{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T17:07:15Z","timestamp":1787677635361,"version":"build-2784847793"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T00:00:00Z","timestamp":1761868800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82273373"],"award-info":[{"award-number":["82273373"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Radical cystectomy (RC) is the standard treatment for muscle-invasive and select high-risk non\u2013muscle-invasive bladder cancer. Despite definitive surgery, recurrence and progression remain major clinical concerns. Adjuvant chemotherapy and immunotherapy may improve outcomes, but therapeutic response varies due to tumor heterogeneity. Robust predictive models are needed to guide individualized treatment strategies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study retrospectively analyzed bladder cancer patients undergoing RC. Data included tumor morphology (e.g., vascular and perineural invasion), demographic variables (e.g., age, sex), and molecular markers (e.g., PD-L1, HER2, GATA3). LASSO regression identified key features, followed by model development using nine machine learning algorithms, including XGBoost and LightGBM. Model performance was assessed via area under the ROC curve (AUC), and Shapley Additive Explanations (SHAP) were used for model interpretability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The random forest model achieved the highest predictive performance (AUC = 0.92 in training; 0.74 in testing). SHAP analysis identified vascular invasion, perineural invasion, and PD-L1\/HER2 expression as major contributors. Decision curve analysis showed favorable net benefit within a moderate-risk threshold.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A machine learning model integrating pathological, demographic, and molecular features demonstrates promising potential to predict response to adjuvant therapy post-RC in bladder cancer. Decreased performance in the external test cohort highlights the need for further validation. Prospective studies incorporating multi-center and longitudinal data are warranted to enhance model generalizability and clinical applicability.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2025.1664965","type":"journal-article","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T05:25:02Z","timestamp":1761888302000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Explainable machine learning model predicts response to adjuvant therapy after radical cystectomy in bladder cancer"],"prefix":"10.3389","volume":"15","author":[{"given":"Jian","family":"Hou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Runlin","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yumin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanping","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junxiong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingbo","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pinyao","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,10,31]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.3390\/molecules28083436","article-title":"Altered glycosylation in progression and management of bladder cancer","volume":"28","author":"Wilczak","year":"2023","journal-title":"Molecules (Basel Switzerland)"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.21037\/tau.2019.06.01","article-title":"The evolving role of lymphadenectomy for bladder cancer: why, when, and how","volume":"9","author":"Packiam","year":"2020","journal-title":"Trans andrology Urol"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1016\/j.euf.2019.10.016","article-title":"Predicting response to neoadjuvant chemotherapy in bladder cancer","volume":"6","author":"Motterle","year":"2020","journal-title":"Eur Urol Focus"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocaa290","article-title":"Automated model versus treating physician for predicting survival time of patients with metastatic cancer","volume":"28","author":"Gensheimer","year":"2021","journal-title":"J Am Med Inf Assoc"},{"key":"B5","doi-asserted-by":"publisher","first-page":"112184","DOI":"10.1016\/j.intimp.2024.112184","article-title":"Integrated analysis of multiple transcriptomic approaches and machine learning integration algorithms reveals high endothelial venules as a prognostic immune-related biomarker in bladder cancer","volume":"136","author":"Zhang","year":"2024","journal-title":"Int Immunopharmacol"},{"key":"B6","doi-asserted-by":"publisher","first-page":"106818","DOI":"10.1016\/j.intimp.2020.106818","article-title":"Tumor mutational burden related classifier is predictive of response to PD-L1 blockade in locally advanced and metastatic urothelial carcinoma","volume":"87","author":"Wang","year":"2020","journal-title":"Int Immunopharmacol"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2024.1415584","article-title":"Construction of a risk prediction model for axillary lymph node metastasis in breast cancer based on gray-scale ultrasound and clinical pathological features","volume":"14","author":"Ouyang","year":"2024","journal-title":"Front Oncol"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1016\/j.crad.2023.07.020","article-title":"MRI-based radiomics analysis of bladder cancer: prediction of pathological grade and histological variant","volume":"78","author":"Wang","year":"2023","journal-title":"Clin Radiol"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1111\/bju.15379","article-title":"A panel of systemic inflammatory response biomarkers for outcome prediction in patients treated with radical cystectomy for urothelial carcinoma","volume":"129","author":"Schuettfort","year":"2022","journal-title":"Bju Int"},{"key":"B10","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1186\/s40001-025-02339-3","article-title":"Predicting survival in Malignant glioma using artificial intelligence","volume":"30","author":"Awuah","year":"2025","journal-title":"Eur J Med Res"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.21873\/anticanres.16281","article-title":"First-line chemotherapy response is associated with clinical outcome during immune checkpoint inhibitor treatment in advanced urothelial carcinoma: A real world retrospective study","volume":"43","author":"Li","year":"2023","journal-title":"Anticancer