Risk Stratification and Overall Survival Prediction in extensive stage Small Cell Lung Cancer after chemotherapy with immunotherapy Based on CT Radiomics

Risk Stratification and Overall Survival Prediction in extensive stage Small Cell Lung Cancer after chemotherapy with immunotherapy Based on CT Radiomics | 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 Article Risk Stratification and Overall Survival Prediction in extensive stage Small Cell Lung Cancer after chemotherapy with immunotherapy Based on CT Radiomics Fang Wang, Wujie Chen, Fangmin Chen, Jinlan Lu, Yanjun Xu, Min Fang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4097602/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Sep, 2024 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Purpose The prognosis of extensive-stage small cell lung cancer is usually poor. In this study, a combined model based on pre-treatment CT radiomics and clinical features was constructed to predict the OS of extensive-stage small cell lung cancer after chemotherapy with immunotherapy. Methods Clinical data of 111 patients with extensive stage small-cell lung cancer who received first-line immunotherapy combined with chemotherapy in our hospital from December 2019 to December 2021 were retrospectively collected. Finally, 93 patients were selected for inclusion in the study, and CT images were obtained through PACS system before treatment. All patients were randomly divided into a training set (n = 66) and a validation set (n = 27). Images were imported into ITK-SNAP to outline areas of interest, and Python software was used to extract radiomics features. A total of 1781 radiomics features were extracted from each patient's images. The feature dimensions were reduced by MRMR and LASSO methods, and the radiomics features with the greatest predictive value were screened. The weight coefficient of radiomics features was calculated, and the linear combination of the feature parameters and the weight coefficient was used to calculate Radscore. Univariate cox regression analysis was used to screen out the factors significantly associated with prognosis from the radiomics and clinical features, and multivariate cox regression analysis was performed to establish the prognosis prediction model of extensive stage small cell lung cancer. Results The degree of metastases was selected as a significant clinical prognostic factor by univariate cox regression analysis. Seven radiomics features with significance were selected by LASSO-COX regression analysis, and the Radscore was calculated according to the coefficient of the radiomics features. An alignment diagram survival prediction model was constructed by combining Radscore with the number of metastatic lesions. The study population was stratified into those who survived less than 11 months, and those with a greater than 11 month survival. The C-index was 0.722 (se = 0.044) and 0.68(se = 0.074) in the training and the validation sets, respectively. The Log_rank test results of the combination model were as follows: training set: p < 0.0001, validation set: p = 0.00042. Conclusion In this study, a combined model based on radiomics and clinical features could predict OS in patients with extensive stage small cell lung cancer after chemotherapy with immunotherapy, which could help guide clinical treatment strategies. Biological sciences/Cancer Biological sciences/Immunology small cell lung cancer overall survival prediction model radiomics computed tomography Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Small cell lung cancer (SCLC) is an aggressive neuroendocrine malignancy, accounting for 10%-15% of all lung cancers, and is commonly associated with smoking [ 1 ]. Most patients present in an extensive stage with multiple metastases at the time of diagnosis, and the prognosis is often extremely poor, with a 5-year survival rate of only 6.3% [ 2 ]. The TNM staging according to the AJCC guidelines was used to guide clinical decision-making and prognosis evaluation. However, a previous study [ 3 ] had shown that the applicability of TNM staging to assess extensive-stage small-cell lung cancer (ES-SCLC) prognosis in clinical practice remains uncertain. Therefore, it is necessary to search for more reliable biomarkers to predict the prognosis of ES-SCLC, to accurately guide clinical treatment. In recent years, there have been many applied studies on efficacy prediction and the prognosis assessment of radiomics. Many studies [ 4 – 10 ] have shown that radiomics has a good application value in predicting immunotherapy efficacy with different types of cancer. Yet, the number of studies describing using radiomics to predict the outcomes of SCLC are few. Herein, a machine learning algorithm was performed on pre-treatment CT images to extract radiomics features. These results were then combined with clinical features to construct a combined model to predict the prognosis of patients with ES-SCLC after first-line chemotherapy with immunotherapy. MATERIALS AND METHODS Patients A total of 111 patients with initial ES-SCLC in our hospital from December 2019 to December 2021 were retrospectively collected. Inclusion criteria: (1) patients were pathologically or cytologically diagnosed with ES-SCLC; (2) received at least 2 doses of chemoimmunotherapy as first line treatment therapy;(3) CT images before treatment; (4) With follow-up. Exclusion criteria: (1) received any other anti-tumor therapy before immunotherapy; (2) No CT images before treatment; (3) patients without progression during follow up. Finally, a total of 93 cases were included in this study. The immunotherapy regimen was durvalumab or atezolizumab. And the chemotherapy regimen was platinum etoposide. CT scanning protocol The CT scanning equipment used in our hospital included GE, Siemens and Philips models; Tube voltage was120KV, tube current: GE: 20-140mAs; Siemens: 110mAs; Philips: 101mAs; Collimator: GE and Philips: 0.624x64MM; Siemens: 0.6x128MM; Matrix: GE and Siemens: 512x512; Philips: 1024x1024; Pitch: GE:1.375P; Siemens: 1.2P; Philips: 1.0P; Tube speed: GE:0.4s; Siemens and Philips: 0.5s. During the examination, the patient was supine, and the scan was conducted during the end of deep inspiration. The visual range ranged from the thoracic entrance to the level below the costophrenic angle. The layer thickness was 5mm, and the pitch was 5mm. Prior to scanning, 85ml-95ml of CT contrast agent was injected into the cubital vein with a high pressure syringe at a rate of 3.0ml/s. The scans were performed 60 seconds after the injection, and the scans were performed in one phase. Segmentation of the tumor The chest CT images were imported into ITK-SNAP (version 3.8.0, USA) [ 11 ]. The window position used was the enhanced mediastinal window, which could show a good boundary between the lesion and the surrounding large blood vessels, obstructive atelectasis or obstructive pneumonia. An outline was made along the inner boundary of the tumor focus, and the entire tumor focus was outlined to obtain the 3D-ROI. The delineation of ROI was carried out by two cardiothoracic diagnostic physicians alone with more than 5 years of experience blinded to the outcome of study patients. In the delineation process, the trachea, vasculature, distal obstructive atelectasis and regions of pneumonia