Diagnostic performance of mammography and magnetic resonance imaging for evaluating mammographically visible breast masses

Radiologic-Pathologic Correlation of Nonmass Enhancement Contiguous with Malignant Index Breast Cancer Masses at Preoperative Breast MRI

Purpose To determine the pathologic features of nonmass enhancement (NME) directly adjacent to biopsy-proven malignant masses (index masses) at preoperative MRI and determine imaging characteristics that are associated with a malignant pathologic condition. Materials and Methods This retrospective study involved the review of breast MRI and mammography examinations performed for evaluating disease extent in patients newly diagnosed with breast cancer from July 1, 2016, to September 30, 2019. Inclusion criteria were limited to patients with an index mass and the presence of NME extending directly from the mass margins. Wilcoxon rank sum test, Fisher exact test, and χ2 test were used to analyze cancer, patient, and imaging characteristics associated with the NME diagnosis. Results Fifty-eight patients (mean age, 58 years ± 12 [SD]; all women) were included. Malignant pathologic findings for mass-associated NME occurred in 64% (37 of 58) of patients, 43% (16 of 37) with ductal carcinoma in situ and 57% (21 of 37) with invasive carcinoma. NME was more likely to be malignant when associated with an index cancer that had a low Ki-67 index (<20%) (P = .04). The presence of calcifications at mammography correlating with mass-associated NME was not significantly associated with malignant pathologic conditions (P = .19). The span of suspicious enhancement measured at MRI overestimated the true span of disease at histologic evaluation (P < .001), while there was no evidence of a difference between span of calcifications at mammography and true span of disease at histologic evaluation (P = .27). Conclusion Mass-associated NME at preoperative MRI was malignant in most patients with newly diagnosed breast cancer. The span of suspicious enhancement measured at MRI overestimated the true span of disease found at histologic evaluation. Keywords: Breast, Mammography © RSNA, 2024 See also the commentary by Newell in this issue.

Rethinking Breast Cancer Diagnosis through Deep Learning Based Image Recognition

This paper explored techniques for diagnosing breast cancer using deep learning based medical image recognition. X-ray (Mammography) images, ultrasound images, and histopathology images are used to improve the accuracy of the process by diagnosing breast cancer classification and by inferring their affected location. For this goal, the image recognition application strategies for the maximal diagnosis accuracy in each medical image data are investigated in terms of various image classification (VGGNet19, ResNet50, DenseNet121, EfficietNet v2), image segmentation (UNet, ResUNet++, DeepLab v3), and related loss functions (binary cross entropy, dice Loss, Tversky loss), and data augmentation. As a result of evaluations through the presented methods, when using filter-based data augmentation, ResNet50 showed the best performance in image classification, and UNet showed the best performance in both X-ray image and ultrasound image as image segmentation. When applying the proposed image recognition strategies for the maximal diagnosis accuracy in each medical image data, the accuracy can be improved by 33.3% in image segmentation in X-ray images, 29.9% in image segmentation in ultrasound images, and 22.8% in image classification in histopathology images.