) 32(36.0%) 5(25.0%) 3(18.7%) 0.169 High expression 85(68.0%) 57(64.0%) 15(75.0%) 13(81.3%) EGFR E19 mutation(n = 125) No 119(95.2%) 88(98.9%) 19(95.0%) 12(75.0%) 0.001 b* Yes 6(4.8%) 1(1.1%) 1(5.0%) 4(25.0%) EGFR E21 mutation(n = 125) No 122(97.6%) 88(98.9%) 19(95.0%) 15(93.7%) 0.210 Yes 3(2.4%) 1(1.1%) 1(5.0%) 1(6.3%) a: pure mucinous vs . mixed mucinous, b: pure mucinous vs . mucin secretion, c: mixed mucinous vs . mucin secretion had significant difference, * P < 0.05. Abbreviations:STAS, spread through air spaces; CEA, carcinoembryonic Antigen; CT: computed tomography; CK7, cytokeratin 7; TTF-1, thyroid transcription factor-1; EGFR, epidermal growth factor receptor. Among the 175 patients diagnosed with PMA, 113 (64.6%) cases were classified as pure mucinos type, 36 (20.6%) as mixed mucinous type, and 26 (14.8%) cases with mucin secretion type. Immunohistochemistry expression profiles did not exhibit significant variations among different subtypes. Nevertheless, pure mucinous adenocarcinoma was notably associated with smaller tumor size, lower lobe localization, lower rates of lymph node metastasis and STAS, early pathological stage, CEA ≤ 5ng/mL, and less EGFR E19 mutations ( P < 0.001, < 0.001, < 0.001, = 0.001, < 0.001, 0.002, 0.001, respectively, Table 1 ). In the CT imaging analysis, pure mucinous type demonstrated a significantly higher prevalence of pleural traction and spiculated margins compared to mixed mucinous type ( P = 0.022, 0.013, respectively). However, no statistically significant differences were observed between pure mucinous type and mucin secretion type for these two imaging biomarkers ( P = 0.224, 0.565, respectively). Additionally, comparative analysis revealed no statistically significant difference between mixed mucinous and mucin secretion subetypes in tumor size, tumor localization, lymph node metastasis, STAS, pathological stage, and CEA level ( P all > 0.05, Table 1 ). Considering the distinct clinicopathological characteristics observed in pure mucinous type compared to mixed mucinous type and mucin secretion type, our analysis focused on establishing differential criteria to distinguish pure mucinous lesions from these two variant forms, and the results presented significant difference in tumor size, lymph node metastasis, STAS, tumor location, pathological stage, serum CEA level, mixed density, plural traction, and EGFR E19 mutation ( P = 0.001, < 0.001, 0.001, < 0.001, < 0.001, 0.001, 0.039, 0.032, 0.017, respectively, Table 2 ). Multivariate logistic analysis revealed that tumor size ( HR = 3.051, 95%CI = 1.034–8.999, P = 0.043), lymphnode metastasis ( HR = 7.137, 95%CI = 1.123–45.334, P = 0.037), STAS ( HR = 15.098, 95%CI = 2.961–76.987, P = 0.001), and tumor location ( HR = 0.220, 95%CI = 0.096–0.506, P < 0.001) were the independent factors for pure mucinous adenocarcinoma (Table 2 ). Table 2 Univariate and multivariate Logistic regression analysis for pure mucinous adenocarcinoma Characteristic Group Univariate analysis Multivariate analysis OR 95%CI P OR 95%CI P Age ≤ 60 vs. >60 0.771 0.405–1.469 0.429 Sex Female vs. Male 1.711 0.911–3.212 0.095 Smoking status Never vs. Current 1.162 0.536–2.520 0.703 Tumor size ≤ 2.0cm vs. >2.0cm 3.019 1.530–5.955 0.001 * 2.175 0.811–5.834 0.123 lymph node metastasis No vs. Yes 10.294 3.282–32.292 < 0.001 * 7.137 1.123–45.334 0.037 * STAS No vs. Yes 8.800 2.378–32.568 0.001 * 15.098 2.961–76.987 0.001 * Tumor location Upper and middle vs. Lower lobe 0.270 0.140–0.511 < 0.001 * 0.220 0.096–0.506 < 0.001 * Pathological stage Ⅰ vs. Ⅱand Ⅲ 5.276 2.288–12.165 5 ng/mL 4.958 1.996–12.318 0.001 * 1.521 0.381–6.075 0.553 Margin Clear vs. Indistinct 0.956 0.479–1.910 0.899 Mixed density No vs. Yes 0.482 0.241–0.964 0.039 * 0.391 0.151–1.008 0.052 Vacuole sign No vs. Yes 0.580 0.242–1.387 0.221 Plural traction No vs. Yes 2.455 1.082–5.572 0.032 * 1.732 0.647–4.636 0.274 Lobulation No vs. Yes 1.625 0.862–3.065 0.133 Air bronchogram No vs. Yes 0.910 0.081–10.239 0.939 Spiculated margin No vs. Yes 1.585 0.838–2.998 0.157 P63(n = 125) Low vs. High expression 0.708 0.282–1.779 0.463 CK7(n = 125) Low vs. High expression 1.517 0.620–3.709 0.361 Ki-67(n = 125) Low vs. High expression 0.232 0.051–1.058 0.059 Napsin A(n = 125) Low vs. High expression 0.839 0.359–1.963 0.686 TTF-1(n = 125) Low vs. High expression 0.509 0.208–1.248 0.140 EGFR E19 mutation(n = 125) No vs. Yes 14.194 1.595-126.279 0.017 * 14.703 2.566–84.233 0.003 * EGFR E21 mutation(n = 125) No vs. Yes 0.193 0.017–2.201 0.185 * P < 0.05. Abbreviations: STAS, spread through air spaces; CEA, carcinoembryonic Antigen; CT: computed tomography; CK7, cytokeratin 7; TTF-1, thyroid transcription factor-1; EGFR , epidermal growth factor receptor. Table 3 Univariate and multivariate Cox regression analysis of postoperative survival in PMA Characteristic Univariate analysis Multivariate analysis HR 95%CI P HR 95%CI P Age ≤ 60 vs. >60 0.601 0.274–1.319 0.204 Sex Female vs. Male 1.301 0.462–2.302 0.941 Smoking status Never vs. Current 1.149 0.425–3.052 0.796 Tumor size ≤ 2.0cm vs. >2.0cm 3.501 1.588–7.721 0.002 * 1.036 0.333–3.231 0.951 lymph node metastasis No vs. Yes 5.579 2.530–12.30 < 0.001 * 1.510 0.387–5.890 0.553 STAS No vs. Yes 2.642 0.784–8.913 0.117 Tumor location Upper and middle vs. Lower lobe 0.542 0.243–1.211 0.135 Histological type Pure mucinous vs. Mixed mucinous and mucin secretion 3.053 1.346–6.926 0.008 * 1.394 0.511–3.804 0.517 Pathological stage Ⅰ vs. Ⅱand Ⅲ 5.748 2.607–12.67 5 ng/mL 6.633 2.918–15.08 < 0.001 * 3.051 1.034–8.999 0.043 * Margin Clear vs. Indistinct 3.282 1.480–7.276 0.003 * 2.828 1.238–6.458 0.014 * Mixed density No vs. Yes 1.016 0.448–2.301 0.970 Vacuole sign No vs. Yes 0.224 0.030–1.655 0.143 Plural traction No vs. Yes 1.380 0.514-3.700 0.522 Lobulation No vs. Yes 1.108 0.502–2.443 0.799 Air bronchogram No vs. Yes 1.944 0.258–14.62 0.518 Spiculated margin No vs. Yes 1.683 0.761–3.718 0.198 P63(n = 125) Low vs. High expression 1.009 0.283–3.593 0.989 CK7(n = 125) Low vs. High expression 1.209 0.324–4.507 0.778 Ki-67(n = 125) Low vs. High expression 1.210 0.270–5.404 0.804 Napsin A(n = 125) Low vs. High expression 0.302 0.039–2.340 0.252 TTF-1(n = 125) Low vs. High expression 0.759 0.275–2.097 0.595 EGFR E19 mutation(n = 125) No vs. Yes 4.549 1.266–16.35 0.020 * 3.994 0.686–2.017 0.044 * EGFR E21 mutation(n = 125) No vs. Yes 1.887 0.246–14.46 0.541 * P < 0.05. Abbreviations: STAS, spread through air spaces; CEA, carcinoembryonic Antigen; CT: computed tomography; CK7, cytokeratin 7; TTF-1, thyroid transcription factor-1; EGFR , epidermal growth factor receptor. LASSO regression was adopted to identify parameters linked to different subtype of PMA (Fig. 2 A-B), followed by the development of a nomogram based on the four identified parameters (Fig. 2 C). The training set yielded an AUC of 0.810 (Fig. 2 E), while the validation set produced an AUC of 0.785 (Fig. 2 E), indicating the strong performance of the predictive model. The calibration plot demonstrated generally favorable performance (MAE = 0.046), with room for potential improvement, particularly within the intermediate probability ranges (Fig. 3 A). The DCA illustrated that the nomogram model offered greater net benefits across a broad range of high-risk thresholds compared to strategies that strictly categorize individuals as high or low risk. Notably, the nomogram curve exhibited a sharp decline around a threshold of 0.8, implying an increase in the model's false-negative or false-positive rate in this range, leading to reduced net benefit (Fig. 3 B). Analysis of prognostic factors and predictive modeling for PMA Univariate Cox proportional hazards analysis revealed larger tumor size, lymph node metastases, mixed mucinous and mucin secretion type, advanced pathological stage, higher CEA levels, indistinct margin, and EGFR E19 mutation were distinctly correlated with prognosis ( P = 0.002, < 0.001, = 0.008, < 0.001, < 0.001, 0.003, 0.020, respectively, Table 2 ). Subsequently, variables showing univariate significance were included in the multivariate Cox proportional hazards regression. Higher CEA levels ( HR = 3.051, 95%CI = 1.034–8.999, P = 0.043), indistinct margin ( HR = 2.828, 95%CI = 1.238–6.458, P = 0.014), and EGFR E19 mutation ( HR = 3.994, 95%CI = 0.686–2.017, P = 0.044), were identified as independent prognostic factors in PMA (Table 2 ). LASSO regression analysis identified three parameters and tumor size, lymph node metastases, histological type and stage associated with postoperative survival in PMA (Fig. 4 A-B). Using these predictive criteria, a nomogram was developed to predict the 3-year and 5-year overall survival (Fig. 4 C). In the training set (n = 122, d = 108, p = 7, B = 100, 30 subjects per group), the AUC of the ROC curve for predicting of 3-year and 5-year overall survival were 0.630 and 0.601, respectively (Fig. 4 E). For the internal validation set (n = 53, d = 11, p = 7, B = 100, 20 subjects per group), the corresponding AUC values were 0.587 and 0.543 respectively (Fig. 4 F). The calibration curves for the 3-year and 5-year overall survival in the training set suggested that the model exhibited strong calibration with a C-index of 0.827 (95% CI: 0.751–0.954) and 0.782 (95%CI: 0.691–0.863), respectively (Fig. 5 A). However, due to the limited sample size and fewer occurrences in the internal validation set, there may be increased variability in the calibration assessment with C-index of 0.605 (95%CI: 0.446–0.758) and 0.628 (95%CI: 0.492–0.761) for 3-year and 5-year overall survival (Fig. 5 B). By calculating the nomogram-derived risk scores (range: 0-375 points) for each patient, we determined the statistically optimal cutoff value through survival-based cutoff point analysis. A threshold of 34 points was established to stratify patients into different prognostic groups: individuals with scores ≤ 34 points formed the low-risk group (n = 119), while those with scores > 34 points constituted the high-risk group (n = 56). Kaplan-Meier survival analysis revealed significantly distinct clinical outcomes between the two risk groups, as evidenced by a remarkable separation in the overall survival curves ( P < 0.0001, Fig. 5 C). Moreover, although histological subtype of PMA was not identified as an independent factor for the outcome of patients with PMA, Kaplan-Meier curve displayed that cases with pure mucinous type had longer overall survival time than those with mixed mucinous type or mucin secretion type ( P = 0.032, 0.013, respectively). Discussion PMA represents a rare subtype of lung adenocarcinoma, characterized by its low incidence rate yet sharing a similar epidemiology profile with other lung adenocarcinoma subtypes[ 14 ]. Noteworthy distinctions in clinical, pathological, genetic and CT characteristics set PMA apart from non-mucinous lung adenocarcinomas[ 15 – 17 ]. Notably, our previous research identified heterogeneous PMA subtypes based on variations in mucinous cellularity and extracellular mucinous matrix composition within the tumor microenvironments[ 9 ]. Despite these advancements, inconsistent survival comes persist, likely due to limited samples sizes and diverse analytical approaches. A key challenge lies in integrating multifaceted data including clincopathological, genetic and CT imaging features, to accurately predict prognosis. Machine learning-based nomogram models offer a promising solution by leveraging complex, high-dimensional data to capture nonlinear relationships and improve predictive precision. However, the systematic application of nomogram models to assess histological subtype and survival outcomes in PMA, remains scarce, highlighting an understudied area. Addressing this gap could refine risk stratification and guide personalized therapeutic interventions for this rare yet clinically complex malignancy. Our study reaffirmed previous observations that PMA is predominantly associated with the lower lobe of the lung, T1 stage, N0 stage, and pathological stage I[ 18 ]. Notably, we found that the pure mucinous type of PMA exhibited less aggressive compared to mixed mucinous and mucin secretion types characterized by smaller tumor size, early pathological stage, CEA level ≤ 5ng/mL, lower percentage of lymph node metastasis and STAS, and EGFR E19 mutation. Hwang et al. classified the malignant degree of PMA based on the extent of lepidic and non-lepidic invasive patterns, noting that invasive patterns were least common in small tumors and progressively increased in larger tumors, suggesting a correlation between histological subtype of PMA and tumor progression.