- Tiam Jaroensri
- Ellery Wulczyn
- Narayan G Hegde
- Trissia Brown
- Isabelle Flament
- Fraser Tan
- Yuannan Cai
- Kunal Nagpal
- Emad Rakha
- David J. Dabbs
- Niels Olson
- James H. Wren
- Elaine E. Thompson
- Erik Seetao
- Carrie Robinson
- Melissa Miao
- Fabien Beckers
- Greg Corrado
- Lily Hao Yi Peng
- Craig Mermel
- Yun Liu
- Dave Steiner
- Cameron Chen
Abstract
Histologic grading of breast cancer involves review and scoring of three well-established morphologic features: mitotic count, nuclear pleomorphism, and tubule formation. Taken together, these features form the basis of the Nottingham Grading System which is used to inform breast cancer characterization and prognosis. In this study, we developed deep learning models to perform histologic scoring of all three components using digitized hematoxylin and eosin-stained slides containing invasive breast carcinoma. We then evaluated the prognostic potential of these models using an external test set and progression free interval as the primary outcome. The individual component models performed at or above published benchmarks for algorithm-based grading approaches and achieved high concordance rates in comparison to pathologist grading. Prognostic performance of histologic scoring provided by the deep learning-based grading was on par with that of pathologists performing review of matched slides. Additionally, by providing scores for each component feature, the deep-learning based approach provided the potential to identify the grading components contributing most to prognostic value. This may enable optimized prognostic models as well as opportunities to improve access to consistent grading and better understand the links between histologic features and clinical outcomes in breast cancer.
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