The sensitivities of %SUV(max) (66.7%), %k(ep) (51.7%), and %AUC(90) (50.0%) at (18)F-FDG PET/CT and DCE MR after two cycles of NAC are not acceptable, but the specificities (96.4%, 92.0%, and 95.2%, respectively) are high for stratification of pCR cases in breast cancer.
Of the three modalities, dynamic MRI was the best for evaluating the efficacy of TACE in the treatment of HCC. We also found that superficial lesions of the right lobe are good candidates for PD sonography. However, high signals on precontrast MR images, motion artifacts, and ultrasonic attenuation remain key limitations.
Breast cancer is the most frequently diagnosed cancer in women; it poses a serious threat to women’s health. Thus, early detection and proper treatment can improve patient prognosis. Breast ultrasound is one of the most commonly used modalities for diagnosing and detecting breast cancer in clinical practice. Deep learning technology has made significant progress in data extraction and analysis for medical images in recent years. Therefore, the use of deep learning for breast ultrasonic imaging in clinical practice is extremely important, as it saves time, reduces radiologist fatigue, and compensates for a lack of experience and skills in some cases. This review article discusses the basic technical knowledge and algorithms of deep learning for breast ultrasound and the application of deep learning technology in image classification, object detection, segmentation, and image synthesis. Finally, we discuss the current issues and future perspectives of deep learning technology in breast ultrasound.
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