were avoided. Feature extraction and screening python (3.7) software was used to extract features based on pyradiomics conforming to IBSI standard [ 12 ]. The steps of feature screening were as follows: 1) Outliers in data are replaced by median values; 2) Conducted Z-Score standardization for each characteristic data value. For feature dimension reduction, MRMR and least absolute shrinkage and selection operator (LASSO) method were used to select the most predictive radiomics features. The LASSO-cox regression analysis model was used to construct the radiomics survival analysis model, and the Radscore was calculated. Data analysis and model construction All patients were randomly divided into the training set and validation set according to the ratio 7:3. Univariate cox regression was used to select significant predictors of radiomics characteristics (P < 0.05). Correlation analysis was performed among features. Univariate cox regression analysis model was used to screen out statistically significant predictors of clinical features (p < 0.05). Multiple Cox regression analyses were performed to the construct the combined model based on the features retained by Radscore and clinical features. Statistical analysis SPSS 26.0 was used for statistical analysis, and classified data were compared by Chi-square test or Fisher exact probability method. Quantitative data were first tested for normality.Quantitative data with a normal distribution were represented by mean ± standard deviation and compared by an independent sample t-test.Quantitative data without the normal distribution were compared by Mann-Whitney U test, p < 0.05 indicated statistically significant differences. Model verification: C-index and KM curve (Log-Rank) were used for survival analysis. The research process was shown in Fig. 1 . A medical image acquisition, B Segmentation of the tumor, C Feature extraction, D Features selection, E Model construction, F Model verification RESULTS Clinical features A total of 93 cases were included, which were divided into training set (n = 66) and validation set (n = 27). Two groups with an average age of 64.1 (±6.9) and 62.7 (±6.8) were obtained. There were 62 males (93.9%) and 4 females (6.1%) in the training set. There were 26 males (96.3%) and 1 female (3.7%) in the validation set. There were no statistically significant differences in clinical features between the training and validation sets (p > 0.05) (Table 1 ). In addition, univariate cox regression analysis showed that the risk ratio of the number of metastatic lesions in the training set was 2.43 (p = 0.007), which was statistically correlated with OS. The count of metastatic lesions(transfer-ct.) was referred to the total number of single-organ metastases or multi-organ metastases lesions. Groups were divided by whether they had more than, or less than/equal to 5 metastatic lesions. Table 1 There was no significant difference in clinical features between the training and test set Variable Level Training group(n = 66) Test group(n = 27) P -value gender male 62(93.9%) 26(96.3%) female 4(6.1%) 1(3.7%) 0.647 Age Mean (SD) 64.1(6.9) 62.7(6.8) 0.349 C T 1 9(13.6%) 5(18.5%) 2 22(33.3%) 7(25.9%) 3 16(24.2%) 4(14.8%) 4 19(28.8%) 11(40.7%) 0.516 C N 0 2(3.0%) 0(0.0%) 1 3(4.5%) 2(7.4%) 2 13(19.7%) 9(33.3%) 3 48(72.7%) 16(59.3%) 0.373 C M 0 5(7.6%) 2(7.4%) 1 61(92.4%) 25(92.6%) 0.978 smoking NO 16(24.2%) 5(18.5%) YES 50(75.8%) 22(81.5%) 0.549 diabetes NO 56(84.8%) 26(96.3%) YES 10(15.2%) 1(3.7%) 0.121 hypertension NO 52(78.8%) 23(85.2%) YES 14(21.2%) 4(14.8%) 0.478 Heart-disease NO 65(98.5%) 26(96.3%) YES 1(1.5%) 1(3.7%) 0.509 Baseline-lung NO 63(95.5%) 26(96.3%) YES 3(4.5%) 1(3.7%) 0.856 Hepatitis-B NO 63(95.5%) 25(92.6%) YES 3(4.5%) 2(7.4%) 0.579 location left 29(43.9%) 15(55.6%) right 37(56.1%) 12(44.4%) 0.308 liver NO 43(65.2%) 19(70.4%) YES 23(34.8%) 8(29.6%) 0.628 bone NO 43(65.2%) 17(63.0%) YES 23(34.8%) 10(37.0%) 0.841 brain NO 53(80.3%) 25(92.6%) YES 13(19.7%) 2(7.4%) 0.144 lung NO 57(86.4%) 19(70.4%) YES 9(13.6%) 8(29.6%) 0.070 pleural NO 59(89.4%) 20(74.1%) YES 7(10.6%) 7(25.9%) 0.061 Adrenal-glands NO 55(83.3%) 23(85.2%) YES 11(16.7%) 4(14.8%) 0.826 Distant-lymph-node NO 56(84.8%) 22(81.5%) YES 10(15.2%) 5(18.5%) 0.689 Transfer-ct. 5 34(51.5%) 16(59.3%) 0.497 Feature Selection and Radscore calculation Python software (3.7) was used to screen 1781 radiomics features extracted from CT enhanced mediastinal window sequences by LASSO-COX regression analysis, and 7 features were finally screened. exponential_gldm_LargeDependenceHighGrayLevelEmphasis, wavelet.LLH_firstorder_90Percentile, exponential_gldm_SmallDependenceHighGrayLevelEmphasis, wavelet.HHH_glrlm_LowGrayLevelRunEmphasis, wavelet.HHH_firstorder_Median, original_gldm_LargeDependenceHighGrayLevelEmphasis, wavelet.HHH_glcm_Correlation. The corresponding radscore was calculated according to the coefficients of the radiomics features. Clinical Predictors Univariate cox regression analysis showed that the count of metastatic lesions(transfer-ct.) was an independent prognostic factor (p < 0.05), those with more than 5 metastatic lesions were considered a high-risk group, while those with less than 5 fit into the low-risk group. A multivariate cox regression analysis showed that the Radscore was an independent risk factor (Table 2 ). Table 2 Preoperative clinical risk factors for OS in patients with SCLC Variable Univariable analysis Multivariable analysis OR (95% CI) P -value OR (95% CI) P -value Age 1.000(0.950 − 1.054) 0.989 Sex 0.564(0.135 − 2.350) 0.431 - Location 1.010(0.541 − 1.889) 0.974 - cT 1.177 (0.866 − 1.600) 0.297 - cN 1.211 (0.722 − 2.033) 0.468 - cM 0.663(0.187 − 2.071) 0.440 - Smoking 0.692(0.344 − 1.392) 0.302 - Diabetes 1.155 (0.530 − 2.519) 0.717 - Hypertension 0.719 (0.330 − 1.566) 0.407 - Heart 6.992(0.884 − 55.320) 0.065 - Baseline-lung 0.414(0.056 − 3.050) 0.387 - Hepatitis 0.000(0.000 − Inf) 0.996 - Liver 1.618 (0.866 − 3.023) 0.131 - Bone 1.692(0.895 − 3.200) 0.106 - Brain 1.134(0.535 − 2.401) 0.743 - Lung 0.432(0.133 − 1.406) 0.163 - Pleural 1.421(0.590 − 3.420) 0.433 - Adrenal-glands 0.839(0.327 − 2.152) 0.714 - Distant-lymph-node 1.003(0.420 − 2.396) 0.994 - Others 2.229(0.856 − 5.807) 0.101 - Transfer-ct. 2.431 (1.271 − 4.650) 0.007* 1.911(0.984 − 3.710) 0.056* Progress-mode 1.003(0.509 − 1.979) 0.992 Radscore 6.755(3.008 − 15.170) < 0.001* 5.888(2.574 − 13.470) < 0.001* Establishment of a combined model with radiomics and clinical features An alignment Diagram model was constructed by combining the radscore and the number of metastatic lesions (Fig. 2 ). The KM curve (Fig. 3 ) and the Log-rank test showed that the combined model based on radiomics and clinical features could predict the OS of ES-SCLC after first-line chemotherapy and immunotherapy in both the training set (p < 0.0001) and the validation set (p = 0.00042). The C-index of the line graph model was 0.722 (se = 0.044) in the training set and 0.68 (se = 0.074) in the validation set, respectively. The nomogram of survival combined model. The nomogram showed the clinical application of the survival combined model. The red highlights showed that the total point of the patient was 97 and the probability of survival time of the patient less than 11 months was 0.6. ***means P value < 0.001. DISCUSSION In this study, a combined model based on the Radscore selected from radiomics features and the number of metastatic lesions selected from clinical features was constructed to predict the OS for ES-SCLC after chemotherapy and immunotherapy. The test results of the C-index and the KM curve were satisfactory, which indicated that the model established in this study can objectively and accurately stratify the survival of ES-SCLC patients. Chen et al [ 13 ] reported on the value of radiomics for progression-free survival prediction for ES-SCLC after chemotherapy with etoposide and cisplatin. 