Prediction of prognosis of immune checkpoint inhibitors combined with anti-angiogenic agents for unresectable hepatocellular carcinoma by machine learning-based radiomics.
Authors
Affiliations (7)
Affiliations (7)
- Department of Radiology, Shaoxing Central Hospital, The Central Affiliated Hospital, Shaoxing University, Shaoxing, 312000, China.
- Department of Radiation and Chemotherapy Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
- Department of Pathology, Jinhua Municipal Central Hospital, Affiliated Jinhua Hospital, Zhejiang University School of Medicine, Jinhua, 321000, China.
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China.
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China. [email protected].
- Department of Radiation and Chemotherapy Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China. [email protected].
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China. [email protected].
Abstract
This study aims to develop and validate a novel radiomics model utilizing magnetic resonance imaging (MRI) to predict progression-free survival (PFS) in patients with unresectable hepatocellular carcinoma (uHCC) who are receiving a combination of immune checkpoint inhibitors (ICIs) and antiangiogenic agents. This is an area that has not been previously explored using MRI-based radiomics. 111 patients with uHCC were enrolled in this study. After performing univariate cox regression and the least absolute shrinkage and selection operator (LASSO) algorithms to extract radiological features, the Rad-score was calculated through a Cox proportional hazards regression model and a random survival forest (RSF) model. The optimal calculation method was selected by comparing the Harrell's concordance index (C-index) values. The Rad-score was then combined with independent clinical risk factors to create a nomogram. C-index, time-dependent receiver operating characteristics (ROC) curves, calibration curves, and decision curve analysis were employed to assess the forecast ability of the risk models. The combined nomogram incorporated independent clinical factors and Rad-score calculated by RSF demonstrated better prognosis prediction for PFS, with C-index of 0.846, 0.845, separately in the training and the validation cohorts. This indicates that our model performs well and has the potential to enable more precise patient stratification and personalized treatment strategies. Based on the risk level, the participants were classified into two distinct groups: the high-risk signature (HRS) group and the low-risk signature (LRS) group, with a significant difference between the groups (P < 0.01). The effective clinical-radiomics nomogram based on MRI imaging is a promising tool in predicting the prognosis in uHCC patients receiving ICIs combined with anti-angiogenic agents, potentially leading to more effective clinical outcomes.