Validation and real-world clinical application of an artificial intelligence algorithm for breast cancer detection in biopsies

Judith Sandbank, Guillaume Bataillon, Alona Nudelman, Ira Krasnitsky, Rachel Mikulinsky, Lilach Bien, Lucie Thibault, Anat Albrecht Shach, Geraldine Sebag, Douglas P. Clark, Daphna Laifenfeld, Stuart J. Schnitt, Chaim Linhart, Manuela Vecsler, Anne Vincent-Salomon*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


Breast cancer is the most common malignant disease worldwide, with over 2.26 million new cases in 2020. Its diagnosis is determined by a histological review of breast biopsy specimens, which can be labor-intensive, subjective, and error-prone. Artificial Intelligence (AI)—based tools can support cancer detection and classification in breast biopsies ensuring rapid, accurate, and objective diagnosis. We present here the development, external clinical validation, and deployment in routine use of an AI-based quality control solution for breast biopsy review. The underlying AI algorithm is trained to identify 51 different types of clinical and morphological features, and it achieves very high accuracy in a large, multi-site validation study. Specifically, the area under the receiver operating characteristic curves (AUC) for the detection of invasive carcinoma and of ductal carcinoma in situ (DCIS) are 0.99 (specificity and sensitivity of 93.57 and 95.51%, respectively) and 0.98 (specificity and sensitivity of 93.79 and 93.20% respectively), respectively. The AI algorithm differentiates well between subtypes of invasive and different grades of in situ carcinomas with an AUC of 0.97 for invasive ductal carcinoma (IDC) vs. invasive lobular carcinoma (ILC) and AUC of 0.92 for DCIS high grade vs. low grade/atypical ductal hyperplasia, respectively, as well as accurately identifies stromal tumor-infiltrating lymphocytes (TILs) with an AUC of 0.965. Deployment of this AI solution as a real-time quality control solution in clinical routine leads to the identification of cancers initially missed by the reviewing pathologist, demonstrating both clinical utility and accuracy in real-world clinical application.

Original languageEnglish
Article number129
Journalnpj Breast Cancer
Issue number1
StatePublished - Dec 2022
Externally publishedYes


FundersFunder number
Ibex Medical Analytics


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