Multiparametric MRI-based Machine Learning Radiomics for Predicting Treatment Response to Transarterial
Wendi Kang1, Peiyun Tang2, Yingen Luo1
1Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
Rationale And Objectives:
To develop and validate multiple machine learning predictive models incorporating clinical features and pretreatment multiparametric magnetic resonance imaging (MRI) radiomic features for predicting treatment response to transarterial chemoembolization combined with molecular targeted therapy plus immunotherapy in unresectable hepatocellular carcinoma (HCC).
Materials And Methods:
This retrospective study involved 276 patients with unresectable HCC who received combination therapy from 4 medical centers. Patients were divided into one training cohort and two independent external validation cohorts. 16 radiomic features from six multiparametric MRI sequences and 2 clinical features were used to build six machine learning models. The models were evaluated using the area under the curve (AUC), decision curve analysis, and incremental predictive value.
Results:
Alpha-fetoprotein and neutrophil-to-lymphocyte ratio are clinical independent predictors of treatment response. In the training cohort and two external validation cohorts, the AUCs and 95% confidence intervals for predicting treatment response were respectively 0.782 (0.698-0.857) 0.695 (0.566-0.823), and 0.679 (0.542-0.810) for the clinical model; 0.942 (0.903-0.974), 0.869 (0.761-0.949), and 0.868 (0.769-0.942) for the radiomics model; and 0.956 (0.920-0.984), 0.895 (0.810-0.967), and 0.892 (0.804-0.957) for the combined clinical-radiomics model. In the three cohorts, the incremental predictive value of the radiomics model over the clinical model was 49.2% (P < 0.001), 28.8% (P < 0.001), and 31.5% (P < 0.001).
Conclusion:
The combined clinical-radiomics model may provide a reliable and non-invasive tool to predict individual treatment responses and guide and improve clinical decision-making in combination therapy of HCC patients.
