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

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....

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Bibliographic Details
Published in:Cell reports. Medicine 2021-09, Vol.2 (9), p.100382, Article 100382
Main Authors: Mi, Haoyang, Bivalacqua, Trinity J., Kates, Max, Seiler, Roland, Black, Peter C., Popel, Aleksander S., Baras, Alexander S.
Format: Article
Language:English
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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. [Display omitted] Using imaging of pathology samples to predict chemotherapy response in bladder cancerMulti-modal integration of cell nuclear and tissue architectural featuresModels using H&E images and basic clinical features able to enrich for respondersPredictive features suggest response-modulating factors in tumor microenvironment Using multi-modal machine-learning leveraging features from digital pathology, Mi et al. develop models to predict response to chemotherapy in muscle-invasive bladder cancer. Models using handcrafted features derived from conventional H&E TMAs in conjunction with basic clinico-demographic features significantly stratify likelihood of response in both discovery and independent validation cohorts.
ISSN:2666-3791
2666-3791
DOI:10.1016/j.xcrm.2021.100382