[ 19 ]. Moreover, consistent with prior reports, EGFR mutation rates in PMA vary significantly across populations (0-33.3%) due to racial and technical disparities[ 20 – 22 ]. A subsequent investigation revealed EGFR mutations in 3 out of 41 metastatic PMA cases, which showed favorable clinical responses to EGFR-targeted therapy [ 23 ]. Our results indicated EGFR E19 and EGFR E21 mutation accounted for 4.8% and 2.4%, respectively. Crucially, mucin secretion type exhibited a higher frequency of EGFR mutation compared to pure mucinous type, consistent with another Chinese cohort[ 22 ]. Additionally, in CT imaging features, the pure mucinous type showed a notably higher prevalence of pleural traction and spiculated margins compared to mixed mucinous type, while being similar to mucin secretion type. Compared to non-mucinous adenocarcinoma, mucinous adenocarcinoma were more likely to exhibit indistinct margins and pleural retraction[ 24 , 25 ], and the spiculated margins being more common in pure mucinous adenocarcinoma than in mixed mucinous adenocarcinoma[ 17 ]. Significantly, we have pioneered the development and validation of a predictive model for distinguishing pure PMA utilizing logistic and LASSO regression analyses within a machine learning framework. The model demonstrated robust performance with relatively high AUC values in both the training and validation sets, along with well-calibrated curves. This predictive model offers a promising approach for accurately identifying pure mucinous adenocarcinoma, providing a valueble tool for facilitating more precise therapeutic interventions for patients. To enhance the accuracy of prognostic modeling for PMA, we initially employed Cox regression analysis to identify prognostic factors of PMA, identifying the indistinct margin, higher CEA level and EGFR E19 mutation as independent prognostic factors for PMA patients. CEA had been established as a clinically significant tumor biomarker known to facilitate malignant progression in lung cancer[ 26 ]. Our previous cohort study revealed a correlation between elevated serum CEA level and adverse clinical outcomes[ 27 ]. Additionally, our cohort identified indistinct margin as an independent prognostic factor in PMA outcomes. Notably, downstream genes activation by EGFR mutants has been linked to anti-apoptosis, pyroptosis, metastasis, and immune evasion[ 28 – 30 ]. While specific subtypes of EGFR mutations are rarely used as distinguishing factors in prognostic assessments for PMA[ 22 ], our previous findings indicated that EGFR E19 was a unfavourable prognostic marker in lung adenocarcinoma patients, with worse outcomes compared to EGFR E21 mutation cases[ 27 ]. Moreover, Deng et al. demonstrated EGFR mutation was a poor prognostic factor in PMA and advanced stage lung adenocarcinoma[ 31 ], which was correlated with our study. LASSO regression technique from machine learning was utilized to incorporate variables in the development of a nomogram. The nomogram model, along wtih time-dependent ROC curves revealed moderately high AUC values for the 3-year and 5-year survival in both training and validation sets, while the calibration curves demonstrated strong consistency. The relatively lower AUC values of the nomogram may stem from the single-center data collection approach, potentially limiting the generalizability of the results. Despite not achieving high AUC and C-index, the survival curve displayed significant differentiation between the two stratified groups, indicating the effectively discrimination ability of the nomogram and its potential utility in clinical prediction. Consistent with prior studies that have also developed satisfactory nomogram model despite moderately high C-index values[ 10 ], our findings underscore the important of histopathological classification in PMA by demonstrating that pure mucinous adenocarcinoma exhibits significantly superior clinical outcome compared to other PMA subtype, validating the prognostic relevance of histopathological classification in PMA. This study exhibits notable strengths and limitations. Initially, the data collection was confined to a single center, potentially introducing bias, and further multicenter cohort studies are warranted to enhance the dataset's breadth and credibility. Secondly, the restricted patient population with PMA and the frequency of fatalities might result in data bias when partitioning patients into training and validation sets. The limited sample size and reduced event rate could lead to increased variability in calibration assessments. Thirdly, due to constrains in laboratory infrastructure in the archipelago setting, KRAS mutations were not identified in the patient cohort. Consequently, expanding the sample size, refining grouping strategies, or employing avanced corrective methods is recommended. Furthermore, it is crucial to validate the model's performance across multiple datasets to ensure its generalizability. Furthermore, this study employed two distinct machine learning regression methodologies. In clinical practice, the implementation of binary classification analysis through machine learning provides a robust framework for predicting binary outcomes, such as distinguishing between disease subtypes or estimating the likelihood of positive outcomes within a defined period. Similarly, machine learning-based prognostic analysis can effectively forecast patient survival under varying conditions. This investigative process has the potential to unveil previously unidentified yet clinically significant insights. In conclusion, we have established a refined histopathological classification approach for PMA with our validation study revealing distinctive clinicopathological characteristics indicating lower aggressiveness in pure mucinous adenocarcinoma compared to mixed mucinous and mucin secretion types. Moreover, our developed nomogram model exhibited exceptional performance in predicting histological subtype of PMA. Importantly, higher CEA level, indistinct margin, and EGFR E19 mutation were confirmed as independent prognostic factors for PMA. These findings offer valuable insights for histopathological diagnosis and prognostic evaluation in the context of PMA. Abbreviations PMA pulmonary mucinous adenocarcinoma LASSO least absolute shrinkage and selection operator NSCLC non-small cell lung carcinomas WHO World Health Organization AIS adenocarcinoma in situ MIA minimally invasive adenocarcinoma,IAC,invasive adenocarcinoma ML machine learning AI artificial intelligence EMR electronic medical record HRCT high-resolution CT OS overall survival AUC area under the curv DCA decision curve analysis EGF epidermal growth factor receptor CEA carcinoembryonic antige STAS spread through air spaces. Declarations Acknowledgments We thank the anonymous reviewers for reviewing the manuscript and providing valuable comments. Author Contribution (I) Conception and design: Wangyu Zhu; (II) Administrative support: Hanbo Le and Yongkui Zhang; (III) Provision of study materials or patients: Weiwei Yang, Chen Chen, Zehao Chen, and Ziyi Chen; (IV) Collection and assembly of data: Weiwei Yang, Chen Chen, and Lue Li; (V) Data analysis and interpretation: Wangyu Zhu, Weiwei Yang; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. Funding This study was supported in part by grants from the Health Commission of Zhejiang Province, and National Traditional Chinese Medicine Comprehensive Reform Demonstration Zone (grant no. 2022RC292 and GZY-KJS-ZJ-2025-091 to Wang-yu Zhu), the Scientific Research Program of Traditional Chinese Medicine of Zhejiang Province (grant no. 2021ZB349 to Lue Li), and the Science and Technology Program of Zhoushan (grant no. 2023C31001 to Han-bo Le). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data availability statement The datasets generated and/or analyzed during the current study can be obtained under reasonable conditions by contacting Dr. Wangyu Zhu (email: [email protected] ). Ethics approval and consent to participate This retrospective cohort study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Zhoushan Hospital (Approval No.: Zhoushan Hospital Ethical Review 2025-052-01). The requirement for informed consent was formally waived by the ethics committee.Individual patient contact was impracticable given the study's historical design. All personal identifiers (including names, ID numbers, and admission dates) were removed prior to analysis using a double-masking protocol. The anonymization process was independently verified by the hospital's data security officer. Consent for publication All authors gave consent for the publication of this study. Conflict of interest No potential conflict of interest was reported by theauthor(s). References Siegel R L, Giaquinto A N and Jemal A. Cancer statistics, 2024 . CA Cancer J Clin 2024; 74 :12-49. Li C, Lei S, Ding L, et al. Global burden and trends of lung cancer incidence and mortality . Chin Med J (Engl) , 2023; 136 :1583-90. Moon S W, Choi S Y and Moon M H. Effect of invasive mucinous adenocarcinoma on lung cancer-specific survival after surgical resection: a population-based study . J Thorac Dis 2018; 10 :3595-608. Zhu D, Zhang Q, Rui Z, et al. Pulmonary invasive mucinous adenocarcinoma mimicking pulmonary actinomycosis . BMC Pulm Med 2022; 22 :181. Yin Y H, Qi Y G and Wang B. Differential diagnosis of pulmonary nodular mucinous adenocarcinoma and tuberculoma with dynamic CT: a retrospective study . J Thorac Dis 2022; 14 :1225-31. Li W, Yang Y, Yang M, et al. Clinicopathologic features and survival outcomes of primary lung mucinous adenocarcinoma based on different radiologic subtypes . Ann Surg Oncol 2024; 31 :167-77. Travis W D, Brambilla E, Noguchi M, et al. International association for the study of lung cancer/american thoracic society/european respiratory society international multidisciplinary classification of lung adenocarcinoma . J Thorac Oncol 2011; 6 :244-85. Travis W D, Brambilla E, Noguchi M, et al. International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society: international multidisciplinary classification of lung adenocarcinoma: executive summary . Proc Am Thorac Soc 2011; 8 :381-85. Pan X, Fang R, Zhang B, et al. Pathological and imaging features of pulmonary invasive mucinous adenocarcinoma-a retrospective cohort study . Transl Lung Cancer Res 2024; 13 :1376-82. Zeng Q, Li J, Tan F, et al. Development and validation of a nomogram prognostic model for resected limited-stage small cell lung cancer patients . Ann Surg Oncol 2021; 28 :4893-904. Li Y, Wu X, Yang P, et al. Machine learning for lung cancer diagnosis, treatment, and prognosis . Genomics Proteomics Bioinformatics 2022; 20 :850-66. Gould M K, Huang B Z, Tammemagi M C, et al. Machine learning for early lung cancer identification using routine clinical and laboratory data . Am J Respir Crit Care Med 2021; 204 :445-53. Yang Y, Xu L, Sun L, et al. Machine learning application in personalised lung cancer recurrence and survivability prediction . Comput Struct Biotechnol J 2022; 20 :1811-20. Xu L, Li C and Lu H. Invasive mucinous adenocarcinoma of the lung . Transl Cancer Res 2019; 8 :2924-32. Gow C H, Hsieh M S, Liu Y N, et al. Clinicopathological features and survival outcomes of primary pulmonary invasive mucinous adenocarcinoma . Cancers (Basel) 2021; 13 :4103. Xiao Z, Chen J, Feng X, et al. Use of CT-derived radiomic features to preoperatively identify invasive mucinous adenocarcinoma in solitary pulmonary nodules ≤3 cm . Heliyon 2024; 10 :e30209. Zhang J, Hao L, Xu Q, et al. CT imagingand clinical characters based Gaussian Naive Bayes (GNB) model for preoperative differentiation of pulmonary pure invasive mucinous adenocarcinoma from mixed mucinous adenocarcinoma . Technol Cancer Res Treat 2024; 23 :15330338241258415. Cui D, Xie S and Liu Q Postoperative survival of pulmonary invasive mucinous adenocarcinoma versus non-mucinous invasive adenocarcinoma . BMC Pulmonary Medicine 2023; 23 :9. Hwang S, Han J, Choi M, et al. Size of non-lepidic invasive pattern predicts recurrence in pulmonary mucinous adenocarcinoma: morphologic analysis of 188 resected cases with reappraisal of invasion criteria . J Pathol Transl Med 2017; 51 :56-68. Kim M, Hwang J, Kim K A, et al. Genomic characteristics of invasive mucinous adenocarcinoma of the lung with multiple pulmonary sites of involvement . Modern Pathologyn 2022; 35 :202-9. Xu X, Li N, Wang D, et al. Clinical relevance of PD-L1 expression and CD8+ T cells' infiltration in patients with lung invasive mucinous adenocarcinoma . Front Oncol 2021; 11 :683432. Cai L, Wang J, Yan J, et al. Genomic profiling and prognostic value analysis of genetic alterations in Chinese resected lung cancer with invasive mucinous adenocarcinoma . Front Oncol 2020; 10 :603671. Demir T, Araz M, Moloney C, et al. Efficacy of systemic treatments in patients with metastatic lung invasive mucinous adenocarcinoma . Clin Lung Cancer 2024; 25 :e316-22. Hong R, Ping X, Liu Y, et al. Combined CT-based CT imagingand clinic-radiological characteristics for preoperative differentiation of solitary-type invasive mucinous and non-mucinous lung adenocarcinoma . Int J Gen Med 2024; 17 :4267-79. Zhang J, Hao L, Li M, et al. CT CT imagingcombined with clinicopathological features to predict invasive mucinous adenocarcinoma in patients with lung adenocarcinoma . Technol Cancer Res Treat 2023; 22 :15330338231174306. Yuan J, Sun Y, Wang K, et al. Development and validation of reassigned CEA, CYFRA21-1 and NSE-based models for lung cancer diagnosis and prognosis prediction . BMC Cancer 2022; 22 :686. Zhu W Y, Li H F, Fang K X, et al. Epidermal growth factor receptor mutations and their prognostic value with carcinoembryonic antigen in pathological T1 lung adenocarcinoma . Dis Markers 2018; 2018 :2942618. Hu L Y, Zhuang W T, Chen M J, et al. EGFR oncogenic mutations in NSCLC impair macrophage phagocytosis and mediate innate immune evasion through up-regulation of CD47 . J Thorac Oncol, 2024, 19(8):1186-1200. Salama M F, Liu M, Clarke C J, et al. PKCα is required for Akt-mTORC1 activation in non-small cell lung carcinoma (NSCLC) with EGFR mutation . Oncogene, 2019, 38(48):7311-7328. Kamle S, Ma B, Schor G, et al. Chitinase 3-like-1 (CHI3L1) in the pathogenesis of epidermal growth factor receptor mutant non-small cell lung cancer . Transl Oncol, 2024, 49:102108. Deng C, Zhang Y, Ma Z, et al. Prognostic value of epidermal growth factor receptor gene mutation in resected lung adenocarcinoma . J Thorac Cardiovasc Surg 2021; 162 :664-74.e667. