Radiomics-based nomogram as predictive model for prognosis of hepatocellular carcinoma with portal vein tumor thrombosis receiving radiotherapy - Huang, Yu-Ming; Wang, Tsang-En; Chen, Ming-Jen; Lin, Ching-Chung; Chang, Ching-Wei; Tai, Hung-Chi; Hsu, Shih-Ming; Chen, Yu-Jen (Frontiers in Oncology, 2022)
Background: To establish and validate a predictive model based on radiomic features, clinical features, and radiation therapy (RT) dosimetric parameters for overall survival (OS) in hepatocellular carcinoma (HCC) patients treated with RT for portal vein tumor thrombosis (PVTT). Methods: We retrospectively reviewed 131 patients. Patients were randomly divided into the training (n = 105) and validation (n = 26) cohorts. The clinical target volume was contoured on pre-RT computed tomography images and 48 textural features were extracted. Least absolute shrinkage and selection operator regression was used to determine the radiomic score (rad-score). A nomogram based on rad-score, clinical features, and dosimetric parameters was developed using the results of multivariate regression analysis. The predictive nomogram was evaluated using Harrell’s concordance index (C-index), area under curve (AUC), and calibration curve. Results: Two radiomic features were extracted to calculate the rad-score for the prediction of OS. The radiomics based nomogram had a better performance than clinical nomogram for the prediction of OS, with a C-index of 0.73 (95% CI, 0.67-0.79) and an AUC of 0.71 (95% CI, 0.62-0.79). The predictive accuracy was assessed by calibration curve. Conclusion: The radiomics based predictive model significantly improved OS prediction in HCC patients treated with RT for PVTT. This model should be included in HCC treatment strategy in all hospitals.