Evaluation of a deep learning-based software to automatically detect and quantify breast arterial calcifications on digital mammogram - 26/10/24

Doi : 10.1016/j.diii.2024.10.001

Laetitia Saccenti ^a,^b,^⁎ , Bilel Ben Jedida ^a, Lise Minssen ^a, Refaat Nouri ^a, Lina El Bejjani ^a, Haifa Remili ^a, An Voquang ^a, Vania Tacher ^a,^b, Hicham Kobeiter ^a,^b, Alain Luciani ^a,^b, Jean Francois Deux ^c, Thu Ha Dao ^a
^a Department of Medical Imaging, Hopital Henri Mondor, Assistance Publique-Hopitaux de Paris, 94000, Creteil, France
^b Henri Mondor Institute of Biomedical Research -Inserm, U955 Team N 18, Paris Est Creteil University, 94000, Creteil, France
^c Department of Radiology, Geneva University Hospitals, 1205, Geneva, Switzerland

^⁎Corresponding author.

Sous presse. Épreuves corrigées par l'auteur. Disponible en ligne depuis le Saturday 26 October 2024

Highlights

•	A deep learning-based software is able to detect and quantify breast arterial calcifications on mammogram with a strong correlation with manual scoring in an external validation cohort.
•	A high level of breast arterial calcifications as detected by the deep learning-based software, is associated with a high coronary artery calcium score and therefore a higher risk of death from cardiovascular disease.
•	Automated quantification of breast arterial calcifications could be a useful tool to improve awareness of a woman's cardiovascular risk status.

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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.

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