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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-7827699","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":529292585,"identity":"c16bdf42-7231-41fe-bdf9-4eb51893c458","order_by":0,"name":"Weiwei Yang","email":"","orcid":"","institution":"Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weiwei","middleName":"","lastName":"Yang","suffix":""},{"id":529292586,"identity":"23175309-6c5f-4251-88fc-85baf45ced94","order_by":1,"name":"Chen Chen","email":"","orcid":"","institution":"Wenzhou Medical 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12:47:18","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":167926,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7827699/v1/a297d27fda99afde595c3003.html"},{"id":93779937,"identity":"e8fc6bb2-7aea-48ee-8772-0bdd73e9ba1f","added_by":"auto","created_at":"2025-10-17 12:55:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1840153,"visible":true,"origin":"","legend":"\u003cp\u003eCT imaging and pathological categorization of pulmonary mucinous adenocarcinoma. (A) Pure mucinos type: CT imaging showed a relatively clear margin, lobulated, high density nodule measuring 13 mm in tmaximum diameter in the posterior segment of the right upper lobe, adjacent to the pleura. Histopathological examination revealed adenocarcinoma composed exclusively of mucin-producing neoplastic cells with abundant extracellular mucin deposits, devoid of non-mucinous cellular components. (B) Mixed mucinous type: CT imaging demonstrated a clear margin, spiculated appearance, pleural traction, and a mixed ground glass nodule measuring 20 mm in maximum diameter in the right upper lobe. Microscopic examination described the tumor tissue mainly characterized by acinar growth pattern, accompanied by 20% papillary growth pattern and 40% mucinous adenocarcinoma. (C) Mucin secretion type: CT imaging indicated an indistinct margin, spiculated, lobulated, solid nodule with a maximum diameter of 14 mm in the left upper lobe. Microscopic examination demonstrated the tumor tissue was predominantly characterized by papillary/acinar growth pattern with lacked mucinous cells but rich in mucin. Pathological pictures were scanned by a digital pathology slide scanner and subsequently analyzed with K-viewer software (KF-PRO-005, KFBIO).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7827699/v1/6986a6a8cf2de88641a8b0f8.png"},{"id":93778515,"identity":"5c23cb5a-15c4-4ac9-a412-83cd7b9fa445","added_by":"auto","created_at":"2025-10-17 12:47:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":277861,"visible":true,"origin":"","legend":"\u003cp\u003eClassification of pulmonary mucinous adenocarcinoma (PMA). (A) Coefficient shrinkage profile demonstrating regularization paths of 35 candidate predictors. The upper x-axis indicates cumulative non-zero coefficients, while the lower x-axis represents log-transformed penalty parameter (λ); (B) Ten-fold cross-validation with minimum criteria (dashed vertical line) determining optPMAl λ selection. The y-axis quantifies cross-validated partial likelihood deviance (mean ± SE). OptPMAl feature selection occurred at log[λ]=-3.6), The optimal number of knots for curve interception was determined to be between 4 and 10. Following variable selection, the machine learning algorithm identified and retained 4 predictors with non-zero coefficients for inclusion in the subsequent model; (C) Nomogram for predicting the pathological classification of PMA by using 6 predictors; (D) ROC curve of the training set; (E) ROC curve of the internal validation set.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7827699/v1/adc2c52531ee94a2feb73db2.png"},{"id":93778517,"identity":"669380d4-6e43-46ce-ab62-5c69aef5f468","added_by":"auto","created_at":"2025-10-17 12:47:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":99907,"visible":true,"origin":"","legend":"\u003cp\u003e(A)\u003cstrong\u003e \u003c/strong\u003eCalibration plot of pulmonary mucinous adenocarcinoma(PMA) pathological typing predicted by the Logistic regression model. (B) Decision curve analysis (DCA) employed for a model forecasting the pathological classifications of PMA. a comparison of net benefits among three strategies—the Nomogram model, assuming all patients are at high risk (All), and assuming no patients are at high risk (None)—across various high-risk thresholds.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7827699/v1/f698553460c42c33154c6d48.png"},{"id":93778518,"identity":"885f776e-0a8f-4da7-aed9-2250424399e1","added_by":"auto","created_at":"2025-10-17 12:47:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":397212,"visible":true,"origin":"","legend":"\u003cp\u003eIn the Cox regression analysis of pulmonary mucinous adenocarcinoma(PMA), the machine learning method of Least Absolute Shrinkage and Selection Operator (LASSO) regression model was used for feature selection. (A) LASSO coefficient curve for 35 features (y-axis). The numbers on the upper x-axis represent the average number of predictor variables. The x-axis below represents log(λ). (B) Select the tuning parameter (λ) for the LASSO model through 10-fold cross-validation based on the minimum criterion. The y-axis represents the partial likelihood deviance. The optimal number of knots for curve interception was determined to be between 4 and 9. Following variable selection, the machine learning algorithm identified and retained 7 predictors with non-zero coefficients for inclusion in the subsequent model.(C) Nomogram for predicting postoperative survival in PMA by using 7 predictors. Time-dependent receiver operating characteristic (ROC) curves based on nomograms for 3-year and 5-year survival rates of PMA. The Y-axis represents the True Positive Rate, the X-axis represents the False Positive Rate. (D) Training set ROC curve (E) Internal validation set ROC curve.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7827699/v1/569c02f3a9bce5b6cad2dea7.png"},{"id":93778519,"identity":"2839ee5a-6952-46b2-a001-e4a695a5fff8","added_by":"auto","created_at":"2025-10-17 12:47:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":386567,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration plots of 3-year and 5-year overall survival rates predicted by the Cox regression model. (A) 3-year overall survival rate training set; (B) 3-year overall survival rate validation set; (C) 5-year pulmonary mucinous adenocarcinoma(PMA)-specific survival rate training set; (D) 5-year PMA-specific survival rate validation set. (E) Kaplan-Meier survival curves for low-and high-risk groups of PMA. (F) Kaplan-Meier survival curves for groups of PMA with pure mucinous type, mixed mucinous type and mucin secretion type.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7827699/v1/c10e673f2c99aab2025c5e1f.png"},{"id":93781594,"identity":"58549404-ce9d-43cd-99f6-f3bd2099a1c3","added_by":"auto","created_at":"2025-10-17 13:19:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4277309,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7827699/v1/9144a85e-d182-4193-9d30-48fa62523ff0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating Multimodal Data for Precise Subtyping and Prognostication in Pulmonary Mucinous Adenocarcinoma","fulltext":[{"header":"Novelty and Impact","content":"\u003cp\u003ePulmonary pure mucinous adenocarcinoma exhibited lower malignancy compared to the mixed mucinous or mucin secretion type. The developed nomogram model demonstrated exceptional efficacy in predicting histological subtype of PMA, while the prognostic nomagram model effectively predicted PMAsurvival. This study offers insights into the potential pathological classification of PMA, highlighting the \u0026nbsp;pure mucinous type as distinct entity with reduced malignancy. Additionally, this study \u0026nbsp; introduces a practical diagnostic and prognosis predictive tool for PMA and identifies independent prognostic factors for the disease.\u003c/p\u003e"},{"header":"Background","content":"\u003cp\u003eLung cancer poses a significant global health burden, being the most prevalent cancer and the primary cause of cancer-related mortality worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Pulmonary mucinous adenocarcinoma (PMA), a rare subtype comprising less than 1% of non-small cell lung carcinomas (NSCLC), is characterized by the presence of malignant epithelial cells with extracellular mucin pools. Analysis of 2018 U.S. cancer registry data at the population level revealed that PMA constinuted 0.15% of lung cancer diagnoses, underscoring its epidemiological rarity[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. PMA typically presents with nonspecific clinical symptoms that can overlap with those of inflammatory and metastatic conditions, leading to diagnostic complexities. While population- based studies have indicated similar survival outcomes between PMA and non-mucinous adenocarcinomas, emerging data suggest potential variations in treatment responses and recurrence patterns, highlighting the need for further molecular stratification[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSpecifically, the three-dimensional distribution patterns of acinar/papillary components and mucin deposition density are associated with the risk of lymph vascular invasion and chemotherapy response rates in PMA. These findings highlight the importance of incoporating assessments of tumor heterogeneity into PMA prognostic frameworks and therapeutic decision-making algorithms[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, different pathological variants of PMA may exhibit distinct association with various clinical characteristics. Therefore, a comprehensive analysis of factors across the pathological subgroups of PMA is crucial for optimizing both diagnosis and treatment strategies.\u003c/p\u003e\u003cp\u003eIn 2015, the World Health Organization (WHO) established a classification system for mucinous adenocarcinoma of the lung, which includes mucinous adenocarcinoma in situ (AIS), mucinous minimally invasive adenocarcinoma (MIA), invasive mucinous adenocarcinoma (IMA), mixed invasive mucinous and non-mucinous adenocarcinoma, and colloid adenocarcinoma, albeit with a weak recommendation and low-quality evidence[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A detailed pathological subtype classification for PMA is currently lacking. Our previous research has delineated the pathological features of PMA based on the WHO criteria, enabling a more refined classification into pure mucinous, mixed mucinous, and mucin-secreting subtypes[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Moreover, the correlation between PMA subtypes and clinical characters remains uncertain, and there is a need to identify further prognostic factors for PMA patients. Therefore, investigating predictive factors for PMA is of considerable clinical significance for improving disease management.。\u003c/p\u003e\u003cp\u003eRecently, machine learning (ML), a subset of artificial intelligence (AI), has been employed to extract valuable information and identify elusive risk factors to enhance prediction accuracy by improving the handling of missing data and nonlinear parameters in cancer research[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Numerous studies have demonstrated the benefits of ML in diagnosis and predicting prognosis in various types of lung cancers: Gould et al. constructed an ML based model to enhance the accuracy of NSCLC diagnosis using routing clinical and laboratory parameters[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]; Zeng et al. developed a nomogram model for predicting survival in surgically treated limited-stage small cell lung cancer patients by incorporating demographic and clinical variables[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]; and Yang et al. Identified risk factors derived from genomic features, clinical profiles, and demographics using ML models, offering valuable insights to aid clinicians in devising personalized treatment strategies[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile an increasing number of studies have integrated pathology, radiology and genomics to developed a predicted model in types of lung cancer, research on prediction model for PMA remains limited. This study employs machine learning techniques to analyze 175 cases of primary PMA with the goal of exploring the relationship between PMA subtypes and clincal characteristics, identifying predictive factors for diagnosis and survival in PMA patients, and assessing the effectiveness of the predictive models. Thus, in clinical practice, specific clinical features can serve as predictive markers for determining potential pathological subtypes and estimating survival outcomes in PMA patients.