Use of radiomics for the prediction of local control of brain metastases after stereotactic radiosurgery. Issue 6 (20th January 2020)

• Record Type: Journal Article
• Authors: Mouraviev, Andrei; Detsky, Jay; Sahgal, Arjun; Ruschin, Mark; Lee, Young K; Karam, Irene; Heyn, Chris; Stanisz, Greg J; Martel, Anne L
• Is Part Of: Neuro-oncology. Volume 22:Issue 6(2020)
• Page Start: 797
• Page End: 805
• Publication Date: 2020-01-20
• Subjects: brain metastases -- radiomics -- response prediction -- neuro-oncology -- local control; Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noaa007
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: Background: Local response prediction for brain metastases (BM) after stereotactic radiosurgery (SRS) is challenging, particularly for smaller BM, as existing criteria are based solely on unidimensional measurements. This investigation sought to determine whether radiomic features provide additional value to routinely available clinical and dosimetric variables to predict local recurrence following SRS. Methods: Analyzed were 408 BM in 87 patients treated with SRS. A total of 440 radiomic features were extracted from the tumor core and the peritumoral regions, using the baseline pretreatment volumetric post-contrast T1 (T1c) and volumetric T2 fluid-attenuated inversion recovery (FLAIR) MRI sequences. Local tumor progression was determined based on Response Assessment in Neuro-Oncology‒BM criteria, with a maximum axial diameter growth of >20% on the follow-up T1c indicating local failure. The top radiomic features were determined based on resampled random forest (RF) feature importance. An RF classifier was trained using each set of features and evaluated using the area under the receiver operating characteristic curve (AUC). Results: The addition of any one of the top 10 radiomic features to the set of clinical features resulted in a statistically significant ( P < 0.001) increase in the AUC. An optimized combination of radiomic and clinical features resulted in a 19% higher resampled AUC (mean = 0.793; 95% CI = 0.792–0.795) than clinical features alone (0.669, 0.668–0.671). Conclusions: The increase in AUC of the RF classifier, after incorporating radiomic features, suggests that quantitative characterization of tumor appearance on pretreatment T1c and FLAIR adds value to known clinical and dosimetric variables for predicting local failure.