OBJECTIVES: The ability to differentiate between brain tumor progression and 
radiation therapy induced necrosis is critical for appropriate patient 
management. In order to improve the differential diagnosis, we combined 
fluorine-18 2-fluoro-deoxyglucose positron emission tomography ((18)F-FDG PET), 
proton magnetic resonance spectroscopy ((1)H MRS) and histological data to 
develop a multi-parametric machine-learning model.
METHODS: We enrolled twelve post-therapy patients with grade 2 and 3 gliomas 
that were suspicious of tumor progression. All patients underwent (18)F-FDG PET 
and (1)H MRS. Maximal standardized uptake value (SUVmax) of the tumors and 
reference regions were obtained. Multiple 2D maps of choline (Cho), creatine 
(Cr), and N-acetylaspartate (NAA) of the tumors were generated. A support vector 
machine (SVM) learning model was established to take imaging biomarkers and 
histological data as input vectors. A combination of clinical follow-up and 
multiple sequential MRI studies served as the basis for assessing the clinical 
outcome. All vector combinations were evaluated for diagnostic accuracy and 
cross validation. The optimal cutoff value of individual parameters was 
calculated using Receiver operating characteristic (ROC) plots.
RESULTS: The SVM and ROC analyses both demonstrated that SUVmax of the lesion 
was the most significant single diagnostic parameter (75% accuracy) followed by 
Cho concentration (67% accuracy). SVM analysis of all paired parameters showed 
SUVmax and Cho concentration in combination could achieve 83% accuracy. SUVmax 
of the lesion paired with SUVmax of the white matter as well as the tumor Cho 
paired with the tumor Cr both showed 83% accuracy. These were the most 
significant paired diagnostic parameters of either modality. Combining all four 
parameters did not improve the results. However, addition of two more 
parameters, Cho and Cr of brain parenchyma contralateral to the tumor, increased 
the accuracy to 92%.
CONCLUSION: This study suggests that SVM models may improve detection of glioma 
progression more accurately than single parametric imaging methods.
RESEARCH SUPPORT: National Cancer Institute, Cancer Center Support Grant 
Supplement Award, Imaging Response Assessment Teams.