Shape Features of the Lesion Habitat to Differentiate Brain Tumor Progression from Pseudoprogression on Routine Multiparametric MRI: A Multisite Study

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 measuremen...

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Veröffentlicht in:American journal of neuroradiology : AJNR 2018-12, Vol.39 (12), p.2187-2193
Hauptverfasser: Ismail, M, Hill, V, Statsevych, V, Huang, R, Prasanna, P, Correa, R, Singh, G, Bera, K, Beig, N, Thawani, R, Madabhushi, A, Aahluwalia, M, Tiwari, P
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container_issue 12
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container_title American journal of neuroradiology : AJNR
container_volume 39
creator Ismail, M
Hill, V
Statsevych, V
Huang, R
Prasanna, P
Correa, R
Singh, G
Bera, K
Beig, N
Thawani, R
Madabhushi, A
Aahluwalia, M
Tiwari, P
description 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. A total of 105 glioblastoma studies from 2 institutions were analyzed, consisting of a training ( = 59) and an independent test ( = 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. 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. 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.
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subjects Adult
Adult Brain
Aged
Brain Neoplasms - diagnostic imaging
Brain Neoplasms - pathology
Cohort Studies
Diagnosis, Differential
Disease Progression
Female
Glioblastoma - diagnostic imaging
Glioblastoma - pathology
Humans
Imaging, Three-Dimensional - methods
Magnetic Resonance Imaging - methods
Male
Middle Aged
Neuroimaging - methods
Radiation Injuries - diagnostic imaging
Radiation Injuries - pathology
Retrospective Studies
Support Vector Machine
title Shape Features of the Lesion Habitat to Differentiate Brain Tumor Progression from Pseudoprogression on Routine Multiparametric MRI: A Multisite Study
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