Related Experiment Video For Hepatocellular carcinoma
Updated: Feb 17, 2026
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
Radiomics based analysis to predict local control and survival in hepatocellular carcinoma patients treated with
Luca Cozzi1,2,3, Nicola Dinapoli4, Antonella Fogliata5
1Radiotherapy and Radiosurgery Department, Humanitas Clinical and Research Hospital, Rozzano, Italy. luca.cozzi@humanitas.it.
Background:
To appraise the ability of a radiomics based analysis to predict local response and overall survival for patients with hepatocellular carcinoma.
Methods:
A set of 138 consecutive patients (112 males and 26 females, median age 66 years) presented with Barcelona Clinic Liver Cancer (BCLC) stage A to C were retrospectively studied. For a subset of these patients (106) complete information about treatment outcome, namely local control, was available. Radiomic features were computed for the clinical target volume. A total of 35 features were extracted and analyzed. Univariate analysis was used to identify clinical and radiomics significant features. Multivariate models by Cox-regression hazards model were built for local control and survival outcome. Models were evaluated by area under the curve (AUC) of receiver operating characteristic (ROC) curve. For the LC analysis, two models selecting two groups of uncorrelated features were analyzes while one single model was built for the OS analysis.
Results:
The univariate analysis lead to the identification of 15 significant radiomics features but the analysis of cross correlation showed several cross related covariates. The un-correlated variables were used to build two separate models; both resulted into a single significant radiomic covariate: model-1: energy p < 0.05, AUC of ROC 0.6659, C.I.: 0.5585-0.7732; model-2: GLNU p < 0.05, AUC 0.6396, C.I.:0.5266-0.7526. The univariate analysis for covariates significant with respect to local control resulted in 9 clinical and 13 radiomics features with multiple and complex cross-correlations. After elastic net regularization, the most significant covariates were compacity and BCLC stage, with only compacity significant to Cox model fitting (Cox model likelihood ratio test p < 0.0001, compacity p < 0.00001; AUC of the model is 0.8014 (C.I. = 0.7232-0.8797)).
Conclusion:
A robust radiomic signature, made by one single feature was finally identified. A validation phases, based on independent set of patients is scheduled to be performed to confirm the results.
