The Liver Imaging Reporting and Data System (LI-RADS) is a set of algorithms designed to provide a standardized, comprehensive framework for the interpretation of surveillance and diagnostic imaging in patients at high risk for hepatocellular carcinoma. LI-RADS is the result of a multidisciplinary collaboration between radiologists, hepatologists, hepatobiliary surgeons and pathologists and has recently been incorporated into the practice guidelines for the American Association for the Study of Liver Diseases (AASLD) and made congruent with the Organ Procurement and Transplantation Network (OPTN) criteria. This manuscript illustrates the common ultrasound, computed tomography, and magnetic resonance imaging appearances of hepatocellular carcinoma and describes how these findings can be properly categorized using the LI-RADS system.
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http://dx.doi.org/10.1053/j.sult.2021.03.002 | DOI Listing |
Arch Biochem Biophys
January 2025
Department of Regenerative Medicine, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran; Experimental Cancer Medicine, Institution for Laboratory Medicine, Karolinska Institute, Stockholm, Sweden. Electronic address:
Hepatocellular carcinoma (HCC) is one of the most lethal malignancies worldwide and the most common form of liver cancer. Despite global efforts toward early diagnosis and effective treatments, HCC is often diagnosed at advanced stages, where conventional therapies frequently lead to resistance and/or high recurrence rates. Therefore, novel biomarkers and promising medications are urgently required.
View Article and Find Full Text PDFMed Image Anal
January 2025
Department of Electrical and Computer Engineering, College of Information and Communication Engineering, Sungkyunkwan University, Suwon, 440-746, South Korea. Electronic address:
This study introduces HCC-Net, a novel wavelet-based approach for the accurate diagnosis of hepatocellular carcinoma (HCC) from abdominal ultrasound (US) images using artificial neural networks. The HCC-Net integrates the discrete wavelet transform (DWT) to decompose US images into four sub-band images, a lesion detector for hierarchical lesion localization, and a pattern-augmented classifier for generating pattern-enhanced lesion images and subsequent classification. The lesion detection uses a hierarchical coarse-to-fine approach to minimize missed lesions.
View Article and Find Full Text PDFCrit Rev Oncol Hematol
January 2025
Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; Institute of Radiation Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; Hubei Key Laboratory of Precision Radiation Oncology, Hubei, China.
Hepatocellular carcinoma (HCC) presents a formidable challenge in oncology, attributed to its association with chronic liver diseases and global prevalence. The immune microenvironment profoundly influences HCC progression, balancing immune suppression and antitumor responses. The Signal Transducer and Activator of Transcription 3 (STAT3) is central to this equilibrium, orchestrating immune dynamics and intertwining tumor progression with immune evasion mechanisms.
View Article and Find Full Text PDFHepatology
February 2025
Department of Medicine III, Division of Gastroenterology and Hepatology, Medical University of Vienna, Vienna, Austria.
Background And Aims: Around 750,000 patients per year will be cured of HCV infection until 2030. Those with compensated advanced chronic liver disease remain at risk for hepatic decompensation and de novo HCC. Algorithms have been developed to stratify risk early after cure; however, data on long-term outcomes and the prognostic utility of these risk stratification algorithms at later time points are lacking.
View Article and Find Full Text PDFHepatology
January 2025
State Key Laboratory of Liver Research, Department of Pathology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, the University of Hong Kong, Hong Kong, China.
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