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Abstract #3048

Survival Rate Prediction in Patients with Glioblastoma Multiforme, Using Dynamic Contrast Enhanced MRI and Nested Model Selection Technique

Hamed Moradi 1 , Azimeh Noorizadeh Dehkordi 2,3 , Siamak P Nejad-Davarani 4 , Reza Faghihi 1 , Brent Griffith 5 , Ali S Arbab 6 , Tom Mikkelsen 7 , Hamid Soltanian-Zadeh 5 , Lisa Scarpace 7 , and Hassan Bagher-Ebadian 5,8

1 Mechanical Engineering, Shiraz University, Shiraz, Fars, Iran, 2 Nuclear Engineering, Shahid Beheshti University, Tehran, Iran, 3 Nuclear Engineering and Science, Azad University of Najafabad, Najafabad, Isfahan, Iran, 4 Neurology, Henry Ford Hospital, Detroit, Michigan, United States, 5 Radiology and Research Administration, Henry Ford Hospital, Detroit, Michigan, United States, 6 GRU Cancer Center, Georgia Regents University, Atlanta, Georgia, United States, 7 Neurological Surgery, Henry Ford Hospital, Detroit, Michigan, United States, 8 Physics, Oakland University, Rochester, Michigan, United States

The purpose of this pilot study was to investigate the role of Nested Model Selection (NMS) technique in Dynamic Contrast Enhanced MRI (DCE-MRI) data analysis for predicting patient survival. This study investigates the predictive power of different permeability parameters from different nested models for survival of patients with Glioblastoma Multiforme. 20 treatment nave patients with GBM were studied. A Cox proportional hazards regression (CPHR) model was used to analyze the survival time of the patients. This study suggests an association between Ktrans, Kep and Ve of model 3 and the patient survival that may be of considerable clinical importance.

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