Features of Undiagnosed Breast Cancers at Screening Breast MR Imaging and Potential Utility of Computer-Aided Evaluation
Related Experiment Video For Computer-aided evaluation
Updated: Mar 26, 2026
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014
Mirinae Seo1, Nariya Cho2, Min Sun Bae3
1Department of Radiology, Seoul National University Hospital, Seoul 03080, Korea.; Department of Radiology, Chung-Ang University Hospital, Seoul 06973, Korea.
Objective:
To retrospectively evaluate the features of undiagnosed breast cancers on prior screening breast magnetic resonance (MR) images in patients who were subsequently diagnosed with breast cancer, as well as the potential utility of MR-computer-aided evaluation (CAE).
Materials And Methods:
Between March 2004 and May 2013, of the 72 consecutive pairs of prior negative MR images and subsequent MR images with diagnosed cancers (median interval, 32.8 months; range, 5.4-104.6 months), 36 (50%) had visible findings (mean size, 1.0 cm; range, 0.3-5.2 cm). The visible findings were divided into either actionable or underthreshold groups by the blinded review by 5 radiologists. MR imaging features, reasons for missed cancer, and MR-CAE features according to actionability were evaluated.
Results:
Of the 36 visible findings on prior MR images, 33.3% (12 of 36) of the lesions were determined to be actionable and 66.7% (24 of 36) were underthreshold; 85.7% (6 of 7) of masses and 31.6% (6 of 19) of non-mass enhancements were classified as actionable lesions. Mimicking physiologic enhancements (27.8%, 10 of 36) and small lesion size (27.8%, 10 of 36) were the most common reasons for missed cancer. Actionable findings tended to show more washout or plateau kinetic patterns on MR-CAE than underthreshold findings, as the 100% of actionable findings and 46.7% of underthreshold findings showed washout or plateau (p = 0.008).
Conclusion:
MR-CAE has the potential for reducing the number of undiagnosed breast cancers on screening breast MR images, the majority of which are caused by mimicking physiologic enhancements or small lesion size.
