Predictive models of response to neoadjuvant chemotherapy in muscle-invasive bladder cancer using nuclear morphology and tissue architecture

Cell Reports Medicine

Trinity J. Bivalacqua¹  Roland Seiler²  Haoyang Mi³  Max Kates⁴  Alexander S. Baras⁵  Aleksander S. Popel⁶  Peter C. Black⁷ 

[1] Corresponding author;James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA;Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA;Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA;Department of Urologic Sciences, University of British Columbia Faculty of Medicine, Vancouver, BC, Canada;Department of Urology, University Hospital Bern, Bern, Switzerland;James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA;

关键词: machine learning;    bladder cancer;    neoadjuvant;    chemotherapy;    image processing;    digital pathology;   

DOI  :  

来源: DOAJ

【 摘 要 】

Summary: 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.

【 授权许可】

Unknown