Women's Imaging Unit - Kasr El Ainy Hospital- Cairo University, Giza, Egypt.
Department of Radiology, Baheya Foundation for Early Detection and Treatment of Breast Cancer, Giza, Egypt.
Br J Radiol. 2021 Dec;94(1128):20210820. doi: 10.1259/bjr.20210820. Epub 2021 Oct 18.
To study the impact of artificial intelligence (AI) on the performance of mammogram with regard to the classification of the detected breast lesions in correlation to ultrasound-aided mammograms.
Ethics committee approval was obtained in this prospective analysis. The study included 2000 mammograms. The mammograms were interpreted by the radiologists and breast ultrasound was performed for all cases. The Breast Imaging Reporting and Data System (BI-RADS) score was applied regarding the combined evaluation of the mammogram and the ultrasound modalities. Each breast side was individually assessed with the aid of AI scanning in the form of targeted heat-map and then, a probability of malignancy (abnormality scoring percentage) was obtained. Operative and the histopathology data were the standard of reference.
Normal assigned cases (BI-RADS 1) with no lesions were excluded from the statistical evaluation. The study included 538 benign and 642 malignant breast lesions ( = 1180, 59%). BI-RADS categories for the breast lesions with regard to the combined evaluation of the digital mammogram and ultrasound were assigned BI-RADS 2 (Benign) in 385 lesions with AI median value of the abnormality scoring percentage of 10 ( = 385/1180, 32.6%), and BI-RADS 5 (malignant) in 471, that had showed median percentage AI value of 88 ( = 471/1180, 39.9%). AI abnormality scoring of 59% yielded a sensitivity of 96.8% and specificity of 90.1% in the discrimination of the breast lesions detected on the included mammograms.
AI could be considered as an optional primary reliable complementary tool to the digital mammogram for the evaluation of the breast lesions. The color hue and the abnormality scoring percentage presented a credible method for the detection and discrimination of breast cancer of near accuracy to the breast ultrasound. So consequently, AI- mammogram combination could be used as a one setting method to discriminate between cases that require further imaging or biopsy from those that need only time interval follows up.
Recently, the indulgence of AI in the work-up of breast cancer was concerned. AI noted as a screening strategy for the detection of breast cancer. In the current work, the performance of AI was studied with regard to the diagnosis not just the detection of breast cancer in the mammographic-detected breast lesions. The evaluation was concerned with AI as a possible complementary reading tool to mammogram and included the qualitative assessment of the color hue and the quantitative integration of the abnormality scoring percentage.
研究人工智能（AI）对乳腺 X 线摄影中检测到的乳腺病变分类的影响，以及与超声辅助乳腺 X 线摄影的相关性。
本前瞻性分析获得伦理委员会批准。该研究纳入了 2000 例乳腺 X 线摄影。由放射科医生对乳腺 X 线摄影进行解读，并对所有病例进行乳腺超声检查。采用乳腺影像报告和数据系统（BI-RADS）评分对乳腺 X 线摄影和超声联合评估进行综合评估。使用 AI 扫描对每侧乳房进行单独评估，形成靶向热图，然后获得恶性肿瘤的概率（异常评分百分比）。手术和组织病理学数据为参考标准。
正常分配病例（BI-RADS 1）无病变者被排除在统计评估之外。该研究纳入了 538 例良性和 642 例恶性乳腺病变（n=1180，59%）。对于数字乳腺 X 线摄影和超声联合评估的乳腺病变，BI-RADS 分类为 BI-RADS 2（良性）的病变有 385 例，AI 异常评分百分比中位数为 10（n=385/1180，32.6%），BI-RADS 5（恶性）的病变有 471 例，AI 异常评分百分比中位数为 88（n=471/1180，39.9%）。AI 异常评分 59%时，对纳入乳腺 X 线摄影中检测到的乳腺病变的鉴别诊断，灵敏度为 96.8%，特异性为 90.1%。
AI 可作为数字乳腺 X 线摄影的一种可选的主要可靠补充工具，用于评估乳腺病变。颜色色调和异常评分百分比为乳腺癌的检测和鉴别提供了一种可靠的方法，其准确性接近乳腺超声。因此，AI-乳腺 X 线摄影联合应用可作为一种方法，用于区分需要进一步影像学检查或活检的病例和仅需要时间间隔随访的病例。
最近，人们对 AI 在乳腺癌工作流程中的应用产生了兴趣。AI 被视为乳腺癌检测的筛查策略。在本研究中，研究了 AI 的性能，不仅涉及乳腺癌的检测，还涉及乳腺 X 线摄影中检测到的乳腺病变的诊断。评估涉及 AI 作为乳腺 X 线摄影的一种可能的补充阅读工具，包括对颜色色调的定性评估和对异常评分百分比的定量整合。
