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

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

doi:10.3389/fmolb.2021.662366

Abstract

Objective: 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 ablationMaterials and Methods: 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 (n = 78 cases) and a validation cohort (n = 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.Results: 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, p < 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, p = 0.002], AFP level (HR: 2.01, 95% CI: 1.01–3.99 p = 0.046), time interval (HR: 0.48, 95% CI: 0.26–0.87, p = 0.016) and radiomics signature (HR 2.98, 95% CI: 1.60–5.51, p = 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), p＜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), p = 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.Conclusion: 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.

Highlights

Primary liver cancer (PLC) is the sixth most common malignant cancer and the fourth leading cause of cancer-related death worldwide (Bray et al, 2018)
There were no significant differences in sex, age, hepatitis B surface antigen (HBsAg), cirrhosis, BCLC stage, ChildPugh classification, AFP level, tumour diameter, node number, metastasis, portal vein thrombosis, transarterial arterial chemoembolization (TACE)-radiofrequency ablation (RFA) procedures and time interval between TACE and RFA treatment in the training and validation cohorts (Table 1)
Accuracy of Radiomics Signature Combined With Clinical Factors The concordance index (C-index) of the combined model in the training cohort was higher than that of clinical model [0.722 vs. 0.669, p < 0.001]. The C-index of the combined model in the validation cohort, was higher than that of clinical model [0.821 vs. 0.76, p 0.004]

Summary

Introduction

Primary liver cancer (PLC) is the sixth most common malignant cancer and the fourth leading cause of cancer-related death worldwide (Bray et al, 2018). A total of 854,000 new cases are diagnosed every year, and almost 50% of PLCs come from China, which places a heavy burden on Chinese health care (Chen et al, 2018). Hepatocellular carcinoma (HCC) accounts for 75–85% of PLC, which cause a heavy burden of death in China. 70% of patients with HCC are diagnosed with advanced HCC and have missed the best opportunity for surgery (European Association for the Study of the Liver, 2018). The 5 years recurrence rate of HCC is as high as 70%. Local interventional strategies such as transarterial arterial chemoembolization (TACE), radiofrequency ablation (RFA) and radioactive particle implantation are widely used in the treatment of advanced HCC (Makary et al, 2020). The combination of TACE and RFA therapy has a synergistic cytotoxic effect on HCC and results in better local tumour control and longer survival than TACE or RFA alone (Peng et al, 2013; Shimose et al, 2019; Chang et al, 2020; Yuan et al, 2021)

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Discussion

Conclusion