RT Journal Article
SR Electronic
T1 Shape Features of the Lesion Habitat to Differentiate Brain Tumor Progression from Pseudoprogression on Routine Multiparametric MRI: A Multisite Study
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP 2187
OP 2193
DO 10.3174/ajnr.A5858
VO 39
IS 12
A1 M. Ismail
A1 V. Hill
A1 V. Statsevych
A1 R. Huang
A1 P. Prasanna
A1 R. Correa
A1 G. Singh
A1 K. Bera
A1 N. Beig
A1 R. Thawani
A1 A. Madabhushi
A1 M. Aahluwalia
A1 P. Tiwari
YR 2018
UL http://www.ajnr.org/content/39/12/2187.abstract
AB BACKGROUND AND PURPOSE: Differentiating pseudoprogression, a radiation-induced treatment effect, from tumor progression on imaging is a substantial challenge in glioblastoma management. Unfortunately, guidelines set by the Response Assessment in Neuro-Oncology criteria are based solely on bidirectional diametric measurements of enhancement observed on T1WI and T2WI/FLAIR scans. We hypothesized that quantitative 3D shape features of the enhancing lesion on T1WI, and T2WI/FLAIR hyperintensities (together called the lesion habitat) can more comprehensively capture pathophysiologic differences across pseudoprogression and tumor recurrence, not appreciable on diametric measurements alone.MATERIALS AND METHODS: A total of 105 glioblastoma studies from 2 institutions were analyzed, consisting of a training (n = 59) and an independent test (n = 46) cohort. For every study, expert delineation of the lesion habitat (T1WI enhancing lesion and T2WI/FLAIR hyperintense perilesional region) was obtained, followed by extraction of 30 shape features capturing 14 “global” contour characteristics and 16 “local” curvature measures for every habitat region. Feature selection was used to identify most discriminative features on the training cohort, which were evaluated on the test cohort using a support vector machine classifier.RESULTS: The top 2 most discriminative features were identified as local features capturing total curvature of the enhancing lesion and curvedness of the T2WI/FLAIR hyperintense perilesional region. Using top features from the training cohort (training accuracy = 91.5%), we obtained an accuracy of 90.2% on the test set in distinguishing pseudoprogression from tumor progression.CONCLUSIONS: Our preliminary results suggest that 3D shape attributes from the lesion habitat can differentially express across pseudoprogression and tumor progression and could be used to distinguish these radiographically similar pathologies.CcurvednessGdgadoliniumKTmeasure of the total curvaturePsPpseudoprogressionRANOResponse Assessment in Neuro-OncologySsharpnessSIshape indexSVMsupport vector machineTPtumor progression