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Peer-Reviewed Publication
Cancer Res2024;84(20):3478-3489.October 15, 2024Journal Article

A Genomics-Driven Artificial Intelligence-Based Model Classifies Breast Invasive Lobular Carcinoma and Discovers CDH1 Inactivating Mechanisms.

Fresia Pareja1, Higinio Dopeso1, Yi Kan Wang2, Andrea M Gazzo1, David N Brown1, Monami Banerjee2, Pier Selenica1, Jan H Bernhard2, Fatemeh Derakhshan1, Edaise M da Silva1, Lorraine Colon-Cartagena1, Thais Basili1, Antonio Marra1, Jillian Sue2, Qiqi Ye1, Arnaud Da Cruz Paula3, Selma Yeni Yildirim1, Xin Pei4, Anton Safonov5, Hunter Green1, Kaitlyn Y Gill1, Yingjie Zhu1, Matthew C H Lee2, Ran A Godrich2, Adam Casson2, Britta Weigelt1, Nadeem Riaz4, Hannah Y Wen1, Edi Brogi1, Diana L Mandelker1, Matthew G Hanna1, Jeremy D Kunz2, Brandon Rothrock2, Sarat Chandarlapaty5, Christopher Kanan6, Joe Oakley2, David S Klimstra2, Thomas J Fuchs2, Jorge S Reis-Filho1
1Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.
2Paige AI, New York, New York.
3Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York.
4Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York.
5Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York.
6Department of Computer Science, University of Rochester, Rochester, New York.
PubMed PMC DOI PDF

Abstract

Artificial intelligence (AI) systems can improve cancer diagnosis, yet their development often relies on subjective histologic features as ground truth for training. Herein, we developed an AI model applied to histologic whole-slide images using CDH1 biallelic mutations, pathognomonic for invasive lobular carcinoma (ILC) in breast neoplasms, as ground truth. The model accurately predicted CDH1 bia…

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