5 and 6 radiomics features were extracted from the lung window and the enhanced mediastinal window, respectively. The C-index of the model constructed with 11 features was 0.7531 and the average C/D AUC was 0.8487 in the validation set, which was greater than that of the model constructed with lung window (C-index 0.6951, the mean C/D AUC was 0.7836) and enhanced mediastinal window (C-index was 0.7192, the mean C/D AUC was 0.7964). Another study [ 14 ] used radiomics to predict the efficacy of platinum-based chemotherapy and found an OS of 153 SCLC patients with lung window feature modeling. The results showed that the radiomics risk score was correlated with the OS, with a C-index of 0.72 in training set and 0.69 in validation set. The results of the two studies showed that the effectiveness of the lung window feature model was essentially identical, and the C-index of the validation set was 0.69. In this study, the radiomics features of the enhanced mediastinal window were extracted, and the C-index of the combined model established with the clinical features was 0.722 in the training set and 0.68 in the validation set, respectively. These results were similar to what was reported above. Chen et al. showed that the predictive value of the model constructed by the radiomics features of different window positions was higher than that of the radiomics model with a single window position. According to the correlation study between pathological and radiomics features [ 15 , 16 ], plain CT images reflected the uneven tissue and cell density caused by necrosis, bleeding, and degeneration inside the tumor. The enriched and deficient blood supply areas in the enhanced scan images reflect the heterogeneity of blood supply vessels in the tumor. The heterogeneity within the tumor was translated into a quantitative expression of radiomics pixel density and distribution characteristics. Therefore, the radiomics features acquired from both the plain sequence and the enhanced sequence is greater than that of a single sequence, and the combination of multiple sequences was needed for disease diagnosis in practice. It was suggested that the multi-sequence radiomics features can be extracted for modeling in subsequent studies, and the differences with the single sequence model can be analyzed statistically. This study was the first to use radiomics to predict the prognosis for SCLC after first-line chemotherapy plus immunotherapy, which is unique from previous radiomics studies which predicted prognosis after chemotherapy was used alone. The C-index of this model was noted to be slightly different from other models examining only conventional chemotherapy. There was also a limited amount of literature that reports on the application of radiomics in predicting the efficacy of immunotherapy. The study of melanoma indicated that radiomics has a certain value in predicting the efficacy of first-line immunotherapy [ 17 ]. By combining radiomics and clinical characteristics, a more realistic prognostic prediction model was constructed to accurately predict the survival rate of patients with ES-SCLC after first-line immunotherapy plus chemotherapy. Traditionally, the prognosis of SCLC was mainly based on the TNM staging of AJCC. However, in this study, the T stage, N stage and M stage were not independent predictors of OS in ES-SCLC. Rather, only the number of metastases in the M1 stage was an independent factor, which was consistent with the results of previous literature [ 18 ]. In this study, the patients were divided into two subgroups based on those which had more than, or less than 5 metastatic lesions. The greater the number of metastatic lesions, the worse the prognosis, which was consistent with clinical practice. This study did not find any evidence to suggest that smoking is an independent risk factor for OS in SCLC. In addition, the imbalance of male to female participants in this study may add a confounding variable. However, it has been well-documented that brain metastasis is a poor prognostic indicator [ 19 ]. Prophylactic whole brain irradiation (PCI) has been shown to reduce the incidence of brain metastasis and improve prognosis. In this study, some patients received PCI independently. As such, brain metastasis had no overall correlation with OS. Several studies [ 19 , 21 ] have reported that thoracic radiotherapy could improve the OS of ES-SCLC. Some patients in this study cohort received chest radiotherapy after first-line immunotherapy plus chemotherapy. Therefore, the value of the combined radiomics model in this study may be affected by the treatment strategies that were used. There are several limitations to this study. Firstly, as a single-center retrospective study, the design is susceptible to selective bias. The number of cases in this study is also relatively small for AI machine learning. A better prediction model may be necessary by expanding the cohort. Finally, the gender ratio of this study biases males due to the demographic of SCLC. A more balanced study may be required to draw definite conclusions. CONCLUSION Herein, this study determined that radiomics information combined with the clinical examination was able to predict the OS of patients objectively and accurately with ES-SCLC after first-line immunotherapy plus chemotherapy. The radiomics nomogram used in this study may help guide treatment strategy by identifying key prognostic indicators for the overall outcome. Declarations DATA AVAILABILITY STATEMENT The original data could be provided on reasonable request from the authors. Requests to access these datasets should be directed to HT, [email protected] . ETHICS STATEMENT The studies involving human participants were reviewed and approved by the ethics committees of *** Hospital. Written informed consent for participation was waived in accordance with the national legislation and the institutional requirements. 