Prediction of Local Response After Stereotactic Radiosurgery for Brain Metastases Using Convolutional Neural Networks based radiomics

Abstract

A deep learning concept based on artificial convolutional neural networks (CNN) is regarded as an emerging radiomics methodology because it uses minimal amount of image preprocessing. The purpose of this study is to predict radiation response of metastatic brain tumor receiving stereotactic radiosurgery (SRS) using the radiomics model based on CNN. The BM lesions that received stereotactic radiosurgery (SRS) were reviewed and 110 tumors were selected for the study. On the planning CT images of the target tumors, a data-set was made by matching the single axial image which presents tumor center and the tumor label. The random assignment was repeated to generate 50 independent experimental combinations of data-sets. The same group is composed of 10 different data-sets combination methods, but the data-sets of the evaluation group are assigned equally. We trained the image of the training group and the matching label on the CNN we implemented. We used the validation data-sets to select the best-performing learning model. The results of this classification were compared to the matched label. CNN is a kind of artificial neural network in which the connectivity pattern between its neurons is inspired by the organization of visual cortex. The CNN of this study is implemented by TensorFlow which is a machine learning framework. The predicted AUC(area under the ROC curve) of individual CNN models for the 50 data-set combinations ranged from 0.602 [95% CI, 36.5% -83.9%] to a maximum of 0.826 [95% CI, 64.3% -100%]. -97.1%] to a maximum of 0.856 [95% CI, 68.2% - 100%]. CNN based ensemble radiomics models that learn BM 's SRS planning CT images and their early responses were able to predict SRS responses with high accuracy for unlearned BM images. This study showed for the first time the applicability of the CNN-based radiomics model to predict the prognosis of radiation therapy with small data.