<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>81</volume><submitter>Jiang F</submitter><pubmed_abstract>&lt;h4>Background&lt;/h4>Accurate prediction of early recurrence is essential for disease management of patients with non-muscle-invasive bladder cancer (NMIBC). We aimed to develop and validate a deep learning-based early recurrence predictive model (ERPM) and a treatment response predictive model (TRPM) on whole slide images to assist clinical decision making.&lt;h4>Methods&lt;/h4>In this retrospective, multicentre study, we included consecutive patients with pathology-confirmed NMIBC who underwent transurethral resection of bladder tumour from five centres. Patients from one hospital (Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China) were assigned to training and internal validation cohorts, and patients from four other hospitals (the Third Affiliated Hospital of Sun Yat-se</pubmed_abstract><journal>EClinicalMedicine</journal><pagination>103125</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11909458</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Deep learning-based model for prediction of early recurrence and therapy response on whole slide images in non-muscle-invasive bladder cancer: a retrospective, multicentre study.</pubmed_title><pmcid>PMC11909458</pmcid><pubmed_authors>Yang H</pubmed_authors><pubmed_authors>Xie Y</pubmed_authors><pubmed_authors>Jiang F</pubmed_authors><pubmed_authors>Lin T</pubmed_authors><pubmed_authors>Hong G</pubmed_authors><pubmed_authors>Lin Z</pubmed_authors><pubmed_authors>Wang H</pubmed_authors><pubmed_authors>Liu Y</pubmed_authors><pubmed_authors>Zhu M</pubmed_authors><pubmed_authors>Huang S</pubmed_authors><pubmed_authors>Xu A</pubmed_authors><pubmed_authors>Wu S</pubmed_authors><pubmed_authors>Zeng H</pubmed_authors><pubmed_authors>Luo Y</pubmed_authors><pubmed_authors>Shen R</pubmed_authors><pubmed_authors>Wang Y</pubmed_authors><pubmed_authors>Chen R</pubmed_authors></additional><is_claimable>false</is_claimable><name>Deep learning-based model for prediction of early recurrence and therapy response on whole slide images in non-muscle-invasive bladder cancer: a retrospective, multicentre study.</name><description>&lt;h4>Background&lt;/h4>Accurate prediction of early recurrence is essential for disease management of patients with non-muscle-invasive bladder cancer (NMIBC). We aimed to develop and validate a deep learning-based early recurrence predictive model (ERPM) and a treatment response predictive model (TRPM) on whole slide images to assist clinical decision making.&lt;h4>Methods&lt;/h4>In this retrospective, multicentre study, we included consecutive patients with pathology-confirmed NMIBC who underwent transurethral resection of bladder tumour from five centres. Patients from one hospital (Sun Yat-sen Memorial Hospital of Sun Yat-sen University, Guangzhou, China) were assigned to training and internal validation cohorts, and patients from four other hospitals (the Third Affiliated Hospital of Sun Yat-se</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Mar</publication><modification>2025-04-20T00:11:37.981Z</modification><creation>2025-04-20T00:11:37.981Z</creation></dates><accession>S-EPMC11909458</accession><cross_references><pubmed>40093987</pubmed><doi>10.1016/j.eclinm.2025.103125</doi></cross_references></HashMap>