ArticleCell reports. Medicine2021
Predictive models of response to neoadjuvant chemotherapy in muscle-invasive bladder cancer using nuclear morphology and tissue architecture.
Article in Cell reports. Medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Serial H&E Pathology Reveals Progression-Associated Nuclear Size Drift as a Longitudinal Digital Readout of Bladder Cancer Evolution.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Proliferation- and cytotoxic immune signatures identify chemotherapy-responsive bladder tumors in a molecular subtype dependent manner.NPJ precision oncology · 2026Article
- Machine learning-based integration of transcriptome and digital pathology for predicting chemoresistance in muscle-invasive bladder cancer.Experimental & molecular medicine · 2026Article
- PETIL: Predicting Expansion of Tumor Infiltrating Lymphocytes for the Adoptive Cell Immunotherapy in Bladder Cancers.bioRxiv : the preprint server for biology · 2026Article
- Article
- Spatial proteomics in precision medicine: technologies, bioinformatics, and translational applications.Precision clinical medicine · 2026Review
- A novel spatial framework to validate arsenic exposure gene expression profiling in bladder cancer using multiplex FISH and AI-powered digital pathology.Scientific reports · 2025Article
- Noninvasive prognostic classification of ITH in HCC with multi-omics insights and therapeutic implications.Science advances · 2025Article
- Predicting response to neoadjuvant chemotherapy in muscle-invasive bladder cancer via interpretable multimodal deep learning.NPJ digital medicine · 2025Article
- 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.EClinicalMedicine · 2025Article
- Spatial Architecture of Single-Cell and Vasculature in Tumor Microenvironment Predicts Clinical Outcomes in Triple-Negative Breast Cancer.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2025Article
- The clinical application of artificial intelligence in cancer precision treatment.Journal of translational medicine · 2025Review
- Investigating lung cancer microenvironment from cell segmentation of pathological image and its application in prognostic stratification.Scientific reports · 2025Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Artificial intelligence for predicting response to neoadjuvant chemotherapy for bladder cancer A comprehensive systematic review and meta-analysis.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2024Review
- Enhanced Immunohistochemistry Interpretation with a Machine Learning-Based Expert System.Diagnostics (Basel, Switzerland) · 2024Article
- Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology.Briefings in bioinformatics · 2024Review
- Article
- MIM-CyCIF: masked imaging modeling for enhancing cyclic immunofluorescence (CyCIF) with panel reduction and imputation.Communications biology · 2024Article
- Mapping cancer biology in space: applications and perspectives on spatial omics for oncology.Molecular cancer · 2024Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Characterizing likelihood of response to neoadjuvant chemotherapy (NAC) in muscle-invasive bladder cancer (MIBC) is an important yet unmet challenge. In this study, a machine-learning framework is developed using imaging of biopsy pathology specimens to generate models of likelihood of NAC response. Developed using cross-validation (evaluable N = 66) and an independent validation cohort (evaluable N = 56), our models achieve promising results (65%-73% accuracy). Interestingly, one model-using features derived from hematoxylin and eosin (H&E)-stained tissues in conjunction with clinico-demographic features-is able to stratify the cohort into likely responders in cross-validation and the validation cohort (response rate of 65% for predicted responder compared with the 41% baseline response rate in the validation cohort). The results suggest that computational approaches applied to routine pathology specimens of MIBC can capture differences between responders and non-responders to NAC and should therefore be considered in the future design of precision oncology for MIBC.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.