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

Abstract

BackgroundTo appraise the ability of a radiomics based analysis to predict local response and overall survival for patients with hepatocellular carcinoma.MethodsA 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.ResultsThe 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)).ConclusionA 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.

Radiomics for liver tumours

“…The model indicates the potential of MRI-based texture analysis at arterial-and venous-phase MRI for the early prediction of progressive disease after TARE Cozzi et al [106] To predict overall survival and local control Non-contrast CT…”

“…First promising results regarding the predictive potential of radiomics for local response after radiotherapy and TACE have recently been demonstrated [105][106][107] and are listed in Table 4. Treatment response of liver metastases after TACE has been determined using a radiomics-based analysis resulting in area under the curve (AUC) in receiver operating characteristics (ROC) of up to 0.83 [105].…”

“…Radiomics can be used to non-invasively diagnose HCC in cirrhotic patients with indeterminate liver nodules, which could be used to optimize patient management Donghui et al [13] To identify aggressive behaviour and predict recurrence of HCC after liver transplantation (LT) tures have been integrated into multivariate models predicting local control and overall survival rates after radiotherapy of HCC with an AUC of 0.80 [106]. Furthermore, by combining radiomics features with clinical data, survival prediction might even be improved in patients with HCC [13,107,108].…”

Radiomics in radiotherapy: Applications and future challenges

“…Their results showed that two features have good accuracy in the prediction of local control: the area under the curve of energy and Gray‐level Non‐Uniformity (GLNU), which were 0.6659 and 0.6396, respectively. For the prediction of overall survival, the area under the curve of the feature named compacity reached 0.8014 29 . Another study quantitatively evaluated radiation‐induced changes of internal obturator muscles after radiotherapy for prostate cancer.…”

Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications

“…65–69 In the context of lung cancer, radiomics has successfully allowed detection of malignancies in screening CT scans, 53 provided a means to differentiate between benign and malignant lesions, 51 enabled the prediction of risk of recurrence post-therapy, 52 and provided a means to noninvasively assess response to therapy 70 as well as helped to identify patients who would most benefit from therapy. 28 …”