Utility of radiomics for predicting patient survival in hepatocellular carcinoma with portal vein tumor thrombosis treated with stereotactic body radiotherapy

Abstract

Objective This study aimed to develop and validate the combination of radiomic features and clinical characteristics that can predict patient survival in HCC with PVTT treated with SBRT. Materials and Methods The prediction model was developed in a primary cohort of 70 patients with HCC and PVTT treated with SBRT, using data acquired between December 2015 and June 2017. The radiomic features were extracted from computed tomography (CT) scans. A least absolute shrinkage and selection operator regression model was used to build the radiomic feature. Multivariate Cox-regression hazard models were created for analyzing survival outcomes and the radiomic features and clinical characteristics were presented with a nomogram. The area under the curve (AUC) of the receiver operating characteristic curve was used to evaluate the model. Participants were divided into a high-risk group and a low-risk group based on the radiomic features. Results A total of seven radiomic features and five clinical characteristics were extracted for survival analysis. A combination of the radiomic features and clinical characteristics resulted in better performance for the estimation of overall survival (OS) [AUC = 0.859 (CI: 0.770–0.948)] than that with clinical characteristics alone [AUC = 0.761 (CI: 0.641–0.881)]. These patients were divided into high-risk and low-risk groups according to the radiomic features. Conclusion This study demonstrated that a nomogram of combined radiomic features and clinical characteristics can be conveniently used to facilitate individualized preoperative prediction of OS in patients with HCC with PVTT before SBRT.

Deceased Donor Liver Transplantation After Radioembolization for Hepatocellular Carcinoma and Portal Vein Tumoral Thrombosis: A Pilot Study

“…Hence, it is important to control the disease not only locally but also systemically, before (38) and after LT. (39) Furthermore, a rare case of isolated cardiac metastasis without venous spreading from the liver (40) has occurred in a patient enlisted for LT. All these cases should alert physicians when downstaging very advanced HCCs, taking into account that the available imaging techniques may be not accurate enough in evaluating response to treatment. (41) Although the high recurrence rate after LT raises important ethical questions on the use of deceased…”

“…Hence, the importance of controlling the disease not only locally but also systemically, before (38) and after LT.(39) Furthermore, a rare case of isolated cardiac metastasis without venous spreading from the liver (40) has occurred in one patient enlisted for LT. All these cases should alert physicians when downstaging very advanced HCCs, taking into account that the available imaging techniques may be not accurate enough in evaluating response to treatment. (41) Although the high recurrence rate after LT raises important ethical questions on the use of deceased donors for patients with HCC and portal vein invasion, it has to be said however that most of recurrences occurred in the lungs and could be surgically treated. A 5-year OS of 60% after DDLT is one of the longest survivals reported in the literature, considering the potential for 5 years of follow-up and the fact that one patient died in a car accident 5 months after LT, thus contributing to decrease the survival rate in the transplanted group.…”

Stereotactic body radiation therapy for hepatocellular carcinoma: From infancy to ongoing maturity

“…Radiomic features have been explored as potential biomarkers to predict prognosis in HCC and are an emerging field that could enable treatment personalisation. 128 , 129 Trials are underway on the use of radiomics to assess prognosis and to guide surgical decisions ( NCT02757846 , NCT03917017 ). Incorporation of radiomic features in target delineation may improve accuracy by identifying high-risk areas of microscopic spread beyond the gross tumour, predicting microinvasion and even enabling auto segmentation.…”