PurposePopulation-based screening programs for the early detection of breast cancer have significantly reduced mortality in women, but they are resource intensive in terms of time, cost, and workload and still have limitations mainly due to the use of 2D imaging techniques, which may cause overlapping of tissues, and interobserver variability. Artificial intelligence (AI) systems may be a valuable tool to assist radiologist when reading and classifying mammograms based on the malignancy of the detected lesions. However, there are several factors that can influence the outcome of a mammogram and thus also the detection capability of an AI system. The aim of our work is to analyze the robustness of the diagnostic ability of an AI system designed for breast cancer detection.ApproachMammograms from a population-based screening program were scored with the AI system. The sensitivity and specificity by means of the area under the receiver operating characteristic (ROC) curve were obtained as a function of the mammography unit manufacturer, demographic characteristics, and several factors that may affect the image quality (age, breast thickness and density, compression applied, beam quality, and delivered dose).ResultsThe area under the curve (AUC) from the scoring ROC curve was 0.92 (95% confidence interval = 0.89 − 0.95). It showed no dependence with any of the parameters considered, as the differences in the AUC for different interval values were not statistically significant.ConclusionThe results suggest that the AI system analyzed in our work has a robust diagnostic capability, and that its accuracy is independent of the studied parameters.
Screening programs for the early detection of breast cancer have significantly reduced mortality in women. The limitations of these programmes are primarily due to the use of 2D techniques and the high number of mammograms to be read by radiologists. Artificial Intelligence (AI) systems may lead to new tools to help radiologists read mammograms and classify the examination based on the malignancy of the detected lesions. Several factors related to breast characteristics (thickness and density), technical factors of image acquisition, X-ray system performance and image processing algorithms can influence the outcome of a mammogram and thus also the detection capability of an AI system. The aim of this work is to analyze the robustness of an AI system for breast cancer detection and its dependence on breast characteristics and technical factors. For this purpose, mammograms from a population-based screening program were scored with the AI system. The AUC (area under the ROC curve) index generated from the scoring ROC curve was 0.92 (CI(95%) = 0.89 - 0.95), demonstrating the robust performance of the AI system. Moreover, the statistical analysis performed showed that the AUC index was independent of breast characteristics, the type of mammographic system and most of the technical parameters considered, demonstrating the effectiveness of the AI system.
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