Multiparametric MRI-Based Machine Learning Radiomics Prognostic Models for Multifocal Hepatocellular Carcinoma Beyond Milan Criteria: A Retrospective Study.

PURPOSE

To develop machine learning radiomics models for preoperative risk stratification of multifocal hepatocellular carcinoma (MHCC) beyond Milan criteria.

METHODS

Patients with pathologically proven MHCC beyond Milan criteria between January 2015 and January 2019 were retrospectively included. Radiomic features were extracted from tumor, peritumor, and tumor-peritumor regions using multiparametric MRI (mpMRI). An unsupervised spectral clustering algorithm was used to identify radiomics-based patient subtypes. Radiomics risk scores (RRS) for overall survival (OS) and recurrence-free survival (RFS) were generated using supervised extreme gradient boosting (XGBoost)-LASSO Cox proportional hazard regression analysis. The Concordance index (C-Index) was used to evaluate the model performance in the training and validation sets.

RESULTS

A total of 156 patients were divided into training (n = 78) and validation (n = 78) groups. Two distinct unsupervised subtypes were identified using spectral clustering, and subtype B was associated with worse OS and poor RFS. Incorporating radiomics predictors into the conventional preoperative clinical-radiological features improved the OS prediction performance (training set: from 0.616 to 0.712; validation set: from 0.522 to 0.710), and RFS prediction (training set: from 0.653 to 0.735; validation set: from 0.574 to 0.698). The combined models showed good predictive performance for 5-year OS (AUC, 0.77) and RFS (AUC, 0.81) in the training set and for 5-year OS (AUC, 0.75) and RFS (AUC, 0.76) in the validation set.

CONCLUSION

Two preoperative models combining mpMRI-based clinico-radiological and radiomics predictors effectively predicted outcomes for patients with MHCC beyond the Milan criteria.