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<p>We performed deep learning analysis of histopathological whole-slide (full-face) images (WSI) to predict <i>ATM</i> pathogenic or likely pathogenic variant (PV/LPV) status of women with breast cancer and identify specific histological patterns of their tumor.</p><p>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 by <i>ATM</i> PV/LPV carriers often displayed discohesive neoplastic cells as observed in invasive lobular carcinomas, and dense lymphocytic infiltrate reflecting an immune-enriched microenvironment.</p><p>Recognizing these tumors at the time of diagnosis is a critical first step toward precision medicine in affected women and precision prevention in family members.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月15日

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  • 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

摘要

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 by ATM PV/LPV carriers often displayed discohesive neoplastic cells as observed in invasive lobular carcinomas, and dense lymphocytic infiltrate reflecting an immune-enriched microenvironment.

Recognizing these tumors at the time of diagnosis is a critical first step toward precision medicine in affected women and precision prevention in family members.