Purpose To assess patient perceptions of artificial intelligence (AI) use in the interpretation of screening mammograms. Materials and Methods In a prospective, institutional review board-approved study, all patients undergoing mammography screening ...
Purpose To build a deep learning framework using contrast-enhanced MRI for lesion segmentation and automatic molecular subtype classification in breast cancer. Materials and Methods This retrospective multicenter study included patients with biopsy-p...
Purpose To develop deep learning (DL) radiopathomics models based on contrast-enhanced MRI and pathologic imaging to predict vessels encapsulating tumor clusters (VETC) and survival in hepatocellular carcinoma (HCC). Materials and Methods In this ret...
Purpose To validate a deep learning (DL) model for predicting the risk of prostate cancer (PCa) progression based on MRI and clinical parameters and compare it with established models. Materials and Methods This retrospective study included 1607 MRI ...
Purpose To evaluate the performance of an artificial intelligence (AI) model in detecting overall and clinically significant prostate cancer (csPCa)-positive lesions on paired external and in-house biparametric MRI (bpMRI) scans and assess performanc...
Purpose To compare two deep learning-based commercially available artificial intelligence (AI) systems for mammography with digital breast tomosynthesis (DBT) and benchmark them against the performance of radiologists. Materials and Methods This retr...
MR spectroscopy (MRS) is a noninvasive imaging method enabling chemical and molecular profiling of tissues in a localized, multiplexed, and nonionizing manner. As metabolic reprogramming is a hallmark of cancer, MRS provides valuable metabolic and mo...