PURPOSE: This study aimed to build and evaluate a deep learning (DL) model to predict vessels encapsulating tumor clusters (VETC) and prognosis preoperatively in patients with hepatocellular carcinoma (HCC).
PURPOSE: To evaluate the efficacy of a vendor-specific deep learning reconstruction algorithm (DLRA) in enhancing image quality and focal lesion detection using three-dimensional T1-weighted gradient-echo images in gadoxetic acid-enhanced liver magne...
OBJECTIVES: The current study developed an ultrasound-based deep learning model to make preoperative differentiation among hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and combined hepatocellular-cholangiocarcinoma (cHCC-ICC...
PURPOSE: To establish and validate a deep learning radiomics nomogram (DLRN) based on intratumoral and peritumoral regions of MR images and clinical characteristics to predict recurrence risk factors in early-stage cervical cancer and to clarify whet...
PURPOSE: Liver biopsy was considered the gold standard for diagnosing liver fibrosis; however, with advancements in medical technology and increasing awareness of potential complications, the reliance on liver biopsy has diminished. Ultrasound is gai...
OBJECTIVE: Our study aimed to determine whether radiomics models based on contrast-enhanced computed tomography (CECT) have considerable ability to predict serosal involvement in gallbladder cancer (GBC) patients.
PURPOSE: To investigate the image quality and diagnostic performance of low-contrast-dose liver CT using a deep learning-based iodine contrast-augmenting algorithm (DLICA) for hypovascular hepatic metastases.
BACKGROUND: CT image reconstruction has evolved from filtered back projection to hybrid- and model-based iterative reconstruction. Deep learning-based image reconstruction is a relatively new technique that uses deep convolutional neural networks to ...