Predicting local failure of brain metastases after stereotactic radiosurgery with radiomics on planning MR images and dose maps. Issue 9 (31st July 2021)

• Record Type: Journal Article
• Authors: Wang, Hesheng; Xue, Jinyu; Qu, Tanxia; Bernstein, Kenneth; Chen, Ting; Barbee, David; Silverman, Joshua S.; Kondziolka, Douglas
• Is Part Of: Medical physics. Volume 48:Issue 9(2021)
• Page Start: 5522
• Page End: 5530
• Publication Date: 2021-07-31
• Subjects: brain metastases -- dose map -- local failure -- MRI -- radiomics -- stereotactic radiosurgery; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15110
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: Purpose: Stereotactic radiosurgery (SRS) has become an important modality in the treatment of brain metastases. The purpose of this study is to investigate the potential of radiomic features from planning magnetic resonance (MR) images and dose maps to predict local failure after SRS for brain metastases. Materials/Methods: Twenty‐eight patients who received Gamma Knife (GK) radiosurgery for brain metastases were retrospectively reviewed in this IRB‐approved study. 179 irradiated tumors included 42 that locally failed within one‐year follow‐up. Using SRS tumor volumes, radiomic features were calculated on T1‐weighted contrast‐enhanced MR images acquired for treatment planning and planned dose maps. 125 radiomic features regarding tumor shape, dose distribution, MR intensities and textures were extracted for each tumor. Logistic regression with automatic feature selection was built to predict tumor progression from local control after SRS. Feature selection and model evaluation using receiver operating characteristic (ROC) curves were performed in a nested cross validation (CV) scheme. The associations between selected radiomic features and treatment outcomes were statistically assessed by univariate analysis. Results: The logistic model with feature selection achieved ROC AUC of 0.82 ± 0.09 on 5‐fold CV, providing 83% sensitivity and 70% specificity for predicting local failure. A total of 10 radiomic features including 1 shape feature, 6 MR images and 3 dose distribution features were selected. These features were significantly associated with treatment outcomes ( p < 0.05). The model was validated on independent holdout data with an AUC of 0.78. Conclusions: Radiomic features from planning MR images and dose maps provided prognostic information in SRS for brain metastases. A model built on the radiomic features shows promise for early prediction of tumor local failure after treatment, potentially aiding in personalized care for brain metastases.