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DOI: 10.2147/OTT.S262084 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 Sep, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 17 Jul, 2024 Reviews received at journal 13 Jul, 2024 Reviews received at journal 25 Jun, 2024 Reviewers agreed at journal 25 Jun, 2024 Reviewers agreed at journal 24 Jun, 2024 Reviewers agreed at journal 04 Jun, 2024 Reviewers invited by journal 30 May, 2024 Editor assigned by journal 29 May, 2024 Editor invited by journal 26 Mar, 2024 Submission checks completed at journal 26 Mar, 2024 First submitted to journal 14 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-4097602","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":283915501,"identity":"d499b2b3-b267-480b-b209-e79f8bcb9f1d","order_by":0,"name":"Fang Wang","email":"","orcid":"","institution":"Zhejiang Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Wang","suffix":""},{"id":283915502,"identity":"1af4df76-1fa1-4824-ae9d-6d9a94b3f734","order_by":1,"name":"Wujie Chen","email":"","orcid":"","institution":"Zhejiang Cancer 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yanjun","middleName":"","lastName":"Xu","suffix":""},{"id":283915508,"identity":"d99e10b6-2fa9-42f1-b616-1732b33e8f6f","order_by":5,"name":"Min Fang","email":"","orcid":"","institution":"Zhejiang Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Fang","suffix":""},{"id":283915510,"identity":"84587458-83a1-480c-ae1d-536d2f409024","order_by":6,"name":"Haitao Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBAC9mYgwdjAIMfAcIBILYwQLQbGJGhpgGhJbCDaYYztzM8eft3xJ31+4+GHHxgq7tk1sJ/Fbx9jM5u5sewZg9zGhmPGEgxnipMbePISCGhhMJOWbDPIbWY4w8bA2JaQzCDBY0BAC/s3kJZ0NqK1CDbzmEl+bDNI4IFqsSOoRZqZp0yasc3YcAYD0C8JZxIS2Hhy8Gvh4z++TfJnm5y8/AxgiH2oSLDnZz+DXwsIMPOASIkDDAwJDAyJbQTVAwHjDxDJ3wDm2BOjYxSMglEwCkYWAAAtkT21f72QvwAAAABJRU5ErkJggg==","orcid":"","institution":"Zhejiang Cancer Hospital","correspondingAuthor":true,"prefix":"","firstName":"Haitao","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2024-03-14 05:58:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4097602/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4097602/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-73331-w","type":"published","date":"2024-09-30T15:57:43+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53877326,"identity":"0852b633-7f1e-4df2-8812-b3d1ed6d7672","added_by":"auto","created_at":"2024-04-01 16:44:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31508,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4097602/v1/47b9ab062144d1df92d313a0.png"},{"id":53875691,"identity":"dfedf67b-77fe-4d13-9163-3c969a754938","added_by":"auto","created_at":"2024-04-01 16:36:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":19323,"visible":true,"origin":"","legend":"\u003cp\u003eCombined nomogram\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4097602/v1/72f061606f1e5a080abd96dd.png"},{"id":53875694,"identity":"923ef28a-2591-4f34-aee6-7c37c70cfd02","added_by":"auto","created_at":"2024-04-01 16:36:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":22930,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival analysis of patients with two risk groups and differential analysis in\u003c/p\u003e\n\u003cp\u003esurvival status The Figure showed the Kaplan-Meier curves of two risk groups in the training set (a) and validation set (b)\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4097602/v1/e14bf17371683da8a32d4db1.png"},{"id":66097094,"identity":"d237bf3c-ef10-473d-ad9b-dc841b86e4f5","added_by":"auto","created_at":"2024-10-07 16:13:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":699844,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4097602/v1/1ca40e6b-1127-4fd3-8a0b-d2ca65672af0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk Stratification and Overall Survival Prediction in extensive stage Small Cell Lung Cancer after chemotherapy with immunotherapy Based on CT Radiomics","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSmall cell lung cancer (SCLC) is an aggressive neuroendocrine malignancy, accounting for 10%-15% of all lung cancers, and is commonly associated with smoking [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Most patients present in an extensive stage with multiple metastases at the time of diagnosis, and the prognosis is often extremely poor, with a 5-year survival rate of only 6.3% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The TNM staging according to the AJCC guidelines was used to guide clinical decision-making and prognosis evaluation. However, a previous study [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] had shown that the applicability of TNM staging to assess extensive-stage small-cell lung cancer (ES-SCLC) prognosis in clinical practice remains uncertain. Therefore, it is necessary to search for more reliable biomarkers to predict the prognosis of ES-SCLC, to accurately guide clinical treatment.\u003c/p\u003e \u003cp\u003eIn recent years, there have been many applied studies on efficacy prediction and the prognosis assessment of radiomics. Many studies [\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8 CR9\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] have shown that radiomics has a good application value in predicting immunotherapy efficacy with different types of cancer. Yet, the number of studies describing using radiomics to predict the outcomes of SCLC are few. Herein, a machine learning algorithm was performed on pre-treatment CT images to extract radiomics features. These results were then combined with clinical features to construct a combined model to predict the prognosis of patients with ES-SCLC after first-line chemotherapy with immunotherapy.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eA total of 111 patients with initial ES-SCLC in our hospital from December 2019 to December 2021 were retrospectively collected. Inclusion criteria: (1) patients were pathologically or cytologically diagnosed with ES-SCLC; (2) received at least 2 doses of chemoimmunotherapy as first line treatment therapy;(3) CT images before treatment; (4) With follow-up. Exclusion criteria: (1) received any other anti-tumor therapy before immunotherapy; (2) No CT images before treatment; (3) patients without progression during follow up. Finally, a total of 93 cases were included in this study. The immunotherapy regimen was durvalumab or atezolizumab. And the chemotherapy regimen was platinum etoposide.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCT scanning protocol\u003c/h2\u003e \u003cp\u003eThe CT scanning equipment used in our hospital included GE, Siemens and Philips models; Tube voltage was120KV, tube current: GE: 20-140mAs; Siemens: 110mAs; Philips: 101mAs; Collimator: GE and Philips: 0.624x64MM; Siemens: 0.6x128MM; Matrix: GE and Siemens: 512x512; Philips: 1024x1024; Pitch: GE:1.375P; Siemens: 1.2P; Philips: 1.0P; Tube speed: GE:0.4s; Siemens and Philips: 0.5s. During the examination, the patient was supine, and the scan was conducted during the end of deep inspiration. The visual range ranged from the thoracic entrance to the level below the costophrenic angle. The layer thickness was 5mm, and the pitch was 5mm. Prior to scanning, 85ml-95ml of CT contrast agent was injected into the cubital vein with a high pressure syringe at a rate of 3.0ml/s. The scans were performed 60 seconds after the injection, and the scans were performed in one phase.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSegmentation of the tumor\u003c/h2\u003e \u003cp\u003eThe chest CT images were imported into ITK-SNAP (version 3.8.0, USA) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The window position used was the enhanced mediastinal window, which could show a good boundary between the lesion and the surrounding large blood vessels, obstructive atelectasis or obstructive pneumonia. An outline was made along the inner boundary of the tumor focus, and the entire tumor focus was outlined to obtain the 3D-ROI. The delineation of ROI was carried out by two cardiothoracic diagnostic physicians alone with more than 5 years of experience blinded to the outcome of study patients. In the delineation process, the trachea, vasculature, distal obstructive atelectasis and regions of pneumonia were avoided.