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePatients\u003c/h2\u003e\u003cp\u003eThe study cohort was derived from the prospectively maintained electronic medical record (EMR) system of Zhoushan Hospital, Zhejiang Province, China. This retrospective analysis enrolled 175 patients diagnosed with PMA who underwent surgical resection at the Department of Thoracic Surgery, Zhoushan Hospital between January 2012 and December 2023. The pathological specimens were examined by two senior pathologists with expertise in lung cancer. The inclusion criteria consisted of: (1) histologically verified PMA; (2) prior surgical interventions like lobectomy, segmentectomy, or wedge resection with mediastinal lymph node staging; (3) comprehensive TNM staging data; (4) follow-up information on mortality. The study cohort consisted of 25 deaths (14.3%) and 150 survivors (85.7%). Exclusion criteria included: (1) prior treatments before surgery; (2) personal history of lung cancer; (3) coexistence of other lung cancer subtypes with PMA; (4) multiple nodules; and (5) lack of EGFR-targeted drug treatment for various reasons. 175 were ultimately included. The study was approved by the Ethics Review Committee of Zhoushan Hospital (2025052-01). with informed consent waived due to its retrospective nature.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePathological evaluation\u003c/h3\u003e\n\u003cp\u003eSurgically resected tissue specimens were immediately fixed in 10% neutral formalin and underwent systematic processing involving sequential 5-mm transverse sectioning for thorough histological assenssment. Full-thickness tumor sampling was performed to ensure complete histomorphological anaysis. Two board-certified pathologists conducted independent blinded evaluation of hematoxylin and eosin-stained sections. The histopathological parameters encompassed PMA subclassification: the pure mucinous type, characterized by tumors composed entirely of mucinous cells; the mixed mucinous type, defined by tumors containing\u0026thinsp;\u0026gt;\u0026thinsp;10% mucinous cells along with other histological components (e.g., leptic, papillary, acinar, or solid patterns); and the mucin secretion type, refering to tumors with \u0026gt;\u0026thinsp;10% extracellular mucin deposition but devoid of identifiable mucinous cells[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eCT examination and assessment\u003c/h3\u003e\n\u003cp\u003eHigh-resolution CT (HRCT) scan was conducted preoperational by using a 16-row multislice CT scanner with the lung window settings of follows: level = -500/width\u0026thinsp;=\u0026thinsp;1500 HU, and were independently interpreted by two board-certified radiologists. The CT characters of pulmonary lesions evaluated comprised lobulation, spiculated edges, vacuole signs, pleural traction, alveolar collapse, margin clarity, mixed density, air bronchogram, and vascular convergence signs. The CT features specific to PMA subtypes were detailed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eImmunohistochemistry\u003c/h3\u003e\n\u003cp\u003eImmunohistochemical staining was performed using 3,3'-diaminobenzidine chromogen staining. Two independent pathologists conducted blinded semiquantitative assessments using light microscopy (Olympus BX53) recording both the intensity and spatial distribution patterns of immunoreactivity. Cases shoing weak or negative staining were categorized as low expression, while those displaying strong staining were categorized as high expression.The antibody panel included established pulmonary adenocarcinoma markers: P63 (clone BC4A4), CK7 (OV-TL 12/30), Ki-67 (MIB-1), Napsin A (TMU-Ad02), and TTF-1 (8G7G3/1).\u003c/p\u003e\n\u003cdiv class=\"Heading\"\u003eEGFR mutations\u003c/div\u003e\u003cp\u003eParaffin-embedded tissues were employed for the detection of epidermal growth factor receptor (\u003cem\u003eEGFR\u003c/em\u003e) mutations in histologically confirmed patients. Nucleic acid was extracted from 2\u0026ndash;5 sections by using QIAamp DNA FFPE tissue Kit(Qiagen), followed by spectrophotometric quantification of DNA concentration and purity (A260/A280 ratio\u0026thinsp;\u0026ge;\u0026thinsp;1.8) using the Quawell Q3000 spectrophotometer (Quawell Technology). \u003cem\u003eEGFR\u003c/em\u003e mutations were detected on ABI 7500 Real-Time PCR system (ABI) utilizing a commercial kit based on the amplification refractory mutation system (Yuanqi Diagnostics, Shanghai, China), following the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eFollow-up\u003c/h2\u003e\u003cp\u003eLongitudinal outcome data were collected through semi-annual structured telephone interviews, standardized clinical assessments, or the regional population death registry maintained by Zhoushan Municipal Center for Disease Control and Prevention (CDC). The observation window spanned from the date of curative resection to either mortality events or the predefined study termination point (September 30, 2024), with overall survival (OS) serving as the primary outcome measure. The cohort demonstrated exceptional data integrity, achieving 100% follow-up completeness without any loss to follow-up.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using SPSS 29.0 (IBM Corp., Armonk, NY) and R 4.4.1 (R Foundation). Machine learning implementation involved LASSO regression (glmnet package) for feature selection of pathological subtypes, followed by predictive model construction. Discriminative ability was assessed using time-dependent ROC analysis (time ROC package) with area under the curve(AUC) calculations at 3- and 5-year postoperative intervals. Model calibration was verified using bootstrap-corrected calibration curves (rms package), while clinical utility was assessed via decision curve analysis(DCA) (dca.r package).\u003c/p\u003e\u003cp\u003eSurvival curves were generated using Kaplan-Meier methodology with log-rank testing. Prognostic factor identification comprised a two-stage process: initial screening using univariate Cox regression (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 threshold), followed by multivariate Cox proportional hazards modeling of selected variables. The final predictors were integrated into nomograms (rms package) with 1000-resample internal validation. All analyses adhered to two-tailed testing principles, with statistical significance defined as \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCharacteristics of PMA and development and validation of a predictive model\u003c/h2\u003e\u003cp\u003eDemographic parameters consisted of age at diagnosis (categorized as \u0026le;\u0026thinsp;60/\u0026gt;60 years), sex (male/female), and smoking history (Never and/Current). Detailed records were maintained regarding pathological features, lymph node metastasis, and designated surgical locations. Histopathological assessment focused on quantitatively evaluating predominant architectural patterns, such as papillary, micropapillary, acinar, and solid subtypes, with proportional quantification of each component presented as a percentage of total tumor area. CT imaging features included tumor lobulation, spiculation, vacuolation, pleural traction, alveolar collapse, margin clarity, mixed density, air bronchogram sign, and vascular convergence sign. Immunohistochemical profiling encompassed the expression levels of molecules such as P63, CK7, Ki-67, Napsin A, and TTF-1. Genetic mutation characteristics involved alterations in exons \u003cem\u003eEGFR\u003c/em\u003e\u003csup\u003eE19\u003c/sup\u003e, and \u003cem\u003eEGFR\u003c/em\u003e\u003csup\u003eE21\u003c/sup\u003e of \u003cem\u003eEGFR\u003c/em\u003e. Additionally, data about circulating serum carcinoembryonic antigen (CEA) levels and spread through air paces (STAS) were gathered (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003eClinicopathological and CT characters between pulmonary mucinous adenocarcinoma with pure mucinous, mixed mucinous and mucin secretion\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePure mucinous\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMixed mucinous\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMucin secretion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-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\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61 (34.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37(32.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11(30.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13(50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.230\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e114 (65.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76(67.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25(69.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13(50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70(40.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40(35.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25(69.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13(50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.243\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e105(60.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73(64.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11(30.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13(50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever and\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e141(80.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e92(81.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31(86.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18(69.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.219\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34(19.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21(18.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5(13.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8(30.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le; 2.0cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e125(71.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90(79.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22(61.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13(50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.003\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;2.0cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50(18.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23(20.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14(38.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13(50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymph node metastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e154(88.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e109(96.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28(77.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17(65.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003eab*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21(12.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4(3.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8(22.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9(34.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSTAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e160(91.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e110(97.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29(80.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21(80.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003csup\u003eab*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15(8.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3(2.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7(19.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5(19.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor location\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper and middle lobe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e80(45.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39(34.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25(69.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16(61.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003eab*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLower lobe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e95(54.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e74(65.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11(30.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10(38.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathological stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eⅠ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e144(82.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e103(89.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24(66.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17(65.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003eab*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eⅡand Ⅲ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31(17.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10(10.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12(33.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9(34.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum CEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;5ng/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e150(71.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e105(92.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26(72.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19(73.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003csup\u003eab*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;5ng/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25(18.