1. Development and deployment of a histopathology-based deep learning algorithm for patient prescreening in a clinical trial.
- Author
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Juan Ramon A, Parmar C, Carrasco-Zevallos OM, Csiszer C, Yip SSF, Raciti P, Stone NL, Triantos S, Quiroz MM, Crowley P, Batavia AS, Greshock J, Mansi T, and Standish KA
- Subjects
- Humans, Biomarkers, Tumor metabolism, Biomarkers, Tumor genetics, Clinical Trials as Topic, Urinary Bladder Neoplasms pathology, Urinary Bladder Neoplasms genetics, Urinary Bladder Neoplasms diagnosis, Male, Female, Patient Selection, Urologic Neoplasms pathology, Urologic Neoplasms diagnosis, Urologic Neoplasms genetics, Deep Learning, Algorithms
- 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., (© 2024. The Author(s).)
- Published
- 2024
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