Radiomics-based nomogram as predictive model for prognosis of hepatocellular carcinoma with portal vein tumor thrombosis receiving radiotherapy

BackgroundThis study aims to establish and validate a predictive model based on radiomics 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). MethodsWe 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. The least absolute shrinkage and selection operator regression was used to determine the radiomics 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 the curve (AUC), and calibration curve. ResultsTwo radiomics features were extracted to calculate the rad-score for the prediction of OS. The radiomics-based nomogram had better performance than the 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 a calibration curve. ConclusionThe radiomics-based predictive model significantly improved OS prediction in HCC patients treated with RT for PVTT.

Automated summary

The predictive model based on radiomics features, clinical features, and radiation therapy dosimetric parameters significantly improved overall survival prediction in HCC patients treated with RT for PVTT.