Prediction of response to Stereotactic Radiotherapy for Nonfunctioning Pituitary Adenoma using radiomic features

Abstract

Background: For individually predicting preoperative response to Stereotactic radiotherapy for Nonfunctioning pituitary Adenoma with the use of a radiomics approach.Methods: 93 cases (training set: n = 62; test set: n = 31) were recruited with contrast-enhanced T1-weighted MRI (CE-T1) before stereotactic radiotherapy. All of these patients received another MRI scan to assess sensitivity of radiotherapy after 12 to 18 months. The shrinkage and no increase in tumor volume are regarded as sensitive to gamma knife radiotherapy. According to CE-T1 images, we extracted 1208 quantitative imaging features totally. Support vector machine (SVM) combined with recursive feature elimination (RFE) and grid-search trained a four-feature prediction mode verified with an assay of receiver operating characteristics (ROC) for an individual set of test. In addition, a ROC curves with individual feature and signature bar were constructed for prediction.Results: The cross-validation area under the curve (AUC) on the three-fold train set is 0.991,0.843 and 0.889. In terms of the test and training sets, T1-CE image features led to 0.897 and 0.914 AUC, separately. Conclusions: With the use of a radiomics method, the response to Stereotactic Radiotherapy for Nonfunctioning Pituitary Adenoma was primarily predicted before the operation. The built mode performed well, suggesting that radiomics is promising to preoperatively predict sensitivity to radiotherapy in NFPA.

Radiomics of pituitary adenoma using computer vision: a review

“…For the feature selection, the authors have used very different mathematical methods for feature selection, while generally pursuing different goals. For example, Recursive Feature Elimination (RFE) for feature selection [9]. Only recent studies [7,13,15,25] used the calculation of deep radiomic features, i.e., deep learning using CNN.…”

“…• Random forest (RF) in papers [5,9,16,18,23,27,28,36,38], • Logistic regression (LR) in papers [16,22,28,28],…”