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eFeature extraction and screening\u003c/h2\u003e \u003cp\u003epython (3.7) software was used to extract features based on pyradiomics conforming to IBSI standard [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The steps of feature screening were as follows: 1) Outliers in data are replaced by median values; 2) Conducted Z-Score standardization for each characteristic data value. For feature dimension reduction, MRMR and least absolute shrinkage and selection operator (LASSO) method were used to select the most predictive radiomics features. The LASSO-cox regression analysis model was used to construct the radiomics survival analysis model, and the Radscore was calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData analysis and model construction\u003c/h2\u003e \u003cp\u003eAll patients were randomly divided into the training set and validation set according to the ratio 7:3. Univariate cox regression was used to select significant predictors of radiomics characteristics (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Correlation analysis was performed among features.\u003c/p\u003e \u003cp\u003eUnivariate cox regression analysis model was used to screen out statistically significant predictors of clinical features (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multiple Cox regression analyses were performed to the construct the combined model based on the features retained by Radscore and clinical features.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eSPSS 26.0 was used for statistical analysis, and classified data were compared by Chi-square test or Fisher exact probability method. Quantitative data were first tested for normality.Quantitative data with a normal distribution were represented by mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and compared by an independent sample t-test.Quantitative data without the normal distribution were compared by Mann-Whitney U test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated statistically significant differences.\u003c/p\u003e \u003cp\u003eModel verification: C-index and KM curve (Log-Rank) were used for survival analysis. The research process was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA medical image acquisition, B Segmentation of the tumor, C Feature extraction, D Features selection, E Model construction, F Model verification\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eClinical features\u003c/h2\u003e \u003cp\u003eA total of 93 cases were included, which were divided into training set (n\u0026thinsp;=\u0026thinsp;66) and validation set (n\u0026thinsp;=\u0026thinsp;27). Two groups with an average age of 64.1 (\u0026plusmn;6.9) and 62.7 (\u0026plusmn;6.8) were obtained. There were 62 males (93.9%) and 4 females (6.1%) in the training set. There were 26 males (96.3%) and 1 female (3.7%) in the validation set. There were no statistically significant differences in clinical features between the training and validation sets (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In addition, univariate cox regression analysis showed that the risk ratio of the number of metastatic lesions in the training set was 2.43 (p\u0026thinsp;=\u0026thinsp;0.007), which was statistically correlated with OS. The count of metastatic lesions(transfer-ct.) was referred to the total number of single-organ metastases or multi-organ metastases lesions. Groups were divided by whether they had more than, or less than/equal to 5 metastatic lesions.\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\u003eThere was no significant difference in clinical features between the training and test set\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining group(n\u0026thinsp;=\u0026thinsp;66)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTest group(n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\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\u003egender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62(93.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26(96.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4(6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1(3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.647\u003c/p\u003e \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\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64.1(6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.7(6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9(13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5(18.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7(25.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16(24.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4(14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19(28.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11(40.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2(3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3(4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2(7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13(19.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48(72.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16(59.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5(7.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2(7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61(92.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25(92.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16(24.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5(18.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50(75.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22(81.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ediabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56(84.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26(96.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10(15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1(3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52(78.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23(85.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14(21.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4(14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart-disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65(98.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26(96.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1(1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1(3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline-lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63(95.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26(96.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3(4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1(3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatitis-B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63(95.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25(92.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3(4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2(7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elocation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eleft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29(43.