Development and Preliminary Validation of RP-WX: A WeChat Mini- Program-Based Prediction Model for Radiation Pneumonitis in Patients Undergoing Concurrent Chemoradiotherapy for Locally Advanced Squamous Cell Lung Cancer

Development and Preliminary Validation of RP-WX: A WeChat Mini- Program-Based Prediction Model for Radiation Pneumonitis in Patients Undergoing Concurrent Chemoradiotherapy for Locally Advanced Squamous Cell Lung Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and Preliminary Validation of RP-WX: A WeChat Mini- Program-Based Prediction Model for Radiation Pneumonitis in Patients Undergoing Concurrent Chemoradiotherapy for Locally Advanced Squamous Cell Lung Cancer Jianqiang Fang, Xi’an Xiong, Wei Tian, Qianxi Ni, Xiadong Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9079457/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 16 You are reading this latest preprint version Abstract Background Predicting the development of grade II or higher radiation pneumonitis in locally advanced squamous cell lung cancer (LASCLC) patients prior to concurrent chemoradiotherapy remains challenging, as traditional indicators based on dose-volume histograms (DVHs) or biological markers typically lack data or assessed post-treatment. In this study, we explored the potential of utilizing multi-omics (radiomics, dosimetric, clinical, and radiobiology features) as novel biomarkers to predict the Radiation pneumonitis of grade 2 or higher (RP2+) in LASCLC patients undergoing concurrent chemoradiotherapy. Methods A total of 129 patients with locally advanced small cell lung cancer (LASCLC) from four institutions were included in the training and validation cohort, with an additional 34 patients allocated to an independent test set. Four distinct feature categories—radiomics, dosimetry, clinical, and radiobiological—were employed to develop and validate the predictive model. A four-step feature selection algorithm was applied for dimensionality reduction. The three machine learning algorithms demonstrating the highest predictive performance were integrated into an ensemble model. Model interpretability was achieved using Shapley Additive Explanations (SHAP) values. Finally, a user-friendly graphical user interface (GUI) was developed to facilitate clinical translation. Findings: RP2 + occurred in 51.3% of enrolled patients. Univariate analysis revealed statistically significant differences between RP2 + and non-RP2 + patients in smoking status, radiotherapy position (RTP), Lungs_V5, PTV volume, Heart_V30, LEUD(=0.3)_SICK, LEUD(=0.3)_TOTAL, NTCP_LEUD_SICK, and NTCP_LKB_SICK. Nine features—comprising three dosimetric, three radiomic, and three radiobiological variables—were ultimately selected for model training and validation. Across all nine machine learning algorithms, four features consistently demonstrated strong predictive performance for RP2+: two dosimetric parameters (Lung_V5 and Lung_V20), one radiobiological metric (NTCP_LEUD_SICK), and one radiomic feature (glcm_InverseVariance_PGTV), each achieving mean AUC values > 0.70. The combined radiomic and radiobiological signature (RM + RB) yielded the highest model generalization accuracy (MGA), exceeding 0.92 across all three ensemble models, closely followed by the radiobiological-only model (MGA > 0.90). Within the RM + RB signature, three features contributed positively and three negatively to RP2 + prediction. Notably, lower SHAP values for NTCP_LEUD_SICK were associated with a reduced probability of RP2+. Interpretation: A user-friendly graphical user interface (GUI) was developed to facilitate the clinical implementation of the predictive model, thereby supporting clinical decision-making in routine practice. Predicting Radiation Pneumonitis Locally Advanced Squamous Cell Lung Cancer Ensemble Machine Learning Graphical User Interface (GUI) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 30 Apr, 2026 Reviews received at journal 22 Apr, 2026 Reviews received at journal 19 Apr, 2026 Reviews received at journal 16 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviews received at journal 13 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor assigned by journal 06 Apr, 2026 Editor invited by journal 18 Mar, 2026 Submission checks completed at journal 18 Mar, 2026 First submitted to journal 17 Mar, 2026 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-9079457","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":620700455,"identity":"7213e4dd-fff9-43e7-a690-dbc2e873a2b4","order_by":0,"name":"Jianqiang Fang","email":"","orcid":"","institution":"Jinhua Guangfu Oncology Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jianqiang","middleName":"","lastName":"Fang","suffix":""},{"id":620700456,"identity":"0171069b-8fcb-4460-bbfb-449fd4a10da7","order_by":1,"name":"Xi’an Xiong","email":"","orcid":"","institution":"Hunan Cancer Hospital \u0026 The Affiliated Cancer 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In this study, we explored the potential of utilizing