Mammography

About: Mammography is a research topic. Over the lifetime, 20643 publications have been published within this topic receiving 513679 citations.

The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process

Abstract: Mammography is the most effective method for breast cancer screening available today. However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes. To reduce the high number of unnecessary breast biopsies, several computer-aided diagnosis (CAD) systems have been proposed in the last several years. These systems help physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram or to perform a short term follow-up examination instead. We present two novel CAD approaches that both emphasize an intelligible decision process to predict breast biopsy outcomes from BI-RADS findings. An intelligible reasoning process is an important requirement for the acceptance of CAD systems by physicians. The first approach induces a global model based on decison-tree learning. The second approach is based on case-based reasoning and applies an entropic similarity measure. We have evaluated the performance of both CAD approaches on two large publicly available mammography reference databases using receiver operating characteristic (ROC) analysis, bootstrap sampling, and the ANOVA statistical significance test. Both approaches outperform the diagnosis decisions of the physicians. Hence, both systems have the potential to reduce the number of unnecessary breast biopsies in clinical practice. A comparison of the performance of the proposed decision tree and CBR approaches with a state of the art approach based on artificial neural networks (ANN) shows that the CBR approach performs slightly better than the ANN approach, which in turn results in slightly better performance than the decision-tree approach. The differences are statistically significant (p value < 0.001). On 2100 masses extracted from the DDSM database, the CRB approach for example resulted in an area under the ROC curve of A(z) = 0.89 +/- 0.01, the decision-tree approach in A(z) = 0.87 +/- 0.01, and the ANN approach in A(z) = 0.88 +/- 0.01.

Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning

Abstract: Microcalcification is an effective indicator of early breast cancer. To improve the diagnostic accuracy of microcalcifications, this study evaluates the performance of deep learning-based models on large datasets for its discrimination. A semi-automated segmentation method was used to characterize all microcalcifications. A discrimination classifier model was constructed to assess the accuracies of microcalcifications and breast masses, either in isolation or combination, for classifying breast lesions. Performances were compared to benchmark models. Our deep learning model achieved a discriminative accuracy of 87.3% if microcalcifications were characterized alone, compared to 85.8% with a support vector machine. The accuracies were 61.3% for both methods with masses alone and improved to 89.7% and 85.8% after the combined analysis with microcalcifications. Image segmentation with our deep learning model yielded 15, 26 and 41 features for the three scenarios, respectively. Overall, deep learning based on large datasets was superior to standard methods for the discrimination of microcalcifications. Accuracy was increased by adopting a combinatorial approach to detect microcalcifications and masses simultaneously. This may have clinical value for early detection and treatment of breast cancer.

Variability in Interpretive Performance at Screening Mammography and Radiologists’ Characteristics Associated with Accuracy

Abstract: Fellowship training in breast imaging was the only radiologists’ characteristic significantly associated with greater sensitivity and higher overall accuracy; however, fellowship-trained radiologists also had significantly higher false-positive rates.

Overdiagnosis of invasive breast cancer due to mammography screening: results from the Norwegian screening program.

Abstract: Mammography screening can detect breast cancer that would never have been clinically significant in a woman's lifetime, but the extent of this overdiagnosis is not known. In Norway, mammography scr...