Machine learning modeling to predict HCC locations in cirrhotic patients undergoing MRI - a proof-of-concept study.

Predictive value of an AI model integrating MRI radiomics and clinical features for early recurrence of hepatocellular carcinoma prior to radiofrequency ablation

Objectives This study developed an AI-driven radiomics model fusing pre-radiofrequency ablation (RFA) multi-sequence MRI features and clinical variables to predict early recurrence of hepatocellular carcinoma (HCC) after RFA. Methods A total of 169 HCC patients who underwent pre-RFA MRI (January 2015–December 2021) wer...

Development and Validation of an Interpretable Machine Learning Model Based on Gd-EOB-DTPA-enhanced MRI for Evaluating Small HCC (≤2 cm): A Multicenter Cohort Study.

RATIONALE AND OBJECTIVES This study aims to develop and externally validate an interpretable machine-learning model that integrates gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid magnetic resonance imaging (MRI) radiomics, qualitative MRI features, and clinical data to accurately distinguish small (≤2 cm)...

A preoperative model for predicting microvascular invasion in hepatocellular carcinoma based on laboratory findings and MRI features.

A Clinical-Imaging Nomogram for Predicting Post-Hepatectomy Recurrence in Hepatocellular Carcinoma with Microvascular Invasion.

PURPOSE This study aimed to develop an individualized nomogram incorporating clinical and imaging features for predicting recurrence in hepatocellular carcinoma (HCC) patients with microvascular invasion (MVI) after hepatectomy. METHODS A total of 317 pathologically confirmed MVI-positive HCC patients who underwent c...

Risk-stratified MRI surveillance for hepatocellular carcinoma in cirrhosis: a prospective cohort study.

ADC-based radiomics for risk stratification in prostate cancer: a clinical decision support study

A minimal-feature radiomics model derived exclusively from ADC images showed promising diagnostic performance for differentiating malignant from benign prostate lesions, and may provide a practical, reproducible, and resource-efficient strategy for supporting prostate cancer diagnosis.