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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