AIMC Topic: Carcinoma, Hepatocellular

Clear Filters Showing 81 to 90 of 452 articles

Deep learning for hepatocellular carcinoma recurrence before and after liver transplantation: a multicenter cohort study.

Scientific reports
Hepatocellular carcinoma (HCC) recurrence after liver transplantation (LT) is a major contributor to mortality. We developed a recurrence prediction system for HCC patients before and after LT. Data from patients with HCC who underwent LT were retros...

Predicting the complexity of minimally invasive liver resection for hepatocellular carcinoma using machine learning.

HPB : the official journal of the International Hepato Pancreato Biliary Association
BACKGROUND: Despite technical advancements, minimally invasive liver surgery (MILS) for hepatocellular carcinoma (HCC) remains challenging. Nonetheless, effective tools to assess MILS complexity are still lacking. Machine learning (ML) models could i...

Machine learning analysis identified NNMT as a potential therapeutic target for hepatocellular carcinoma based on PCD-related genes.

Scientific reports
Programmed cell death (PCD) plays a critical role in cancer biology, influencing tumor progression and treatment response. This study aims to investigate the role of PCD-related genes in hepatocellular carcinoma (HCC), identifying potential prognosti...

A deep learning-based psi CT network effectively predicts early recurrence after hepatectomy in HCC patients.

Abdominal radiology (New York)
BACKGROUND: Hepatocellular carcinoma (HCC) exhibits a high recurrence rate, and early recurrence significantly jeopardizes patient prognosis, necessitating reliable methods for early recurrence prediction.

Current State of Evidence for Use of MRI in LI-RADS.

Journal of magnetic resonance imaging : JMRI
The American College of Radiology Liver Imaging Reporting and Data System (LI-RADS) is the preeminent framework for classification and risk stratification of liver observations on imaging in patients at high risk for hepatocellular carcinoma. In this...

Predicting hepatocellular carcinoma survival with artificial intelligence.

Scientific reports
Despite the extensive research on hepatocellular carcinoma (HCC) exploring various treatment strategies, the survival outcomes have remained unsatisfactory. The aim of this research was to evaluate the ability of machine learning (ML) methods in pred...

CT-based detection of clinically significant portal hypertension predicts post-hepatectomy outcomes in hepatocellular carcinoma.

European radiology
BACKGROUND: While the CT-based method of detecting clinically significant portal hypertension (CSPH) emerged as a noninvasive alternative for evaluating CSPH, its predictive ability for post-hepatectomy outcomes is unknown. Therefore, this study aime...

An interpretable ensemble model combining handcrafted radiomics and deep learning for predicting the overall survival of hepatocellular carcinoma patients after stereotactic body radiation therapy.

Journal of cancer research and clinical oncology
PURPOSE: Hepatocellular carcinoma (HCC) remains a global health concern, marked by increasing incidence rates and poor outcomes. This study seeks to develop a robust predictive model by integrating radiomics and deep learning features with clinical d...