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8(7.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10(27.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7(26.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCT characteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMargin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e126(72.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81(71.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24(66.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21(80.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.499\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndistinct\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49(28.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32(28.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12(33.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5(19.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMixed density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e115(65.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68(60.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27(75.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20(76.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60(34.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45(39.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9(25.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6(23.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVacuole sign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e144(82.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90(79.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29(80.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25(96.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31(17.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23(20.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7(19.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1(3.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlural traction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e147(84.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100(88.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26(72.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21(80.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.056\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28(16.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13(11.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10(27.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5(19.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLobulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e109(62.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75(66.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18(50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16(61.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.210\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e66(37.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38(33.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18(50.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10(38.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAir bronchogram\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e172(98.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e111(98.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35(97.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26(100%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3(1.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2(1.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1(2.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0(0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpiculated margin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e111(63.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76(67.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16(44.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19(73.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.026\u003csup\u003eac*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64(36.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37(32.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20(55.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7(26.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImmunohistochemistry(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep63(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e99(79.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72(80.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14(70.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13(81.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.294\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26(20.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17(19.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6(30.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3(18.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCK7(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28(22.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18(20.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7(35.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3(18.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e97(77.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71(79.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13(65.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13(81.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKi-67(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20(19.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18(20.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1(5.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1(6.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.092\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e105(81.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71(79.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19(95.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15(93.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNapsin A(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e90(72.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65(73.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13(65.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12(75.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.716\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35(28.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24(27.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7(35.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4(25.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTTF-1(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e40(32.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32(36.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5(25.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3(18.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.169\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85(68.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57(64.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15(75.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13(81.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEGFR\u003c/em\u003e\u003csup\u003eE19\u003c/sup\u003emutation(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e119(95.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88(98.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19(95.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12(75.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003csup\u003eb*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6(4.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1(1.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1(5.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4(25.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEGFR\u003c/em\u003e\u003csup\u003eE21\u003c/sup\u003emutation(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e122(97.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88(98.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19(95.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15(93.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.210\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3(2.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1(1.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1(5.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1(6.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003ea: pure mucinous \u003cem\u003evs\u003c/em\u003e. mixed mucinous, b: pure mucinous \u003cem\u003evs\u003c/em\u003e. mucin secretion, c: mixed mucinous \u003cem\u003evs\u003c/em\u003e. mucin secretion had significant difference, \u003csup\u003e*\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Abbreviations:STAS, spread through air spaces; CEA, carcinoembryonic Antigen; CT: computed tomography; CK7, cytokeratin 7; TTF-1, thyroid transcription factor-1; EGFR, epidermal growth factor receptor.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAmong the 175 patients diagnosed with PMA, 113 (64.6%) cases were classified as pure mucinos type, 36 (20.6%) as mixed mucinous type, and 26 (14.8%) cases with mucin secretion type. Immunohistochemistry expression profiles did not exhibit significant variations among different subtypes. Nevertheless, pure mucinous adenocarcinoma was notably associated with smaller tumor size, lower lobe localization, lower rates of lymph node metastasis and STAS, early pathological stage, CEA\u0026thinsp;\u0026le;\u0026thinsp;5ng/mL, and less \u003cem\u003eEGFR\u003c/em\u003e\u003csup\u003eE19\u003c/sup\u003e mutations (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u0026lt; 0.001, \u0026lt; 0.001, = 0.001, \u0026lt; 0.001, 0.002, 0.001, respectively, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the CT imaging analysis, pure mucinous type demonstrated a significantly higher prevalence of pleural traction and spiculated margins compared to mixed mucinous type (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022, 0.013, respectively). However, no statistically significant differences were observed between pure mucinous type and mucin secretion type for these two imaging biomarkers (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.224, 0.565, respectively). Additionally, comparative analysis revealed no statistically significant difference between mixed mucinous and mucin secretion subetypes in tumor size, tumor localization, lymph node metastasis, STAS, pathological stage, and CEA level (\u003cem\u003eP\u003c/em\u003e all \u003cem\u003e\u0026gt;\u003c/em\u003e\u0026thinsp;0.05, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eConsidering the distinct clinicopathological characteristics observed in pure mucinous type compared to mixed mucinous type and mucin secretion type, our analysis focused on establishing differential criteria to distinguish pure mucinous lesions from these two variant forms, and the results presented significant difference in tumor size, lymph node metastasis, STAS, tumor location, pathological stage, serum CEA level, mixed density, plural traction, and \u003cem\u003eEGFR\u003c/em\u003e\u003csup\u003eE19\u003c/sup\u003e mutation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, \u0026lt; 0.001, 0.001, \u0026lt; 0.001, \u0026lt; 0.001, 0.001, 0.039, 0.032, 0.017, respectively, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Multivariate logistic analysis revealed that tumor size (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.051, \u003cem\u003e95%CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.034\u0026ndash;8.999, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043), lymphnode metastasis (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.137, \u003cem\u003e95%CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.123\u0026ndash;45.334, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037), STAS (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15.098, \u003cem\u003e95%CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.961\u0026ndash;76.987, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), and tumor location (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.220, \u003cem\u003e95%CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.096\u0026ndash;0.506, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were the independent factors for pure mucinous adenocarcinoma (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and multivariate Logistic regression analysis for pure mucinous adenocarcinoma\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=\"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\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCharacteristic Group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e95%CI\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e95%CI\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;60 \u003cem\u003evs.\u003c/em\u003e \u0026gt;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.405\u0026ndash;1.469\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.429\u003c/p\u003e\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\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale \u003cem\u003evs.