Performance of AI-aided mammography in breast cancer diagnosis: Does breast density matter?

Abstract

Abstract Background One of the top four malignancies affecting women worldwide is breast cancer. Breast density is a risk factor for breast cancer on its own and also a limiting factor for the sensitivity of screening mammography. Tools of artificial intelligence (AI) can help radiologists to make decisions, potentially reducing perceptual and interpretation errors, or as a way to prioritize exams based on the likelihood of malignancy. Objectives The purpose of this study was to assess the impact of breast density on the performance of AI in mammography (MG) for the diagnosis of breast malignancy. Methods In total, 110 patients with pathologically proven breast cancer participated in this retrospective study. These patients had full field digital mammography, and the mammogram pictures were exported to the AI software system. Heat maps displaying the location of discovered lesions then highlighted the affected area or areas and also provided abnormality scores indicating the probability of malignancy (POM). The results of the histopathological analysis were correlated with the breast density and AI category. Results The artificial intelligence software gave a breast density score to each patient as well as POM scoring. Both the software and the radiologist agreed on the breast density in 80.00% (N = 88) of the patients. Upon correlation of AI results to the BI-RADS given by radiologist, demonstrated statistically very significant correlation (P value 0.001), indicating that the likelihood of error is less than one in a thousand. Upon correlating the pathology results with the AI abnormality score, the AI showed sensitivity of 93.64% as it detected 103 true positive lesions. AI showed 100% sensitivity in both ACR A and ACR B, and 94.74%, 76.47% in ACR C, ACR D, respectively. False negative results represented 5.26% in ACR C group and the highest with 23.53% in ACR D group of patients. The P value was found less than 0.001. Pearson correlation coefficient was calculated (R = 0.27) which was interpreted as a weak correlation between the decrease in sensitivity of AI and the breast density. Conclusions Our study showed that there is a slight link between increasing breast density and a relative decline in AI's ability to detect malignant lesions, suggesting that AI can detect breast cancer effectively in breasts of different parenchymal densities, with its effectiveness being highest in breasts with lower parenchymal density.