Development of a novel combined nomogram model integrating Rad-score, age and ECOG to predict the survival of patients with hepatocellular carcinoma treated by transcatheter arterial chemoembolization

Journal of Gastrointestinal Oncology

article

Aiai Liu¹  Bo Liu²  Xiaodong Duan³  Bo Yang⁴  Yiren Wang²  Ping Dong¹  Ping Zhou¹ 

[1] Department of Radiology, The Affiliated Hospital of Southwest Medical University;School of Nursing, Southwest Medical University;Department of Rehabilitation, The Affiliated Hospital of Southwest Medical University;Department of Interventional Medicine, The Affiliated Hospital of Southwest Medical University;Nursing Department, The Affiliated Hospital of Southwest Medical University

关键词: Hepatocellular carcinoma (HCC);    transcatheter arterial chemoembolization (TACE);    radiomics;    nomogram;    survival prediction;   

DOI  :  10.21037/jgo-22-548

学科分类：肿瘤学

来源: Pioneer Bioscience Publishing Company

【 摘 要 】

Background: Liver cancer is affecting more and more people's health. Transcatheter arterial chemoembolization (TACE) has become a routine treatment option, but the prognosis of patients is not optimistic. Effectively prediction of prognosis can provide clinicians with an objective basis for patient prognosis and timely adjustment of treatment strategies, thus improving the quality of patient survival. However, the current prediction methods have some limitations. Therefore, this study aims to develop a radiomics nomogram for predicting survival after TACE in patients with advanced hepatocellular carcinoma (HCC). Methods: Seventy advanced HCC patients treated with TACE were enrolled from January 2013 to July 2019. Clinical information included age, sex, and Eastern Cooperative Oncology Group (ECOG) score. Overall survival (OS) was confirmed by postoperative follow-up. Radiomics features were extracted using 3D Slicer (version 4.11.20210226) software, then obtain radiomics signature and calculate radiomics score (Rad-score) for each patient. Univariate and multivariate Cox regression were used to analyze the baseline clinical data of patients and establish clinical models. The obtained radiomics signature was incorporated into the clinical model to establish the radiomics nomogram. The predictive performance and calibration ability of the model were assessed by the area under the receiver operating characteristic (ROC) curve (AUC), C-index, and calibration curve. Results: Three significant features were selected from 851 radiomics features by the least absolute shrinkage and selection operator (LASSO) Cox regression model to construct the radiomics signature, and were significantly correlated with overall survival (P<0.001). Rad-score, age, and ECOG score were combined to construct a radiomics nomogram. The AUC, sensitivity, and specificity of the radiomics nomogram were 0.801 (95% CI: 0.693–0.909), 0.822 (95% CI: 0.674–0.915), and 0.720 (95% CI: 0.674–0.915), respectively. The C-index of the radiomics nomogram was 0.700 (95% CI: 0.547–0.853). Calibration curves showed better agreement between the predicted and actual probabilities in the radiomics nomogram among the 3 features. Conclusions: The Rad-score was a strong risk predictor of survival after TACE for HCC patients. The radiomics nomogram might be improved the predictive efficacy of survival after TACE and it may also provide assistance to physicians in making treatment decisions.

【 授权许可】

Unknown