AUTHOR=Fang Shiji , Lai Linqiang , Zhu Jinyu , Zheng Liyun , Xu Yuanyuan , Chen Weiqian , Wu Fazong , Wu Xulu , Chen Minjiang , Weng Qiaoyou , Ji Jiansong , Zhao Zhongwei , Tu Jianfei 

TITLE=A Radiomics Signature-Based Nomogram to Predict the Progression-Free Survival of Patients With Hepatocellular Carcinoma After Transcatheter Arterial Chemoembolization Plus Radiofrequency Ablation

JOURNAL=Frontiers in Molecular Biosciences

VOLUME=Volume 8 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2021.662366

DOI=10.3389/fmolb.2021.662366

ISSN=2296-889X

ABSTRACT=Objective
The study aimed to establish and evaluate an MRI-based radiomics signature-based nomogram for predicting the progression-free survival (PFS) of intermediate and advanced hepatocellular carcinoma (HCC) patients treated with transcatheter arterial chemoembolization (TACE) plus radiofrequency ablation (RFA).
Materials and Methods 
113 intermediate and advanced HCC patients treated with TACE and RFA were eligible for this study. Patients were classified into a training cohort (n=78 cases) and a validation cohort (n=35 cases). Radiomics features were extracted from T1WI images. Dimension reduction was conducted to select optimal features using the least absolute shrinkage and selection operator (LASSO). A Rad-score was calculated and used to classify the patients into high-risk and low-risk groups and further integrated into multivariate Cox analysis. Three prediction models and a nomogram were established, and the area under the curve (AUC) of operating characteristics, the concordance index (C-index), a calibration curve, a decision curve and a clinical impact curve were determined to verify their accuracy.
Results
The AUC of the radiomics signature-based model for PFS in the training cohort was 0.83 (95% CI: 0.73, 0.93) and 0.81 (95% CI: 0.66, 0.96) in the validation cohort. The median PFS of the low-risk group (30.4 (95% CI: 19.41-41.38)) was higher than that of the high-risk group (8.1 (95% CI: 4.41-11.79)) in the training cohort (log rank test, z=16.58, P<0.001) and was verified in the validation cohort. Multivariate Cox analysis showed that BCLC stage, AFP level, time interval and radiomics signatures were independent prognostic factors of PFS in the training cohort. The AUC of the clinical model based on clinical factors for PFS in the training cohort was 0.838 (95% CI: 0.731-0.946) and 0.830 (95% CI: 0.669-0.990) in the validation cohort. The C-index of the nomogram was 0.722 (95% CI: 0.657-0.786) in the training cohort and 0.821 (95% CI: 0.726-0.915) in the validation cohort. The calibration curve, decision curve and clinical impact curve showed that the nomogram can be used to accurately predict the PFS of patients.
Conclusion
A nomogram combining radiomics and clinical factors accurately predicted the PFS of intermediate and advanced HCC treated with TACE plus RFA.