Radiomics based analysis to predict local control and survival in hepatocellular carcinoma patients treated with volumetric modulated arc therapy

Abstract

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.

Keywords: Hepatocellular carcinoma; Liver cancer; Outcome prediction; Radiomics; texture analysis; VMAT.

Conflict of interest statement

Ethics approval and consent to participate

This is a retrospective study approved by notification by the Cheng Chin hospital’s Ethical Review Committee. Informed consent to participate in the study was obtained from each patient at admission.

Consent for publication

Not applicable.

Competing interests

L. Cozzi acts as Scientific Advisor to Varian Medical Systems and is Clinical Research Scientist at Humanitas Cancer Center. All other co-authors have no conflicts of interest. No other conflict or source should be disclosed.

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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1

Cross correlation matrix. Numerical values correspond to Person correlation coefficient, achieved with Person correlation test P-Value >0.05 (low cross correlation)

Fig. 2

Cross correlation matrix for covariates used for log-rank test. Uncorrelated covariates are shown with Pearson correlation test P-Value

Fig. 3

Elastic net regularization process with partial likelihood deviance plot. The minimum value corresponds to the covariates used for multivariate modeling (Cox model). The two vertical dot lines represent one standard deviation on each sides from the minimum value, corresponding to the chosen variables that better fit the model

Fig. 4

Calibration plot for Cox model. The predicted overall survival at 12 months shows good agreement among predicted and actual survival while the 24 months prediction shows lack of fit in the first group of patients and a general underestimate of the predicted survival