Evaluation of a deep learning-based software to automatically detect and quantify breast arterial calcifications on digital mammogram

Laetitia Saccenti; Bilel Ben Jedida; Lise Minssen; Refaat Nouri; Lina El Bejjani; Haifa Remili; An Voquang; Vania Tacher; Hicham Kobeiter; Alain Luciani; Jean Francois Deux; Thu Ha Dao

doi:10.1016/j.diii.2024.10.001

Evaluation of a deep learning-based software to automatically detect and quantify breast arterial calcifications on digital mammogram

Diagn Interv Imaging. 2024 Oct 25:S2211-5684(24)00233-X. doi: 10.1016/j.diii.2024.10.001. Online ahead of print.

Authors

Affiliations

¹ Department of Medical Imaging, Hopital Henri Mondor, Assistance Publique-Hopitaux de Paris, 94000, Creteil, France; Henri Mondor Institute of Biomedical Research -Inserm, U955 Team N 18, Paris Est Creteil University, 94000, Creteil, France. Electronic address: laetitia.saccenti@aphp.fr.
² Department of Medical Imaging, Hopital Henri Mondor, Assistance Publique-Hopitaux de Paris, 94000, Creteil, France.
³ Department of Medical Imaging, Hopital Henri Mondor, Assistance Publique-Hopitaux de Paris, 94000, Creteil, France; Henri Mondor Institute of Biomedical Research -Inserm, U955 Team N 18, Paris Est Creteil University, 94000, Creteil, France.
⁴ Department of Radiology, Geneva University Hospitals, 1205, Geneva, Switzerland.

PMID: 39490357
DOI: 10.1016/j.diii.2024.10.001

Abstract

Purpose: The purpose of this study was to evaluate an artificial intelligence (AI) software that automatically detects and quantifies breast arterial calcifications (BAC).

Materials and methods: Women who underwent both mammography and thoracic computed tomography (CT) from 2009 to 2018 were retrospectively included in this single-center study. Deep learning-based software was used to automatically detect and quantify BAC with a BAC AI score ranging from 0 to 10-points. Results were compared using Spearman correlation test with a previously described BAC manual score based on radiologists' visual quantification of BAC on the mammogram. Coronary artery calcification (CAC) score was manually scored using a 12-point scale on CT. The diagnostic performance of the marked BAC AI score (defined as BAC AI score ≥ 5) for the detection of marked CAC (CAC score ≥ 4) was analyzed in terms of sensitivity, specificity, accuracy and area under the receiver operating characteristic curve (AUC).

Results: A total of 502 women with a median age of 62 years (age range: 42-96 years) were included. The BAC AI score showed a very strong correlation with the BAC manual score (r = 0.83). Marked BAC AI score had 32.7 % sensitivity (37/113; 95 % confidence interval [CI]: 24.2-42.2), 96.1 % specificity (374/389; 95 % CI: 93.7-97.8), 71.2 % positive predictive value (37/52; 95 % CI: 56.9-82.9), 83.1 % negative predictive value (374/450; 95 % CI: 79.3-86.5), and 81.9 % accuracy (411/502; 95 % CI: 78.2-85.1) for the diagnosis of marked CAC. The AUC of the marked BAC AI score for the diagnosis of marked CAC was 0.64 (95 % CI: 0.60-0.69).

Conclusion: The automated BAC AI score shows a very strong correlation with manual BAC scoring in this external validation cohort. The automated BAC AI score may be a useful tool to promote the integration of BAC into mammography reports and to improve awareness of a woman's cardiovascular risk status.

Keywords: Breast; Deep learning; Heart disease risk factors; Mammography; Vascular calcification.