Evaluation of an AI-based model to estimate cancer risk that aims at improving early detection of cancer by leveraging information untapped by detection models. We converted a breast cancer detection model into a risk model with light architectural changes and by using the survival analysis / time to event paradigm within the machine learning framework. The new model is able to predict cumulative risk function of a breast/patient from mammogram images. A longitudinal dataset of 2,460 positive patients and 5,466 negative patients over average timespan avg 4.6 years and q75 = 5.5 years, q90 = 7.1 years), independent from our training set, is used to evaluate the performance of our approach. We compare our methods against the open source baseline MIRAI, considered as the state of the art. To do so we used both concordance index aka. C-index and dynamic AUC restricted to the 5 year range that MIRAI model allows. We obtain a concordance index of 0.758 (ci=(0.752, 0.763)). While the baseline reaches a concordance index of 0.736 (ci=(0.730, 0.743)). Regarding cumulative dynamic AUC, our AI model reach 0.796 (ci=(0.791, 805)) remaining close to MIRAI, which is at 0.801 (ci=(0.794, 0.810)). Our model demonstrates performance similar to the state of the art with few modifications.
Diagnostic mammography, conducted to assess symptoms or screen-detected lesions in women, often involves extra views beyond standard ones. Utilization of these additional views may vary across radiologists and healthcare settings. Overall, the aim of such a mammographic work-up is to provide extra imaging data, thus improving result accuracy. While artificial intelligence (AI) has demonstrated promising outcomes in cancer detection through mammographic screening, there remains a lack of evidence concerning its utilization in the diagnostic mammography context. This study aimed to investigate if using an AI-based model for diagnostic mammography could provide advantages beyond its use solely for screening mammograms. We applied an AI system, trained and validated on screening mammograms, to a dataset of diagnostic mammograms. Performance were compared to the same system applied to screening mammogram of the same patient. The findings indicate that the AI model performs similarly well when applied to non-standard views compared to standard digital mammograms. Specifically, the model demonstrates higher accuracy than the baseline and greater specificity at a given sensitivity level. This suggests that the model generalizes well on diagnostic mammograms. Understanding this generalization was important for comprehending the model's performance on diagnostic images and determining the feasibility of developing a specifically trained algorithm.
High breast density is considered a risk factor for breast cancer, and it is particularly important for early detection when masses are small and difficult to see. The BI-RADS reporting system provides guidelines for standardized visual assessment of breast density. Nevertheless, such guidelines which rely in part on the delineation of dense tissues are likely to lead to variability between annotators. In this present study, we hypothesized that such variability is amplified when looking at density scores assigned by trained radiologists of different countries. The hypothesis was tested on a retrospectively collected dataset of mammography images which were assigned with a density value from 3 radiologists from France and 4 radiologists from the United States. In a further step, we used an AI-model to automatically assess density in all images, and compared predictions to annotations obtained from both countries. We then implemented a calibration procedure to adjust those predictions for regional effect. Comparing consensus-based labels between the French and the US datasets resulted in a significant difference. The proposed AI-based model, after undergoing a region-specific calibration procedure, was consistent with the expected behavior showing good agreement with French based or US based consensus respectively.
In breast cancer detection, change in findings throughout time is one of the major biomarkers for the presence of malignancy. Several studies have established the value of comparing mammograms with the ones from previous examinations. Some of them have shown that such comparison decreases the recall rate and increases the biopsy yield of cancer but does not increase the cancer detection rate. This evidence brought us to do the hypotheses that, as for human radiologists, adding temporal context information could be beneficial also for artificial intelligence (AI) systems for breast cancer detection thus improving their specificity which today represents the major limitation for an autonomous use of such AI systems. In this study we carry out a comparison between an AI system for breast cancer detection and an update version of the same system able to integrate the temporal context information.
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