Mark A. Helvie

TL;DR: Quantitative FDG PET scans of primary breast cancers showed a rapid and significant decrease in tumor glucose metabolism after effective treatment was initiated, with the reduction in metabolism antedating any decrement in tumor size.

TL;DR: The authors' results demonstrate the feasibility of using a convolution neural network for classification of masses and normal tissue on mammograms using a generalized, fast and stable implementation of the CNN.

TL;DR: This manuscript discusses the diagnostic evaluation of individuals with suspected breast cancer due to either abnormal imaging and/or physical findings.

TL;DR: These guidelines give health care providers a practical, consistent framework for screening and evaluating a spectrum of breast lesions and empower the patient to determine the level of breast cancer risk that is personally acceptable in the screening or follow-up recommendations.

TL;DR: CAD may be useful for assisting radiologists in classification of masses and thereby potentially help reduce unnecessary biopsies, as predicted from the improved ROC curves.