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

IF 4.9 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Diagnostic and Interventional Imaging Pub Date : 2024-10-25 DOI:10.1016/j.diii.2024.10.001

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

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引用次数: 0

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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评估基于深度学习的软件，以自动检测和量化数字乳房 X 光照片上的乳腺动脉钙化。

目的：本研究旨在评估一款可自动检测和量化乳腺动脉钙化（BAC）的人工智能（AI）软件：这项单中心研究回顾性地纳入了 2009 年至 2018 年期间接受乳腺 X 射线照相术和胸部计算机断层扫描（CT）的女性。使用基于深度学习的软件自动检测和量化 BAC，BAC AI 得分从 0 分到 10 分不等。研究结果通过斯皮尔曼相关性检验与之前描述的基于放射科医师对乳房 X 光片上 BAC 的视觉量化的 BAC 人工评分进行了比较。冠状动脉钙化（CAC）评分是在 CT 上使用 12 分制手动评分的。从敏感性、特异性、准确性和接收器操作特征曲线下面积（AUC）等方面分析了标记的 BAC AI 评分（定义为 BAC AI 评分≥5）在检测标记的 CAC（CAC 评分≥4）方面的诊断性能：共纳入 502 名妇女，中位年龄为 62 岁（年龄范围：42-96 岁）。BAC AI 评分与 BAC 手工评分有很强的相关性（r = 0.83）。标记的 BAC AI 评分具有 32.7 % 的灵敏度（37/113；95 % 置信区间 [CI]：24.2-42.2）、96.1 % 的特异性（374/389；95 % CI：93.7-97.8）、71.2 % 的阳性预测值（37/52；95 % CI：56.诊断明显 CAC 的阳性预测值为 71.2%（37/52；95 % CI：56.9-82.9），阴性预测值为 83.1%（374/450；95 % CI：79.3-86.5），准确率为 81.9%（411/502；95 % CI：78.2-85.1）。诊断明显 CAC 的 BAC AI 评分的 AUC 为 0.64（95 % CI：0.60-0.69）：结论：在这一外部验证队列中，自动 BAC AI 评分与手动 BAC 评分显示出很强的相关性。自动 BAC AI 评分可能是促进将 BAC 纳入乳腺 X 射线摄影报告并提高对妇女心血管风险状况认识的有用工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Diagnostic and Interventional Imaging Medicine-Radiology, Nuclear Medicine and Imaging

CiteScore

8.50

自引率

29.10%

发文量

126

审稿时长

11 days

期刊介绍： Diagnostic and Interventional Imaging accepts publications originating from any part of the world based only on their scientific merit. The Journal focuses on illustrated articles with great iconographic topics and aims at aiding sharpening clinical decision-making skills as well as following high research topics. All articles are published in English. Diagnostic and Interventional Imaging publishes editorials, technical notes, letters, original and review articles on abdominal, breast, cancer, cardiac, emergency, forensic medicine, head and neck, musculoskeletal, gastrointestinal, genitourinary, interventional, obstetric, pediatric, thoracic and vascular imaging, neuroradiology, nuclear medicine, as well as contrast material, computer developments, health policies and practice, and medical physics relevant to imaging.