1. 59. A RADIOMICS-BASED MACHINE LEARNING MODEL FOR DISTINGUISHING RADIATION NECROSIS FROM PROGRESSION OF BRAIN METASTASES TREATED WITH STEREOTACTIC RADIOSURGERY (SRS). (4th August 2020) Authors: Chen, Xuguang; Parekh, Vishwa; Peng, Luke; Chan, Michael; Soike, Michael; McTyre, Emory; Jacobs, Michael; Kleinberg, Lawrence Journal: Neuro-oncology advances Issue: Volume 2(2020)Supplement 2 Page Start: ii12 Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
2. Multiparametric radiomic tissue signature and machine learning for distinguishing radiation necrosis from tumor progression after stereotactic radiosurgery. Issue 1 (25th October 2021) Authors: Chen, Xuguang; Parekh, Vishwa S; Peng, Luke; Chan, Michael D; Redmond, Kristin J; Soike, Michael; McTyre, Emory; Lin, Doris; Jacobs, Michael A; Kleinberg, Lawrence R Journal: Neuro-oncology advances Issue: Volume 3:Issue 1(2021) Page Start: Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗
3. NIMG-08. AN INTEGRATED INFORMATICS MODEL COMBINING CLINICAL FACTORS, RADIOMICS AND A NOVEL CONNECTOMICS FRAMEWORK TO DISTINGUISH PATHOLOGICALLY-PROVEN RADIONECROSIS FROM PROGRESSION IN TREATED BRAIN METASTASES. (14th November 2022) Authors: Lee, Emerson; Cao, Linda; Vishwa, Parekh; Chen, Scott; Redmond, Kristin; Peng, Luke; Michael, Jacobs; Kleinberg, Lawrence Journal: Neuro-oncology Issue: Volume 24(2022)Supplement 7 Page Start: vii163 Record Type: Journal Article View Content: Available online (eLD content is only available in our Reading Rooms) ↗