Prognostic Value of a CT Radiomics-Based Nomogram for the Overall Survival of Patients with Nonmetastatic BCLC Stage C Hepatocellular Carcinoma after Stereotactic Body Radiotherapy

Abstract

Purpose. This study aimed to investigatie the feasibility of pretherapeutic CT radiomics-based nomograms to predict the overall survival (OS) of patients with nondistant metastatic Barcelona Clinic Liver Cancer stage C (BCLC-C) hepatocellular carcinoma (HCC) undergoing stereotactic body radiotherapy (SBRT). Methods. A retrospective review of 137 patients with nondistant metastatic BCLC-C HCC who underwent SBRT was made. Radiomics features distilled from pretherapeutic CT images were selected by the method of LASSO regression for radiomics signature construction. Then, the clinical model was constructed based on clinical characteristics. A radiomics nomogram was constructed using the radiomics score (Rad-score) and clinical characteristics to predict post-SBRT OS in BCLC-C HCC patients. An analysis of discriminatory ability and calibration was performed to confirm the efficacy of the radiomics nomogram. Results. In order to construct the radiomic signature, seven significant features were selected. Patients were divided into low-risk (Rad-score < −0.03) and high-risk (Rad-score ≥ −0.03) groups based on the best Rad-score cutoff value. There were statistically significant differences in OS both in the training set ( p < 0.0001 ) and the validation set ( p = 0.03 ) after stratification. The C-indexes of the radiomics nomogram were 0.77 (95% CI: 0.72–0.82) in the training set and 0.71 (95% CI: 0.61–0.81) in the validation set, which outperformed the clinical model and radiomics signature. An AUC of 0.76, 0.79, and 0.84 was reached for 6-, 12-, and 18-month survival predictions, respectively. Conclusions. The predictive nomogram that combines radiomic features with clinical characteristics has great prospects for application in the prediction of post-SBRT OS in nondistant metastatic BCLC-C HCC patients.

Feasibility Study of Computed Tomographic Radiomics Model for the Prediction of Early and Intermediate Stage Hepatocellular Carcinoma Using BCLC Staging

“…The results showed that the integrated model combining the RS and clinical factors could more effectively predict RFS after the LR of BCLC stage 0 to B stage HCC patients and could effectively stratify patients according to prognostic risk. Wang et al 12 investigated the feasibility of pretherapeutic CT radiomics-based nomograms for predicting the overall survival of patients with nondistant metastatic BCLC stage C HCC who underwent stereotactic body radiotherapy, and the resulting model has great potential. Both studies above demonstrate that CT radiomics can be used to predict the therapeutic effect and prognosis of HCC patients in different BCLC stages with different treatment modalities.…”

A Predictive Study on the Content of Epigallocatechin Gallate (EGCG) in Yunnan Large Leaf Tea Trees Based on the Nomogram Model

“…The AIC serves as a standard for assessing the quality of fit of statistical models. It is built upon the principle of entropy and establishes a benchmark for evaluating the complexity of the estimated model and the accuracy of the fitted data [32,33]. When selecting the optimal model from a given set of models, typically, the model with the lowest AIC is chosen.…”