\u003c/em\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.911\u0026ndash;3.212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.095\u003c/p\u003e\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\u003eSmoking status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNever \u003cem\u003evs.\u003c/em\u003eCurrent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.536\u0026ndash;2.520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.703\u003c/p\u003e\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\u003eTumor size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le; 2.0cm \u003cem\u003evs.\u003c/em\u003e\u0026gt;2.0cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.530\u0026ndash;5.955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.811\u0026ndash;5.834\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elymph node metastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo \u003cem\u003evs.\u003c/em\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.282\u0026ndash;32.292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.123\u0026ndash;45.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.037\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSTAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo \u003cem\u003evs.\u003c/em\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.378\u0026ndash;32.568\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.961\u0026ndash;76.987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor location\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUpper and middle \u003cem\u003evs.\u003c/em\u003e Lower lobe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.140\u0026ndash;0.511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.096\u0026ndash;0.506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathological stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eⅠ \u003cem\u003evs.\u003c/em\u003e Ⅱand Ⅲ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.288\u0026ndash;12.165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.136\u0026ndash;4.525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.786\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum CEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;5 \u003cem\u003evs.\u003c/em\u003e \u0026gt;5 ng/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.996\u0026ndash;12.318\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.381\u0026ndash;6.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.553\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMargin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClear \u003cem\u003evs.\u003c/em\u003e Indistinct\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.479\u0026ndash;1.910\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.899\u003c/p\u003e\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\u003eMixed density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo \u003cem\u003evs.\u003c/em\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.241\u0026ndash;0.964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.039\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.151\u0026ndash;1.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVacuole sign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo \u003cem\u003evs.\u003c/em\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.242\u0026ndash;1.387\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.221\u003c/p\u003e\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\u003ePlural traction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo \u003cem\u003evs.\u003c/em\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.082\u0026ndash;5.572\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.032\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.647\u0026ndash;4.636\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.274\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLobulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo \u003cem\u003evs.\u003c/em\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.862\u0026ndash;3.065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.133\u003c/p\u003e\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\u003eAir bronchogram\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo \u003cem\u003evs.\u003c/em\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.910\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.081\u0026ndash;10.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.939\u003c/p\u003e\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\u003eSpiculated margin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo \u003cem\u003evs.\u003c/em\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.585\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.838\u0026ndash;2.998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.157\u003c/p\u003e\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\u003eP63(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow \u003cem\u003evs.\u003c/em\u003e High expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.708\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.282\u0026ndash;1.779\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.463\u003c/p\u003e\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\u003eCK7(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow \u003cem\u003evs.\u003c/em\u003e High expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.620\u0026ndash;3.709\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.361\u003c/p\u003e\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\u003eKi-67(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow \u003cem\u003evs.\u003c/em\u003e High expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.051\u0026ndash;1.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.059\u003c/p\u003e\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\u003eNapsin A(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow \u003cem\u003evs.\u003c/em\u003e High expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.839\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.359\u0026ndash;1.963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.686\u003c/p\u003e\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\u003eTTF-1(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow \u003cem\u003evs.\u003c/em\u003e High expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.208\u0026ndash;1.248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.140\u003c/p\u003e\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\u003e\u003cem\u003eEGFR\u003c/em\u003e\u003csup\u003eE19\u003c/sup\u003e mutation(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo \u003cem\u003evs.\u003c/em\u003e Yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.595-126.279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.017\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14.703\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.566\u0026ndash;84.233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.003\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEGFR\u003c/em\u003e\u003csup\u003eE21\u003c/sup\u003e mutation(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo \u003cem\u003evs.\u003c/em\u003e Yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.017\u0026ndash;2.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.185\u003c/p\u003e\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\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Abbreviations: STAS, spread through air spaces; CEA, carcinoembryonic Antigen; CT: computed tomography; CK7, cytokeratin 7; TTF-1, thyroid transcription factor-1; \u003cem\u003eEGFR\u003c/em\u003e, epidermal growth factor receptor.\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=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and multivariate Cox regression analysis of postoperative survival in PMA\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eHR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003e95%CI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eHR\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003e95%CI\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;60 \u003cem\u003evs.\u003c/em\u003e \u0026gt;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.274\u0026ndash;1.319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.204\u003c/p\u003e\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\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale \u003cem\u003evs.\u003c/em\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.462\u0026ndash;2.302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.941\u003c/p\u003e\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\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNever \u003cem\u003evs.\u003c/em\u003eCurrent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.425\u0026ndash;3.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.796\u003c/p\u003e\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\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le; 2.0cm \u003cem\u003evs.\u003c/em\u003e\u0026gt;2.0cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.588\u0026ndash;7.721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.333\u0026ndash;3.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elymph node metastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo \u003cem\u003evs.\u003c/em\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.530\u0026ndash;12.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.387\u0026ndash;5.890\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSTAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo \u003cem\u003evs.\u003c/em\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.784\u0026ndash;8.913\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.117\u003c/p\u003e\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\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor location\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUpper and middle \u003cem\u003evs.\u003c/em\u003e Lower lobe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.542\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.243\u0026ndash;1.211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.135\u003c/p\u003e\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\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistological type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePure mucinous \u003cem\u003evs.\u003c/em\u003e Mixed mucinous and mucin secretion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.346\u0026ndash;6.926\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.008\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.394\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.511\u0026ndash;3.804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathological stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eⅠ\u003cem\u003evs.\u003c/em\u003eⅡand Ⅲ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.607\u0026ndash;12.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.752\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.356\u0026ndash;8.627\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSerum CEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;5 \u003cem\u003evs.\u003c/em\u003e \u0026gt;5 ng/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.918\u0026ndash;15.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.034\u0026ndash;8.999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.043\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMargin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClear \u003cem\u003evs.\u003c/em\u003e Indistinct\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.480\u0026ndash;7.276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.238\u0026ndash;6.458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.014\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMixed density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo \u003cem\u003evs.\u003c/em\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.448\u0026ndash;2.301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.970\u003c/p\u003e\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\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVacuole sign\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo \u003cem\u003evs.\u003c/em\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.030\u0026ndash;1.655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.143\u003c/p\u003e\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\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlural traction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo \u003cem\u003evs.\u003c/em\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.514-3.700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.522\u003c/p\u003e\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\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLobulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo \u003cem\u003evs.\u003c/em\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.502\u0026ndash;2.443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.799\u003c/p\u003e\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\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAir bronchogram\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo \u003cem\u003evs.\u003c/em\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.258\u0026ndash;14.