Histopathology-validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo-progression in glioblastoma.

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作者： Hamed,Akbari ^[1] ; Saima,Rathore ^[2] ; Spyridon,Bakas ^[1] ; MacLean P,Nasrallah ^[2] ; Gaurav,Shukla ^[1] ; Elizabeth,Mamourian ^[2] ; Martin,Rozycki ^[3] ; Stephen J,Bagley ^[3] ; Jeffrey D,Rudie ^[1] ; Adam E,Flanders ^[2] ; Adam P,Dicker ^[4] ; Arati S,Desai ^[5] ; Donald M,O'Rourke ^[1] ; Steven,Brem ^[2] ; Robert,Lustig ^[1] ; Suyash,Mohan ^[2] ; Ronald L,Wolf ^[6] ; Michel,Bilello ^[2] ; Maria,Martinez-Lage ^[7] ; Christos,Davatzikos ^[8]

作者单位： Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, Pennsylvania. ^[1] Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania. ^[2] Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania. ^[3] Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania. ^[4] Helen F. Graham Cancer Center and Research Institute, ChristianaCare, Newark, Delaware. ^[5] Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania. ^[6] Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pennsylvania. ^[7] Department of Radiation Oncology, Sidney Kimmel Medical College and Cancer Center, Thomas Jefferson University, Philadelphia, Pennsylvania. ^[8] Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania. ^[9] Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts. ^[10]

关键词 glioblastoma machine learning pseudo-progression radiographic biomarker true progression