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 (n = 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 (n = 385/1180, 32.6%), and BI-RADS 5 (malignant) in 471, that had showed median percentage AI value of 88 (n = 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.
Sahar Mansour, Rasha Kamal, Lamiaa Hashem and Basma AlKalaawy
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