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15(55.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eright\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37(56.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12(44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eliver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43(65.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19(70.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23(34.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8(29.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43(65.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17(63.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23(34.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10(37.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebrain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53(80.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25(92.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13(19.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2(7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57(86.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19(70.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9(13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8(29.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epleural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59(89.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20(74.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7(10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7(25.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdrenal-glands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55(83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23(85.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11(16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4(14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistant-lymph-node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56(84.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22(81.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10(15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5(18.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransfer-ct.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32(48.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11(40.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34(51.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16(59.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFeature Selection and Radscore calculation\u003c/h2\u003e \u003cp\u003ePython software (3.7) was used to screen 1781 radiomics features extracted from CT enhanced mediastinal window sequences by LASSO-COX regression analysis, and 7 features were finally screened.\u003c/p\u003e \u003cp\u003eexponential_gldm_LargeDependenceHighGrayLevelEmphasis,\u003c/p\u003e \u003cp\u003ewavelet.LLH_firstorder_90Percentile,\u003c/p\u003e \u003cp\u003eexponential_gldm_SmallDependenceHighGrayLevelEmphasis,\u003c/p\u003e \u003cp\u003ewavelet.HHH_glrlm_LowGrayLevelRunEmphasis,\u003c/p\u003e \u003cp\u003ewavelet.HHH_firstorder_Median,\u003c/p\u003e \u003cp\u003eoriginal_gldm_LargeDependenceHighGrayLevelEmphasis,\u003c/p\u003e \u003cp\u003ewavelet.HHH_glcm_Correlation.\u003c/p\u003e \u003cp\u003eThe corresponding radscore was calculated according to the coefficients of the radiomics features.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eClinical Predictors\u003c/h2\u003e \u003cp\u003eUnivariate cox regression analysis showed that the count of metastatic lesions(transfer-ct.) was an independent prognostic factor (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), those with more than 5 metastatic lesions were considered a high-risk group, while those with less than 5 fit into the low-risk group. A multivariate cox regression analysis showed that the Radscore was an independent risk factor (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\u003ePreoperative clinical risk factors for OS in patients with SCLC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariable analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariable analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\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 \u003cp\u003e1.000(0.950\u0026thinsp;\u0026minus;\u0026thinsp;1.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.564(0.135\u0026thinsp;\u0026minus;\u0026thinsp;2.350)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.010(0.541\u0026thinsp;\u0026minus;\u0026thinsp;1.889)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.177 (0.866\u0026thinsp;\u0026minus;\u0026thinsp;1.600)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.211 (0.722\u0026thinsp;\u0026minus;\u0026thinsp;2.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.663(0.187\u0026thinsp;\u0026minus;\u0026thinsp;2.071)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.692(0.344\u0026thinsp;\u0026minus;\u0026thinsp;1.392)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.155 (0.530\u0026thinsp;\u0026minus;\u0026thinsp;2.519)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.719 (0.330\u0026thinsp;\u0026minus;\u0026thinsp;1.566)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.992(0.884\u0026thinsp;\u0026minus;\u0026thinsp;55.320)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline-lung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.414(0.056\u0026thinsp;\u0026minus;\u0026thinsp;3.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000(0.000\u0026thinsp;\u0026minus;\u0026thinsp;Inf)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.618 (0.866\u0026thinsp;\u0026minus;\u0026thinsp;3.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.692(0.895\u0026thinsp;\u0026minus;\u0026thinsp;3.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.134(0.535\u0026thinsp;\u0026minus;\u0026thinsp;2.401)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.432(0.133\u0026thinsp;\u0026minus;\u0026thinsp;1.406)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePleural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.421(0.590\u0026thinsp;\u0026minus;\u0026thinsp;3.420)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdrenal-glands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.839(0.327\u0026thinsp;\u0026minus;\u0026thinsp;2.152)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistant-lymph-node\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.003(0.420\u0026thinsp;\u0026minus;\u0026thinsp;2.396)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.229(0.856\u0026thinsp;\u0026minus;\u0026thinsp;5.807)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransfer-ct.