Res"},{"key":"B12","doi-asserted-by":"publisher","first-page":"14431","DOI":"10.1038\/s41598-024-65198-8","article-title":"Identification of bladder cancer subtypes and predictive signature for prognosis, immune features, and immunotherapy based on immune checkpoint genes","volume":"14","author":"Wu","year":"2024","journal-title":"Sci Rep"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.2217\/bmm-2020-0575","article-title":"Predictive biomarkers in bladder cancer","volume":"154","author":"Alkassis","year":"2021","journal-title":"Biomarkers Med"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2025.1536722","article-title":"The clinical significance of T cell infiltration and immune checkpoint expression in central nervous system germ cell tumors","volume":"16","author":"Zhou","year":"2025","journal-title":"Front Immunol"},{"key":"B15","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1007\/s00432-024-05777-4","article-title":"The role of tumor-associated macrophages in tumor immune evasion","volume":"150","author":"Huang","year":"2024","journal-title":"J Cancer Res Clin Oncol"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2019.01296","article-title":"Study progress of radiomics with machine learning for precision medicine in bladder cancer management","volume":"9","author":"Ge","year":"2019","journal-title":"Front Oncol"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1016\/j.euf.2021.04.007","article-title":"Artificial intelligence-based detection of FGFR3 mutational status directly from routine histology in bladder cancer: A possible preselection for molecular testing","volume":"8","author":"Loeffler","year":"2022","journal-title":"Eur Urol Focus"},{"key":"B18","doi-asserted-by":"publisher","first-page":"104176","DOI":"10.1016\/j.ijmedinf.2020.104176","article-title":"The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit","volume":"141","author":"Yuan","year":"2020","journal-title":"Int J Med Inf"},{"key":"B19","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1186\/s13054-019-2411-z","article-title":"Machine learning for the prediction of volume responsiveness in patients with oliguric acute kidney injury in critical care","volume":"23","author":"Zhang","year":"2019","journal-title":"Crit Care"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1097\/JS9.0000000000001866","article-title":"Prediction of sepsis among patients with major trauma using artificial intelligence: a multicenter validated cohort study","volume":"111","author":"Sun","year":"2025","journal-title":"Int J Surg"},{"key":"B21","doi-asserted-by":"publisher","first-page":"16057","DOI":"10.1038\/s41598-020-72685-1","article-title":"Machine learning prediction in cardiovascular diseases: a meta-analysis","volume":"10","author":"Krittanawong","year":"2020","journal-title":"Sci Rep"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2022.938801","article-title":"Machine learning-assisted prediction of pneumonia based on non-invasive measures","volume":"10","author":"Effah","year":"2022","journal-title":"Front Public Health"},{"key":"B23","doi-asserted-by":"publisher","first-page":"109896","DOI":"10.1016\/j.jclinane.2020.109896","article-title":"Predicting postoperative delirium after microvascular decompression surgery with machine learning","volume":"66","author":"Wang","year":"2020","journal-title":"J Clin Anesth"},{"key":"B24","doi-asserted-by":"publisher","first-page":"31","DOI":"10.3233\/THC-151071","article-title":"Machine learning models in breast cancer survival prediction","volume":"24","author":"Montazeri","year":"2016","journal-title":"Technol Health Care"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1016\/j.euo.2021.05.006","article-title":"Deep learning-based recalibration of the CUETO and EORTC prediction tools for recurrence and progression of non-muscle-invasive bladder cancer","volume":"5","author":"Jobczyk","year":"2022","journal-title":"Eur Urol Oncol"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.2174\/0115748936288453240124082031","article-title":"Integrated machine learning algorithms for stratification of patients with bladder cancerr","volume":"19","author":"He","year":"2024","journal-title":"Curr Bioinf"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.3390\/ijms22062800","article-title":"Improving anti-PD-1\/PD-L1 therapy for localized bladder cancer","volume":"22","author":"de Jong","year":"2021","journal-title":"Int J Mol Sci"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.3390\/ijms25126750","article-title":"Programmed cell death ligand 1 (PD-L1) immunohistochemical expression in advanced urothelial bladder carcinoma: an updated review with clinical and pathological implications","volume":"25","author":"German\u00e0","year":"2024","journal-title":"Int J Mol Sci"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.1016\/j.intimp.2018.12.018","article-title":"Evaluation of PD-L1 biomarker for immune checkpoint inhibitor (PD-1\/PD-L1 inhibitors) treatments for urothelial carcinoma patients: A meta-analysis","volume":"67","author":"Rui","year":"2019","journal-title":"Int Immunopharmacol"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.1002\/path.5306","article-title":"Bladder cancer, a unique model to understand cancer immunity and develop immunotherapy approaches","volume":"249","author":"Song","year":"2019","journal-title":"J Pathol"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1002\/ijc.34228","article-title":"The causal association between smoking, alcohol consumption and risk of bladder cancer: A univariable and multivariable Mendelian randomization study","volume":"151","author":"Xiong","year":"2022","journal-title":"Int J Cancer"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1002\/ijc.27508","article-title":"Influences of tobacco and alcohol use on hepatocellular carcinoma survival","volume":"131","author":"Shih","year":"2012","journal-title":"Int J Cancer"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2025.1664965\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T05:25:03Z","timestamp":1761888303000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2025.1664965\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,31]]},"references-count":32,"alternative-id":["10.3389\/fonc.2025.1664965"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2025.1664965","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,31]]},"article-number":"1664965"}}