Publications by authors named "V Y Chernyak"

Guidelines suggest the Liver Imaging Reporting and Data System (LI-RADS) may not be applicable for some populations at risk for hepatocellular carcinoma (HCC). However, data assessing the association of HCC risk factors with LI-RADS major features are lacking. To evaluate whether the association between HCC risk factors and each CT/MRI LI-RADS major feature differs among individuals at-risk for HCC.

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Article Synopsis
  • - The study aimed to create and validate an MRI-based model to diagnose microvascular invasion (MVI) and high-risk histopathology in patients with small hepatocellular carcinoma (HCC) and to predict benefits from adjuvant therapy.
  • - Researchers conducted a retrospective analysis on 577 patients, using various clinical and MRI features to develop the model, which was then validated across multiple hospitals.
  • - They found that specific traits, including high serum α-fetoprotein levels and non-simple nodular growth, indicated worse recurrence-free survival, but patients showing these high-risk traits did benefit from adjuvant therapy.
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Hepatocellular carcinoma (HCC) surveillance is recommended by liver professional societies but lacks broad acceptance by several primary care and cancer societies due to limitations in the existing data. We convened a diverse multidisciplinary group of cancer screening experts to evaluate current and future paradigms of HCC prevention and early detection using a rigorous Delphi panel approach. The experts had high agreement on twenty-one statements about primary prevention, HCC surveillance benefits, HCC surveillance harms, and the evaluation of emerging surveillance modalities.

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Objective: Aim: To explore the multifaceted role of university clinics in shaping medical professionals, advancing medical knowledge, and improving healthcare delivery. Special attention is given to their function as primary platforms for practical training, the development of professional competencies, and the implementation of innovative teaching methods in medical education..

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Initially released in 2011, liver imaging reporting and data (LI-RADS) CT/MRI diagnostic algorithm categorizes hepatic observations on an ordinal scale based on the probability of hepatocellular carcinoma, malignancy, or benignity, and guides reproducible interpretation, clear communication, and standardized terminology for liver imaging. LI-RADS has significantly expanded in scope in the past decade, with the inclusion of algorithms that address screening and surveillance, diagnosis with contrast enhanced ultrasound (CEUS), and treatment response assessment with both CEUS and CT/MRI. LI-RADS algorithms undergo periodic refinements based on accumulating scientific evidence, user feedback, and technological advancements.

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