Prediction of Response to Stereotactic Radiosurgery for Brain Metastases Using Convolutional Neural Networks

Abstract

Background: It is unclear whether radiomic phenotypes of brain metastases (BM) are related to radiation therapy prognosis. This study assessed whether a convolutional neural network (CNN)-based radiomics model which learned computer tomography (CT) image features with minimal preprocessing, could predict early response of BM to radiosurgery.

Materials and methods: Tumor images of 110 BM post stereotactic-radiosurgery (SRS) (within 3 months) were assessed (Response Evaluation Criteria in Solid Tumor, version 1.1) as responders (complete or partial response) or non-responders (stable or progressive disease). Datasets were axial planning CT images containing the tumor center, and the tumor response. Datasets were randomly assigned to training, validation, or evaluation groups repeatedly, to create 50 dataset combinations that were classified into five groups of 10 different dataset combinations with the same evaluation datasets. The CNN learned using training-group images and labels. Validation datasets were used to choose the model that best classified evaluation images as responders or non-responders.

Results: Of 110 tumors, 57 were classified as responders, and 53 as non-responders. The area under the receiver operating characteristic curve (AUC) of each CNN model for 50 dataset combinations ranged from 0.602 [95% confidence interval (CI)=36.5-83.9%] to 0.826 [95% CI, 64.3-100%]. The AUC of ensemble models, which averaged prediction results of 10 individual models within the same group, ranged from 0.761 (95% CI=55.2-97.1%) to 0.856 (95% CI=68.2-100%).

Conclusion: A CNN-based ensemble radiomics model accurately predicted SRS responses of unlearned BM images. Thus, CNN models are able to predict SRS prognoses from small datasets.

Keywords: Brain metastases; convolutional neural networks; machine learning; radiomics; radiosurgery.