Machine Learning Radiomics for Predicting Response to MR-Guided Radiotherapy in Unresectable Hepatocellular
Ke Su1,2,3, Xin Liu1,4, Yue-Can Zeng5
1Department of Radiation Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, People's Republic of China.
Background:
This study was conducted to assess the efficacy and safety of magnetic resonance (MR)-guided hypofractionated radiotherapy in patients with unresectable hepatocellular carcinoma (HCC). Machine learning-based radiomics was utilized to predict responses in these patients.
Methods:
This retrospective study included 118 hCC patients who received MR-guided hypofractionated radiotherapy. The primary study endpoint was the objective response rate (ORR). Radiomics features were based on the gross tumor volume (GTV). K-means clustering was performed to differentiate cancer subtypes based on radiomics. Nine radiomics-utilizing machine learning models were built and validated internally through 5-fold cross-validation.
Results:
The ORR, median progression-free survival (mPFS), and median overall survival (mOS) were 54.4%, 21.7 months, and 40.7 months, respectively. No patient experienced Grade 3/4 adverse events. 1130 radiomics features were extracted from the GTV, of which 7 were included for further analysis. K-means clustering identified 2 subtypes based on the selected features. Subtype 1 had significantly higher response, longer mPFS, and longer mOS than Subtype 2. In both internal and external validations, the multi-layer perceptron (MLP) model demonstrated superior predictive performance for response, achieving a receiver operating characteristic-area under the curve (ROC-AUC) of 0.804 and 0.842, respectively.
Conclusion:
MR-guided radiotherapy was proven to be effective and safe for HCC. The machine learning radiomics model developed in this study could accurately predict the response of radiotherapy-treated inoperable HCC.
