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Development and deployment of a histopathology-based deep learning algorithm for patient prescreening in a clinical trial.
- Source :
-
Nature communications [Nat Commun] 2024 Jun 01; Vol. 15 (1), pp. 4690. Date of Electronic Publication: 2024 Jun 01. - Publication Year :
- 2024
-
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.<br /> (© 2024. The Author(s).)
- 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
Subjects
Details
- Language :
- English
- ISSN :
- 2041-1723
- Volume :
- 15
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Nature communications
- Publication Type :
- Academic Journal
- Accession number :
- 38824132
- Full Text :
- https://doi.org/10.1038/s41467-024-49153-9