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=8

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=<p><bold>Objective:</bold> The study aims to establish an magnetic resonance imaging radiomics signature-based nomogram for predicting the progression-free survival of intermediate and advanced hepatocellular carcinoma (HCC) patients treated with transcatheter arterial chemoembolization (TACE) plus radiofrequency ablation</p><p><bold>Materials and Methods:</bold> A total of 113 intermediate and advanced HCC patients treated with TACE and RFA were eligible for this study. Patients were classified into a training cohort (<italic>n</italic> = 78 cases) and a validation cohort (<italic>n</italic> = 35 cases). Radiomics features were extracted from contrast-enhanced T1W images by analysis kit software. 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. Two prediction models based on radiomics signature combined with or without clinical factors and a clinical model based on clinical factors were developed. A nomogram comcined radiomics signature and clinical factors were established and the concordance index (C-index) was used for measuring discrimination ability of the model, calibration curve was used for measuring calibration ability, and decision curve and clinical impact curve are used for measuring clinical utility.</p><p><bold>Results:</bold> Eight radiomics features were selected by LASSO, and the cut-off of the Rad-score was 1.62. The C-index of the radiomics signature for PFS was 0.646 (95%: 0.582–0.71) in the training cohort and 0.669 (95% CI:0.572–0.766) in validation cohort. The median PFS of the low-risk group [30.4 (95% CI: 19.41–41.38)] months was higher than that of the high-risk group [8.1 (95% CI: 4.41–11.79)] months in the training cohort (log rank test, z = 16.58, <italic>p</italic> &lt; 0.001) and was verified in the validation cohort. Multivariate Cox analysis showed that BCLC stage [hazard ratio (HR): 2.52, 95% CI: 1.42–4.47, <italic>p</italic> = 0.002], AFP level (HR: 2.01, 95% CI: 1.01–3.99 <italic>p</italic> = 0.046), time interval (HR: 0.48, 95% CI: 0.26–0.87, <italic>p</italic> = 0.016) and radiomics signature (HR 2.98, 95% CI: 1.60–5.51, <italic>p</italic> = 0.001) were independent prognostic factors of PFS in the training cohort. The C-index of the combined model in the training cohort was higher than that of clinical model for PFS prediction [0.722 (95% CI: 0.657–0.786) vs. 0.669 (95% CI: 0.657–0.786), <italic>p</italic>＜0.001]. Similarly, The C-index of the combined model in the validation cohort, was higher than that of clinical model [0.821 (95% CI: 0.726–0.915) vs. 0.76 (95% CI: 0.667–0.851), <italic>p</italic> = 0.004]. The calibration curve, decision curve and clinical impact curve showed that the nomogram can be used to accurately predict the PFS of patients.</p><p><bold>Conclusion:</bold> The radiomics signature was a prognostic risk factor, and a nomogram combined radiomics and clinical factors acts as a new strategy for predicted the PFS of intermediate and advanced HCC treated with TACE plus RFA.</p>