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.431 (1.271\u0026thinsp;\u0026minus;\u0026thinsp;4.650)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.007*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e1.911(0.984\u0026thinsp;\u0026minus;\u0026thinsp;3.710)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.056*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProgress-mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.003(0.509\u0026thinsp;\u0026minus;\u0026thinsp;1.979)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadscore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.755(3.008\u0026thinsp;\u0026minus;\u0026thinsp;15.170)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e5.888(2.574\u0026thinsp;\u0026minus;\u0026thinsp;13.470)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment of a combined model with radiomics and clinical features\u003c/h2\u003e \u003cp\u003eAn alignment Diagram model was constructed by combining the radscore and the number of metastatic lesions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The KM curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and the Log-rank test showed that the combined model based on radiomics and clinical features could predict the OS of ES-SCLC after first-line chemotherapy and immunotherapy in both the training set (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and the validation set (p\u0026thinsp;=\u0026thinsp;0.00042). The C-index of the line graph model was 0.722 (se\u0026thinsp;=\u0026thinsp;0.044) in the training set and 0.68 (se\u0026thinsp;=\u0026thinsp;0.074) in the validation set, respectively.\u003c/p\u003e \u003cp\u003eThe nomogram of survival combined model. The nomogram showed the clinical application of the survival combined model. The red highlights showed that the total point of the patient was 97 and the probability of survival time of the patient less than 11 months was 0.6.\u003c/p\u003e \u003cp\u003e***means P value\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, a combined model based on the Radscore selected from radiomics features and the number of metastatic lesions selected from clinical features was constructed to predict the OS for ES-SCLC after chemotherapy and immunotherapy. The test results of the C-index and the KM curve were satisfactory, which indicated that the model established in this study can objectively and accurately stratify the survival of ES-SCLC patients.\u003c/p\u003e \u003cp\u003eChen et al [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] reported on the value of radiomics for progression-free survival prediction for ES-SCLC after chemotherapy with etoposide and cisplatin. 5 and 6 radiomics features were extracted from the lung window and the enhanced mediastinal window, respectively. The C-index of the model constructed with 11 features was 0.7531 and the average C/D AUC was 0.8487 in the validation set, which was greater than that of the model constructed with lung window (C-index 0.6951, the mean C/D AUC was 0.7836) and enhanced mediastinal window (C-index was 0.7192, the mean C/D AUC was 0.7964). Another study [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] used radiomics to predict the efficacy of platinum-based chemotherapy and found an OS of 153 SCLC patients with lung window feature modeling. The results showed that the radiomics risk score was correlated with the OS, with a C-index of 0.72 in training set and 0.69 in validation set. The results of the two studies showed that the effectiveness of the lung window feature model was essentially identical, and the C-index of the validation set was 0.69. In this study, the radiomics features of the enhanced mediastinal window were extracted, and the C-index of the combined model established with the clinical features was 0.722 in the training set and 0.68 in the validation set, respectively. These results were similar to what was reported above.\u003c/p\u003e \u003cp\u003eChen et al. showed that the predictive value of the model constructed by the radiomics features of different window positions was higher than that of the radiomics model with a single window position. According to the correlation study between pathological and radiomics features [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], plain CT images reflected the uneven tissue and cell density caused by necrosis, bleeding, and degeneration inside the tumor. The enriched and deficient blood supply areas in the enhanced scan images reflect the heterogeneity of blood supply vessels in the tumor. The heterogeneity within the tumor was translated into a quantitative expression of radiomics pixel density and distribution characteristics. Therefore, the radiomics features acquired from both the plain sequence and the enhanced sequence is greater than that of a single sequence, and the combination of multiple sequences was needed for disease diagnosis in practice. It was suggested that the multi-sequence radiomics features can be extracted for modeling in subsequent studies, and the differences with the single sequence model can be analyzed statistically.\u003c/p\u003e \u003cp\u003eThis study was the first to use radiomics to predict the prognosis for SCLC after first-line chemotherapy plus immunotherapy, which is unique from previous radiomics studies which predicted prognosis after chemotherapy was used alone. The C-index of this model was noted to be slightly different from other models examining only conventional chemotherapy.\u003c/p\u003e \u003cp\u003eThere was also a limited amount of literature that reports on the application of radiomics in predicting the efficacy of immunotherapy. The study of melanoma indicated that radiomics has a certain value in predicting the efficacy of first-line immunotherapy [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. By combining radiomics and clinical characteristics, a more realistic prognostic prediction model was constructed to accurately predict the survival rate of patients with ES-SCLC after first-line immunotherapy plus chemotherapy.\u003c/p\u003e \u003cp\u003eTraditionally, the prognosis of SCLC was mainly based on the TNM staging of AJCC. However, in this study, the T stage, N stage and M stage were not independent predictors of OS in ES-SCLC. Rather, only the number of metastases in the M1 stage was an independent factor, which was consistent with the results of previous literature [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In this study, the patients were divided into two subgroups based on those which had more than, or less than 5 metastatic lesions. The greater the number of metastatic lesions, the worse the prognosis, which was consistent with clinical practice.\u003c/p\u003e \u003cp\u003eThis study did not find any evidence to suggest that smoking is an independent risk factor for OS in SCLC. In addition, the imbalance of male to female participants in this study may add a confounding variable. However, it has been well-documented that brain metastasis is a poor prognostic indicator [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Prophylactic whole brain irradiation (PCI) has been shown to reduce the incidence of brain metastasis and improve prognosis. In this study, some patients received PCI independently. As such, brain metastasis had no overall correlation with OS. Several studies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] have reported that thoracic radiotherapy could improve the OS of ES-SCLC. Some patients in this study cohort received chest radiotherapy after first-line immunotherapy plus chemotherapy. Therefore, the value of the combined radiomics model in this study may be affected by the treatment strategies that were used.\u003c/p\u003e \u003cp\u003eThere are several limitations to this study. Firstly, as a single-center retrospective study, the design is susceptible to selective bias. The number of cases in this study is also relatively small for AI machine learning. A better prediction model may be necessary by expanding the cohort. Finally, the gender ratio of this study biases males due to the demographic of SCLC. A more balanced study may be required to draw definite conclusions.