multi-omics (radiomics, dosimetric, clinical, and radiobiology features) as novel biomarkers to predict the Radiation pneumonitis of grade 2 or higher (RP2+) in LASCLC patients undergoing concurrent chemoradiotherapy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 129 patients with locally advanced small cell lung cancer (LASCLC) from four institutions were included in the training and validation cohort, with an additional 34 patients allocated to an independent test set. Four distinct feature categories\u0026mdash;radiomics, dosimetry, clinical, and radiobiological\u0026mdash;were employed to develop and validate the predictive model. A four-step feature selection algorithm was applied for dimensionality reduction. The three machine learning algorithms demonstrating the highest predictive performance were integrated into an ensemble model. Model interpretability was achieved using Shapley Additive Explanations (SHAP) values. Finally, a user-friendly graphical user interface (GUI) was developed to facilitate clinical translation.\u003c/p\u003e\u003ch2\u003eFindings:\u003c/h2\u003e \u003cp\u003eRP2\u0026thinsp;+\u0026thinsp;occurred in 51.3% of enrolled patients. Univariate analysis revealed statistically significant differences between RP2\u0026thinsp;+\u0026thinsp;and non-RP2\u0026thinsp;+\u0026thinsp;patients in smoking status, radiotherapy position (RTP), Lungs_V5, PTV volume, Heart_V30, LEUD(=0.3)_SICK, LEUD(=0.3)_TOTAL, NTCP_LEUD_SICK, and NTCP_LKB_SICK. Nine features\u0026mdash;comprising three dosimetric, three radiomic, and three radiobiological variables\u0026mdash;were ultimately selected for model training and validation. Across all nine machine learning algorithms, four features consistently demonstrated strong predictive performance for RP2+: two dosimetric parameters (Lung_V5 and Lung_V20), one radiobiological metric (NTCP_LEUD_SICK), and one radiomic feature (glcm_InverseVariance_PGTV), each achieving mean AUC values \u0026gt;\u0026thinsp;0.70. The combined radiomic and radiobiological signature (RM\u0026thinsp;+\u0026thinsp;RB) yielded the highest model generalization accuracy (MGA), exceeding 0.92 across all three ensemble models, closely followed by the radiobiological-only model (MGA \u0026gt;\u0026thinsp;0.90). Within the RM\u0026thinsp;+\u0026thinsp;RB signature, three features contributed positively and three negatively to RP2\u0026thinsp;+\u0026thinsp;prediction. Notably, lower SHAP values for NTCP_LEUD_SICK were associated with a reduced probability of RP2+.\u003c/p\u003e\u003ch2\u003eInterpretation:\u003c/h2\u003e \u003cp\u003eA user-friendly graphical user interface (GUI) was developed to facilitate the clinical implementation of the predictive model, thereby supporting clinical decision-making in routine practice.\u003c/p\u003e","manuscriptTitle":"Development and Preliminary Validation of RP-WX: A WeChat Mini- Program-Based Prediction Model for Radiation Pneumonitis in Patients Undergoing Concurrent Chemoradiotherapy for Locally Advanced Squamous Cell Lung Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-13 06:21:57","doi":"10.21203/rs.3.rs-9079457/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-30T10:46:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T08:26:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-19T18:15:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-16T13:49:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249548993796176012379870254142911941454","date":"2026-04-15T00:18:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"182934694796009534943891398222615554468","date":"2026-04-14T23:47:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-13T06:21:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242016845919773108139423847768166514649","date":"2026-04-09T16:27:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"120601690732109193742931161843942115169","date":"2026-04-08T07:09:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154174855620888532240496422035755011735","date":"2026-04-08T00:47:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"802658194315819403721253125513590078","date":"2026-04-07T11:38:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-07T11:03:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-06T07:21:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-18T06:47:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-18T06:02:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2026-03-18T02:53:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9b95dea4-beca-4740-8224-f1baf7cac085","owner":[],"postedDate":"April 13th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-04-30T10:46:25+00:00","index":95,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T06:21:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-13 06:21:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9079457","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9079457","identity":"rs-9079457","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}