A Machine Learning Model Predicts the Outcome of SRS for Residual Arteriovenous Malformations after Partial Embolization: A Real-World Clinical Obstacle.

OBJECTIVE

To propose a machine learning (ML) model predicting the favorable outcome of stereotactic radiosurgery (SRS) for residual brain arteriovenous malformation (bAVM) after partial embolization.

METHODS

One hundred and thirty bAVM patients who underwent partial embolization followed by SRS were reviewed retrospectively. Patients were split at random split into training datasets (n = 100) and testing datasets (n = 30). Radiomics and dosimetric features were extracted from pre-SRS treatment images. Feature selection was performed to select appropriate radiomics and dosimetric features. Three ML algorithms were applied to construct models using selected features respectively. A total of 9 models were trained to predict favorable outcomes (obliteration without complication) of bAVMs. The efficacy of these models was evaluated on the testing dataset using mean accuracy (ACC) and area under the receiver operating characteristic curve (AUC).

RESULTS

The obliteration rate of this cohort was 70.77% (92 of 130) with a mean follow-up of 43.8 months (range, 12-108 months). Favorable outcomes were achieved in 89 patients (68.46%). Four radiomics features and 7 dosimetric features were selected for ML model construction. The dosimetric support vector machines (SVM) model showed the best performance on the training dataset, with an ACC of 0.74 and AUC of 0.78. The dosimetric SVM model also showed the best performance on the testing dataset, with an ACC of 0.83 and AUC of 0.77.

CONCLUSIONS

Dosimetric features are good predictors of prognosis for patients with partially embolized bAVM followed by SRS therapy. The use of ML models is an innovative method for predicting favorable outcomes of partially embolized bAVM followed by SRS therapy.