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.518\u003c/p\u003e\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\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpiculated margin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo \u003cem\u003evs.\u003c/em\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.683\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.761\u0026ndash;3.718\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.198\u003c/p\u003e\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\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP63(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow \u003cem\u003evs.\u003c/em\u003e High expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.283\u0026ndash;3.593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.989\u003c/p\u003e\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\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCK7(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow \u003cem\u003evs.\u003c/em\u003e High expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.324\u0026ndash;4.507\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.778\u003c/p\u003e\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\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKi-67(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow \u003cem\u003evs.\u003c/em\u003e High expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.270\u0026ndash;5.404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.804\u003c/p\u003e\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\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNapsin A(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow \u003cem\u003evs.\u003c/em\u003e High expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.039\u0026ndash;2.340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.252\u003c/p\u003e\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\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTTF-1(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow \u003cem\u003evs.\u003c/em\u003e High expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.275\u0026ndash;2.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.595\u003c/p\u003e\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\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEGFR\u003c/em\u003e\u003csup\u003eE19\u003c/sup\u003e mutation(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo \u003cem\u003evs.\u003c/em\u003e Yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.266\u0026ndash;16.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.020\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.686\u0026ndash;2.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.044\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEGFR\u003c/em\u003e\u003csup\u003eE21\u003c/sup\u003e mutation(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo \u003cem\u003evs.\u003c/em\u003e Yes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.246\u0026ndash;14.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.541\u003c/p\u003e\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\u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Abbreviations: STAS, spread through air spaces; CEA, carcinoembryonic Antigen; CT: computed tomography; CK7, cytokeratin 7; TTF-1, thyroid transcription factor-1; \u003cem\u003eEGFR\u003c/em\u003e, epidermal growth factor receptor.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eLASSO regression was adopted to identify parameters linked to different subtype of PMA (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B), followed by the development of a nomogram based on the four identified parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The training set yielded an AUC of 0.810 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE), while the validation set produced an AUC of 0.785 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE), indicating the strong performance of the predictive model. The calibration plot demonstrated generally favorable performance (MAE\u0026thinsp;=\u0026thinsp;0.046), with room for potential improvement, particularly within the intermediate probability ranges (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The DCA illustrated that the nomogram model offered greater net benefits across a broad range of high-risk thresholds compared to strategies that strictly categorize individuals as high or low risk. Notably, the nomogram curve exhibited a sharp decline around a threshold of 0.8, implying an increase in the model's false-negative or false-positive rate in this range, leading to reduced net benefit (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eAnalysis of prognostic factors and predictive modeling for PMA\u003c/h2\u003e\u003cp\u003eUnivariate Cox proportional hazards analysis revealed larger tumor size, lymph node metastases, mixed mucinous and mucin secretion type, advanced pathological stage, higher CEA levels, indistinct margin, and \u003cem\u003eEGFR\u003c/em\u003e\u003csup\u003eE19\u003c/sup\u003e mutation were distinctly correlated with prognosis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, \u0026lt; 0.001, = 0.008, \u0026lt; 0.001, \u0026lt; 0.001, 0.003, 0.020, respectively, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Subsequently, variables showing univariate significance were included in the multivariate Cox proportional hazards regression. Higher CEA levels (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.051, \u003cem\u003e95%CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.034\u0026ndash;8.999, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043), indistinct margin (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.828, \u003cem\u003e95%CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.238\u0026ndash;6.458, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014), and \u003cem\u003eEGFR\u003c/em\u003e\u003csup\u003eE19\u003c/sup\u003e mutation (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.994, \u003cem\u003e95%CI\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.686\u0026ndash;2.017, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044), were identified as independent prognostic factors in PMA (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLASSO regression analysis identified three parameters and tumor size, lymph node metastases, histological type and stage associated with postoperative survival in PMA (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B). Using these predictive criteria, a nomogram was developed to predict the 3-year and 5-year overall survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). In the training set (n\u0026thinsp;=\u0026thinsp;122, d\u0026thinsp;=\u0026thinsp;108, p\u0026thinsp;=\u0026thinsp;7, B\u0026thinsp;=\u0026thinsp;100, 30 subjects per group), the AUC of the ROC curve for predicting of 3-year and 5-year overall survival were 0.630 and 0.601, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). For the internal validation set (n\u0026thinsp;=\u0026thinsp;53, d\u0026thinsp;=\u0026thinsp;11, p\u0026thinsp;=\u0026thinsp;7, B\u0026thinsp;=\u0026thinsp;100, 20 subjects per group), the corresponding AUC values were 0.587 and 0.543 respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). The calibration curves for the 3-year and 5-year overall survival in the training set suggested that the model exhibited strong calibration with a C-index of 0.827 (95% CI: 0.751\u0026ndash;0.954) and 0.782 (95%CI: 0.691\u0026ndash;0.863), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). However, due to the limited sample size and fewer occurrences in the internal validation set, there may be increased variability in the calibration assessment with C-index of 0.605 (95%CI: 0.446\u0026ndash;0.758) and 0.628 (95%CI: 0.492\u0026ndash;0.761) for 3-year and 5-year overall survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBy calculating the nomogram-derived risk scores (range: 0-375 points) for each patient, we determined the statistically optimal cutoff value through survival-based cutoff point analysis. A threshold of 34 points was established to stratify patients into different prognostic groups: individuals with scores\u0026thinsp;\u0026le;\u0026thinsp;34 points formed the low-risk group (n\u0026thinsp;=\u0026thinsp;119), while those with scores\u0026thinsp;\u0026gt;\u0026thinsp;34 points constituted the high-risk group (n\u0026thinsp;=\u0026thinsp;56). Kaplan-Meier survival analysis revealed significantly distinct clinical outcomes between the two risk groups, as evidenced by a remarkable separation in the overall survival curves (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Moreover, although histological subtype of PMA was not identified as an independent factor for the outcome of patients with PMA, Kaplan-Meier curve displayed that cases with pure mucinous type had longer overall survival time than those with mixed mucinous type or mucin secretion type (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.032, 0.013, respectively).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePMA represents a rare subtype of lung adenocarcinoma, characterized by its low incidence rate yet sharing a similar epidemiology profile with other lung adenocarcinoma subtypes[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Noteworthy distinctions in clinical, pathological, genetic and CT characteristics set PMA apart from non-mucinous lung adenocarcinomas[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Notably, our previous research identified heterogeneous PMA subtypes based on variations in mucinous cellularity and extracellular mucinous matrix composition within the tumor microenvironments[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Despite these advancements, inconsistent survival comes persist, likely due to limited samples sizes and diverse analytical approaches. A key challenge lies in integrating multifaceted data including clincopathological, genetic and CT imaging features, to accurately predict prognosis. Machine learning-based nomogram models offer a promising solution by leveraging complex, high-dimensional data to capture nonlinear relationships and improve predictive precision. However, the systematic application of nomogram models to assess histological subtype and survival outcomes in PMA, remains scarce, highlighting an understudied area. Addressing this gap could refine risk stratification and guide personalized therapeutic interventions for this rare yet clinically complex malignancy.\u003c/p\u003e\u003cp\u003eOur study reaffirmed previous observations that PMA is predominantly associated with the lower lobe of the lung, T1 stage, N0 stage, and pathological stage I[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Notably, we found that the pure mucinous type of PMA exhibited less aggressive compared to mixed mucinous and mucin secretion types characterized by smaller tumor size, early pathological stage, CEA level\u0026thinsp;\u0026le;\u0026thinsp;5ng/mL, lower percentage of lymph node metastasis and STAS, and \u003cem\u003eEGFR\u003c/em\u003e\u003csup\u003eE19\u003c/sup\u003e mutation. Hwang et al. classified the malignant degree of PMA based on the extent of lepidic and non-lepidic invasive patterns, noting that invasive patterns were least common in small tumors and progressively increased in larger tumors, suggesting a correlation between histological subtype of PMA and tumor progression.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Moreover, consistent with prior reports, \u003cem\u003eEGFR\u003c/em\u003e mutation rates in PMA vary significantly across populations (0-33.3%) due to racial and technical disparities[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A subsequent investigation revealed \u003cem\u003eEGFR\u003c/em\u003e mutations in 3 out of 41 metastatic PMA cases, which showed favorable clinical responses to EGFR-targeted therapy [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Our results indicated \u003cem\u003eEGFR\u003c/em\u003e\u003csup\u003eE19\u003c/sup\u003e and \u003cem\u003eEGFR\u003c/em\u003e\u003csup\u003eE21\u003c/sup\u003e mutation accounted for 4.8% and 2.4%, respectively. Crucially, mucin secretion type exhibited a higher frequency of \u003cem\u003eEGFR\u003c/em\u003e mutation compared to pure mucinous type, consistent with another Chinese cohort[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Additionally, in CT imaging features, the pure mucinous type showed a notably higher prevalence of pleural traction and spiculated margins compared to mixed mucinous type, while being similar to mucin secretion type. Compared to non-mucinous adenocarcinoma, mucinous adenocarcinoma were more likely to exhibit indistinct margins and pleural retraction[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and the spiculated margins being more common in pure mucinous adenocarcinoma than in mixed mucinous adenocarcinoma[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSignificantly, we have pioneered the development and validation of a predictive model for distinguishing pure PMA utilizing logistic and LASSO regression analyses within a machine learning framework. The model demonstrated robust performance with relatively high AUC values in both the training and validation sets, along with well-calibrated curves. This predictive model offers a promising approach for accurately identifying pure mucinous adenocarcinoma, providing a valueble tool for facilitating more precise therapeutic interventions for patients.