Deep learning analysis of breast cancer histology predicts ATM pathogenic variant carrier status.

Nicolas M Viart; Lucie Thibault; Tristan Lazard; Séverine Eon-Marchais; Yue Jiao; Laetitia Fuhrmann; Dorothée Le Gal; Eve Cavaciuti; Marie-Gabrielle Dondon; Juana Beauvallet; Marina De Brot; Joanne Ngeow; Soo-Hwang Teo; Maria Isabel Achatz; Elizabeth Santana Dos Santos; Fergus J Couch; Dominique Stoppa-Lyonnet; Melissa C Southey; Anne Vincent-Salomon; Thomas Walter; Nadine Andrieu; Fabienne Lesueur

Journal article

Deep learning analysis of breast cancer histology predicts ATM pathogenic variant carrier status.

Nicolas M Viart, Lucie Thibault, Tristan Lazard, Séverine Eon-Marchais, Yue Jiao, Laetitia Fuhrmann, Dorothée Le Gal, Eve Cavaciuti, Marie-Gabrielle Dondon, Juana Beauvallet, Marina De Brot, Joanne Ngeow, Soo-Hwang Teo, Maria Isabel Achatz, Elizabeth Santana Dos Santos, Fergus J Couch, Dominique Stoppa-Lyonnet, Melissa C Southey, Anne Vincent-Salomon, Thomas Walter Show all

NPJ Digit Med | Published : 2026

DOI: 10.1038/s41746-026-02895-w

Open access

Abstract

We performed deep learning analysis of histopathological whole-slide (full-face) images (WSI) to predict ATM pathogenic or likely pathogenic variant (PV/LPV) status of women with breast cancer and identify specific histological patterns of their tumor.In the discovery set composed of tumors from PV/LPV carriers (58 WSI) and noncarriers (129 WSI), our deep learning model predicted ATM status of patients with an area under the curve of 0.90 [95%CI: 0.85-0.95] and a balanced accuracy of 0.80 [95%CI: 0.72-0.88]. In the replication set (29 WSI from carriers and 22 WSI from noncarriers), corresponding results were 0.85 [95%CI: 0.70-1.00] and 0.67 [95%CI: 0.51-0.83]. We found that tumors developed ..

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