Development and deployment of a histopathology-based deep learning algorithm for patient prescreening in a clinical trial

Albert Juan Ramon (), Chaitanya Parmar, Oscar M. Carrasco-Zevallos, Carlos Csiszer, Stephen S. F. Yip, Patricia Raciti, Nicole L. Stone, Spyros Triantos, Michelle M. Quiroz, Patrick Crowley, Ashita S. Batavia, Joel Greshock, Tommaso Mansi and Kristopher A. Standish
Additional contact information
Albert Juan Ramon: a Johnson & Johnson Company. Data Science and Digital Health
Chaitanya Parmar: a Johnson & Johnson Company. Data Science and Digital Health
Oscar M. Carrasco-Zevallos: a Johnson & Johnson Company. Data Science and Digital Health
Carlos Csiszer: a Johnson & Johnson Company. Data Science and Digital Health
Stephen S. F. Yip: a Johnson & Johnson Company. Data Science and Digital Health
Patricia Raciti: a Johnson & Johnson Company. Oncology
Nicole L. Stone: a Johnson & Johnson Company. Oncology
Spyros Triantos: a Johnson & Johnson Company. Oncology
Michelle M. Quiroz: a Johnson & Johnson Company. Oncology
Patrick Crowley: a Johnson & Johnson Company. Global Development
Ashita S. Batavia: a Johnson & Johnson Company. Data Science and Digital Health
Joel Greshock: a Johnson & Johnson Company. Data Science and Digital Health
Tommaso Mansi: a Johnson & Johnson Company. Data Science and Digital Health
Kristopher A. Standish: a Johnson & Johnson Company. Data Science and Digital Health

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract Accurate identification of genetic alterations in tumors, such as Fibroblast Growth Factor Receptor, is crucial for treating with targeted therapies; however, molecular testing can delay patient care due to the time and tissue required. Successful development, validation, and deployment of an AI-based, biomarker-detection algorithm could reduce screening cost and accelerate patient recruitment. Here, we develop a deep-learning algorithm using >3000 H&E-stained whole slide images from patients with advanced urothelial cancers, optimized for high sensitivity to avoid ruling out trial-eligible patients. The algorithm is validated on a dataset of 350 patients, achieving an area under the curve of 0.75, specificity of 31.8% at 88.7% sensitivity, and projected 28.7% reduction in molecular testing. We successfully deploy the system in a non-interventional study comprising 89 global study clinical sites and demonstrate its potential to prioritize/deprioritize molecular testing resources and provide substantial cost savings in the drug development and clinical settings.

Date: 2024
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DOI: 10.1038/s41467-024-49153-9

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