\u003c/p\u003e\u003cp\u003eTo enhance the accuracy of prognostic modeling for PMA, we initially employed Cox regression analysis to identify prognostic factors of PMA, identifying the indistinct margin, higher CEA level and \u003cem\u003eEGFR\u003c/em\u003e\u003csup\u003eE19\u003c/sup\u003e mutation as independent prognostic factors for PMA patients. CEA had been established as a clinically significant tumor biomarker known to facilitate malignant progression in lung cancer[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Our previous cohort study revealed a correlation between elevated serum CEA level and adverse clinical outcomes[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Additionally, our cohort identified indistinct margin as an independent prognostic factor in PMA outcomes. Notably, downstream genes activation by \u003cem\u003eEGFR\u003c/em\u003e mutants has been linked to anti-apoptosis, pyroptosis, metastasis, and immune evasion[\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. While specific subtypes of \u003cem\u003eEGFR\u003c/em\u003e mutations are rarely used as distinguishing factors in prognostic assessments for PMA[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], our previous findings indicated that \u003cem\u003eEGFR\u003c/em\u003e\u003csup\u003eE19\u003c/sup\u003e was a unfavourable prognostic marker in lung adenocarcinoma patients, with worse outcomes compared to \u003cem\u003eEGFR\u003c/em\u003e\u003csup\u003eE21\u003c/sup\u003e mutation cases[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Moreover, Deng et al. demonstrated \u003cem\u003eEGFR\u003c/em\u003e mutation was a poor prognostic factor in PMA and advanced stage lung adenocarcinoma[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], which was correlated with our study.\u003c/p\u003e\u003cp\u003eLASSO regression technique from machine learning was utilized to incorporate variables in the development of a nomogram. The nomogram model, along wtih time-dependent ROC curves revealed moderately high AUC values for the 3-year and 5-year survival in both training and validation sets, while the calibration curves demonstrated strong consistency. The relatively lower AUC values of the nomogram may stem from the single-center data collection approach, potentially limiting the generalizability of the results. Despite not achieving high AUC and C-index, the survival curve displayed significant differentiation between the two stratified groups, indicating the effectively discrimination ability of the nomogram and its potential utility in clinical prediction. Consistent with prior studies that have also developed satisfactory nomogram model despite moderately high C-index values[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], our findings underscore the important of histopathological classification in PMA by demonstrating that pure mucinous adenocarcinoma exhibits significantly superior clinical outcome compared to other PMA subtype, validating the prognostic relevance of histopathological classification in PMA.\u003c/p\u003e\u003cp\u003eThis study exhibits notable strengths and limitations. Initially, the data collection was confined to a single center, potentially introducing bias, and further multicenter cohort studies are warranted to enhance the dataset's breadth and credibility. Secondly, the restricted patient population with PMA and the frequency of fatalities might result in data bias when partitioning patients into training and validation sets. The limited sample size and reduced event rate could lead to increased variability in calibration assessments. Thirdly, due to constrains in laboratory infrastructure in the archipelago setting, \u003cem\u003eKRAS\u003c/em\u003e mutations were not identified in the patient cohort. Consequently, expanding the sample size, refining grouping strategies, or employing avanced corrective methods is recommended. Furthermore, it is crucial to validate the model's performance across multiple datasets to ensure its generalizability.\u003c/p\u003e\u003cp\u003eFurthermore, this study employed two distinct machine learning regression methodologies. In clinical practice, the implementation of binary classification analysis through machine learning provides a robust framework for predicting binary outcomes, such as distinguishing between disease subtypes or estimating the likelihood of positive outcomes within a defined period. Similarly, machine learning-based prognostic analysis can effectively forecast patient survival under varying conditions. This investigative process has the potential to unveil previously unidentified yet clinically significant insights.\u003c/p\u003e\u003cp\u003eIn conclusion, we have established a refined histopathological classification approach for PMA with our validation study revealing distinctive clinicopathological characteristics indicating lower aggressiveness in pure mucinous adenocarcinoma compared to mixed mucinous and mucin secretion types. Moreover, our developed nomogram model exhibited exceptional performance in predicting histological subtype of PMA. Importantly, higher CEA level, indistinct margin, and \u003cem\u003eEGFR\u003c/em\u003e\u003csup\u003eE19\u003c/sup\u003e mutation were confirmed as independent prognostic factors for PMA. These findings offer valuable insights for histopathological diagnosis and prognostic evaluation in the context of PMA.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePMA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epulmonary mucinous adenocarcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLASSO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eleast absolute shrinkage and selection operator\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNSCLC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003enon-small cell lung carcinomas\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWorld Health Organization\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAIS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eadenocarcinoma in situ\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMIA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eminimally invasive adenocarcinoma,IAC,invasive adenocarcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eML\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emachine learning\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eartificial intelligence\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEMR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eelectronic medical record\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHRCT high-resolution CT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eoverall survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003earea under the curv\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003edecision curve analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEGF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eepidermal growth factor receptor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCEA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecarcinoembryonic antige\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSTAS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003espread through air spaces.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the anonymous reviewers for reviewing the manuscript and providing valuable comments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(I) Conception and design: Wangyu Zhu; (II) Administrative support: Hanbo Le and Yongkui Zhang; (III) Provision of study materials or patients: Weiwei Yang, Chen Chen, Zehao Chen, and Ziyi Chen; (IV) Collection and assembly of data: Weiwei Yang, Chen Chen, and Lue Li; (V) Data analysis and interpretation: Wangyu Zhu, Weiwei Yang; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported in part by grants from the Health Commission of Zhejiang Province, and National Traditional Chinese Medicine Comprehensive Reform Demonstration Zone (grant no. 2022RC292 and GZY-KJS-ZJ-2025-091 to Wang-yu Zhu), the Scientific Research Program of Traditional Chinese Medicine of Zhejiang Province (grant no. 2021ZB349 to Lue Li), and the Science and Technology Program of Zhoushan (grant no. 2023C31001 to Han-bo Le). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study can be obtained under reasonable conditions by contacting Dr. Wangyu Zhu (email:[email protected]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective cohort study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Zhoushan Hospital (Approval No.: Zhoushan Hospital Ethical Review 2025-052-01).\u0026nbsp;The requirement for informed consent was formally waived by the ethics committee.Individual patient contact was impracticable given the study\u0026apos;s historical design.\u003c/p\u003e\n\u003cp\u003eAll personal identifiers (including names, ID numbers, and admission dates) were removed prior to analysis using a double-masking protocol. The anonymization process was independently verified by the hospital\u0026apos;s data security officer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors gave consent for the publication of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest was reported by theauthor(s).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel R L, Giaquinto A N and Jemal A. Cancer statistics, 2024\u003cem\u003e.\u003c/em\u003e \u003cem\u003eCA Cancer J Clin\u003c/em\u003e 2024; \u003cstrong\u003e74\u003c/strong\u003e:12-49.\u003c/li\u003e\n\u003cli\u003eLi C, Lei S, Ding L, et al. 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Prognostic value of epidermal growth factor receptor gene mutation in resected lung adenocarcinoma\u003cem\u003e.\u003c/em\u003e \u003cem\u003eJ Thorac Cardiovasc Surg\u003c/em\u003e 2021; \u003cstrong\u003e162\u003c/strong\u003e:664-74.e667.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Machine learning, nomogram, histological characteristics, prognosis, pulmonary mucinous adenocarcinoma","lastPublishedDoi":"10.21203/rs.3.rs-7827699/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7827699/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePulmonary mucinous adenocarcinoma (PMA) represents a rare lung adenocarcinoma subtype, characterized by a lacks of comprehensive pathological classification and prognostic factors. In this study, we introduce a multimodal machine learning framework aimed at improving the accuracy of PMA subtyping and predicting the prognosis of PMA patients.\u003c/p\u003e\u003ch2\u003eMaterials and methods\u003c/h2\u003e\u003cp\u003eThis retrospective study enrolled 175 surgically resected primary PMA cases and demographic, histopathological, CT imaging, and genomic data of patients were collected. LASSO regularized logistic regression model were utilized for histological classification, and Cox proportional hazards model were employed for survival prediction, with internal validaition.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003ePure mucinous adenocarcinoma presented a higher prevalence of smaller tumor size, lower lobe localization, absence of lymph node metastasis, STAS, early pathological stage, CEA\u0026thinsp;\u0026le;\u0026thinsp;5ng/mL, and \u003cem\u003eEGFR\u003c/em\u003e\u003csup\u003eE19\u003c/sup\u003e mutation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u0026lt; 0.001, \u0026lt; 0.001, = 0.001, \u0026lt; 0.001, 0.002, 0.001, respectively) compared to mix mucinous or mucin secretion adenocarcinoma. A machine learning-derived nomogram achieved discriminative accuracy (training AUC\u0026thinsp;=\u0026thinsp;0.810; validation AUC\u0026thinsp;=\u0026thinsp;0.785) with excellent calibration. Multivariate Cox modeling identified higher CEA levels, indistinct margin, and \u003cem\u003eEGFR\u003c/em\u003e\u003csup\u003eE19\u003c/sup\u003e mutation as independent prognostic factors in PMA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043, 0.014, 0.044, respectively). Moreover, Kaplan-Meier curve revealed significantly different outcomes between low and high risk groups stratified upon the nomogram score (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003ePulmonary pure mucinous adenocarcinoma exhibited lower malignancy compared to the mixed mucinous and mucin secretion type. The nomogram model developed and validated in this study exhibited outstanding efficacy in predicting histological subtype and survival of PMA, offering valuable .guidance for clinicians in diagnosis and treatment decision-making.\u003c/p\u003e","manuscriptTitle":"Integrating Multimodal Data for Precise Subtyping and Prognostication in Pulmonary Mucinous Adenocarcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 12:47:12","doi":"10.21203/rs.3.rs-7827699/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"470bda4e-25ae-4a93-927b-d269a13454a2","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-17T12:47:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-17 12:47:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7827699","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7827699","identity":"rs-7827699","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}