AIMC Topic: Liver Neoplasms

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TTGA U-Net: Two-stage two-stream graph attention U-Net for hepatic vessel connectivity enhancement.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Accurate segmentation of hepatic vessels is pivotal for guiding preoperative planning in ablation surgery utilizing CT images. While non-contrast CT images often lack observable vessels, we focus on segmenting hepatic vessels within preoperative MR i...

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...

Recurrence patterns and prediction of survival after recurrence for gallbladder cancer.

Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract
BACKGROUND: Gallbladder cancer (GBC) is associated with a poor prognosis. Recurrence patterns and their effect on survival remain ill-defined. This study aimed to analyze recurrence patterns and develop a machine learning (ML) model to predict surviv...

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...

A Bi-modal Temporal Segmentation Network for Automated Segmentation of Focal Liver Lesions in Dynamic Contrast-enhanced Ultrasound.

Ultrasound in medicine & biology
OBJECTIVE: To develop and validate an automated deep learning-based model for focal liver lesion (FLL) segmentation in a dynamic contrast-enhanced ultrasound (CEUS) video.

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...

ChatExosome: An Artificial Intelligence (AI) Agent Based on Deep Learning of Exosomes Spectroscopy for Hepatocellular Carcinoma (HCC) Diagnosis.

Analytical chemistry
Large language models (LLMs) hold significant promise in the field of medical diagnosis. There are still many challenges in the direct diagnosis of hepatocellular carcinoma (HCC). α-Fetoprotein (AFP) is a commonly used tumor marker for liver cancer. ...