Journal of Hepatocellular Carcinoma (May 2022)
CT-Based Radiomics for the Recurrence Prediction of Hepatocellular Carcinoma After Surgical Resection
Abstract
Fang Wang,1,* Qingqing Chen,1,* Yuanyuan Zhang,1,2 Yinan Chen,3 Yajing Zhu,3 Wei Zhou,4 Xiao Liang,5 Yunjun Yang,6 Hongjie Hu1 1Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, People’s Republic of China; 2Medical College, Shaoxing University, Shaoxing, 312000, People’s Republic of China; 3SenseTime Research, Shanghai, 200030, People’s Republic of China; 4Department of Radiology, Huzhou Central Hospital, Affiliated to Huzhou University, Huzhou, 313000, People’s Republic of China; 5Department of General Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, People’s Republic of China; 6Department of Radiology, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Hongjie Hu, Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, People’s Republic of China, Tel/Fax +86-0571-86044817, Email [email protected] Yunjun Yang, Department of Radiology, The First Affiliated Hospital, Wenzhou Medical University, Wenzhou, People’s Republic of China, Email [email protected]: To explore the effectiveness of radiomics signature in predicting the recurrence of hepatocellular carcinoma (HCC) and the benefit of postoperative adjuvant transcatheter arterial chemoembolization (PA-TACE).Patients and Methods: In this multicenter retrospective study, 364 consecutive patients with multi-phase computed tomography (CT) images were included. Recurrence-related radiomics features of intra- and peritumoral regions were extracted from the pre-contrast, arterial and portal venous phase, respectively. The radiomics model was established in the training cohort (n = 187) using random survival forests analysis to output prediction probability as “Rad-score” and validated by the internal (n = 92) and external validation cohorts (n = 85). Besides, the Clinical nomogram was developed by clinical-radiologic-pathologic characteristics, and the Combined nomogram was further constructed to evaluate the added value of the Rad-score for individualized recurrence-free survival (RFS) prediction, which is our primary and only endpoint. The performance of the three models was assessed by the concordance index (C-index). Furthermore, all the patients were stratified into high- and low-risk groups of recurrence by the median value of the Rad-score to analyze the benefit of PA-TACE.Results: The model built using radiomics signature demonstrated favorable prediction of HCC recurrence across all datasets, with C-index of 0.892, 0.812, 0.809, separately in the training, the internal and external validation cohorts. Univariate and multivariate analysis revealed that the Rad-score was an independent prognostic factor. Significant differences were found between the high- and low-risk group in RFS prediction in all three cohorts. Further analysis showed that compared with the low-risk group, patients with the high-risk received more benefits from PA-TACE.Conclusion: The newly developed Rad-score was not only a powerful biomarker in predicting the RFS of HCC but also a strong stratification basis to explore the high-risk patients who could benefit from PA-TACE.Keywords: hepatocellular carcinoma, recurrence, postoperative adjuvant transcatheter arterial chemoembolization, radiomics