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eHerein, this study determined that radiomics information combined with the clinical examination was able to predict the OS of patients objectively and accurately with ES-SCLC after first-line immunotherapy plus chemotherapy. The radiomics nomogram used in this study may help guide treatment strategy by identifying key prognostic indicators for the overall outcome.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDATA AVAILABILITY STATEMENT\u003c/h2\u003e \u003cp\u003eThe original data could be provided on reasonable request from the authors. Requests to access these datasets should be directed to HT, [email protected].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eETHICS STATEMENT\u003c/h2\u003e \u003cp\u003e The studies involving human participants were reviewed and approved by the ethics committees of *** Hospital. Written informed consent for participation was waived in accordance with the national legislation and the institutional requirements.\u003c/p\u003e \u003c/div\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConception and design: HT, FW, FMAcquisition of data: FW, WJ, JL, FM, YJData analysis and interpretation: FW, HTWriting and drafting the revising: FW, HT, FMprepared figures and tables: FW,HT\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRiaz SP, L\u0026uuml;chtenborg M, Coupland VH, Spicer J, Peake MD, M\u0026oslash;ller H. Lung Cancer - Small Cell: Statistics [Webpage on the Internet]. 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Prognostic value of a nomogram based on the dynamic albumin-to-alkaline phosphatase ratio for patients with extensive-stage small-cell lung cancer[J]. Onco Targets Ther, 2020, 13:9043\u0026ndash;9057. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/OTT.S262084\u003c/span\u003e\u003cspan address=\"10.2147/OTT.S262084\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"small cell lung cancer, overall survival, prediction model, radiomics, computed tomography","lastPublishedDoi":"10.21203/rs.3.rs-4097602/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4097602/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003ePurpose\u003c/b\u003e The prognosis of extensive-stage small cell lung cancer is usually poor. In this study, a combined model based on pre-treatment CT radiomics and clinical features was constructed to predict the OS of extensive-stage small cell lung cancer after chemotherapy with immunotherapy.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e Clinical data of 111 patients with extensive stage small-cell lung cancer who received first-line immunotherapy combined with chemotherapy in our hospital from December 2019 to December 2021 were retrospectively collected. Finally, 93 patients were selected for inclusion in the study, and CT images were obtained through PACS system before treatment. All patients were randomly divided into a training set (n\u0026thinsp;=\u0026thinsp;66) and a validation set (n\u0026thinsp;=\u0026thinsp;27). Images were imported into ITK-SNAP to outline areas of interest, and Python software was used to extract radiomics features. A total of 1781 radiomics features were extracted from each patient's images. The feature dimensions were reduced by MRMR and LASSO methods, and the radiomics features with the greatest predictive value were screened. The weight coefficient of radiomics features was calculated, and the linear combination of the feature parameters and the weight coefficient was used to calculate Radscore. Univariate cox regression analysis was used to screen out the factors significantly associated with prognosis from the radiomics and clinical features, and multivariate cox regression analysis was performed to establish the prognosis prediction model of extensive stage small cell lung cancer.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e The degree of metastases was selected as a significant clinical prognostic factor by univariate cox regression analysis. Seven radiomics features with significance were selected by LASSO-COX regression analysis, and the Radscore was calculated according to the coefficient of the radiomics features. An alignment diagram survival prediction model was constructed by combining Radscore with the number of metastatic lesions. The study population was stratified into those who survived less than 11 months, and those with a greater than 11 month survival. The C-index was 0.722 (se\u0026thinsp;=\u0026thinsp;0.044) and 0.68(se\u0026thinsp;=\u0026thinsp;0.074) in the training and the validation sets, respectively. The Log_rank test results of the combination model were as follows: training set: p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, validation set: p\u0026thinsp;=\u0026thinsp;0.00042.\u003c/p\u003e \u003cp\u003eConclusion\u003c/p\u003e \u003cp\u003eIn this study, a combined model based on radiomics and clinical features could predict OS in patients with extensive stage small cell lung cancer after chemotherapy with immunotherapy, which could help guide clinical treatment strategies.\u003c/p\u003e","manuscriptTitle":"Risk Stratification and Overall Survival Prediction in extensive stage Small Cell Lung Cancer after chemotherapy with immunotherapy Based on CT Radiomics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-01 16:36:13","doi":"10.21203/rs.3.rs-4097602/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-17T11:05:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-13T13:32:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-26T02:32:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172870629535297308049241982593207351682","date":"2024-06-25T04:26:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"88976773040833569400227678288895405590","date":"2024-06-24T11:24:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242393607852538141085720014532434647949","date":"2024-06-04T18:47:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-30T18:47:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-29T13:07:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-26T05:46:30+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-26T05:44:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-03-14T05:57:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7a64b42d-12ae-488f-9ce6-47d80e65f4af","owner":[],"postedDate":"April 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":29885370,"name":"Biological sciences/Cancer"},{"id":29885371,"name":"Biological sciences/Immunology"}],"tags":[],"updatedAt":"2024-10-07T16:07:41+00:00","versionOfRecord":{"articleIdentity":"rs-4097602","link":"https://doi.org/10.1038/s41598-024-73331-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-09-30 15:57:43","publishedOnDateReadable":"September 30th, 2024"},"versionCreatedAt":"2024-04-01 16:36:13","video":"","vorDoi":"10.1038/s41598-024-73331-w","vorDoiUrl":"https://doi.org/10.1038/s41598-024-73331-w","workflowStages":[]},"version":"v1","identity":"rs-4097602","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4097602","identity":"rs-4097602","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}