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Research ArticleADULT BRAIN
Open Access

Computer-Extracted Texture Features to Distinguish Cerebral Radionecrosis from Recurrent Brain Tumors on Multiparametric MRI: A Feasibility Study

P. Tiwari, P. Prasanna, L. Wolansky, M. Pinho, M. Cohen, A.P. Nayate, A. Gupta, G. Singh, K. Hattanpaa, A. Sloan, L. Rogers and A. Madabhushi
American Journal of Neuroradiology September 2016, DOI: https://doi.org/10.3174/ajnr.A4931
P. Tiwari
From the Department of Biomedical Engineering (P.T., P.P., G.S., A.M.), Case Western Reserve University, Cleveland, Ohio; University Hospitals Case Medical Center (A.P.N., A.G., L.W., M.C., A.S., L.R.), Cleveland, Ohio; and University of Texas Southwestern Medical Center (M.P., K.H.), Dallas, Texas.
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P. Prasanna
From the Department of Biomedical Engineering (P.T., P.P., G.S., A.M.), Case Western Reserve University, Cleveland, Ohio; University Hospitals Case Medical Center (A.P.N., A.G., L.W., M.C., A.S., L.R.), Cleveland, Ohio; and University of Texas Southwestern Medical Center (M.P., K.H.), Dallas, Texas.
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L. Wolansky
From the Department of Biomedical Engineering (P.T., P.P., G.S., A.M.), Case Western Reserve University, Cleveland, Ohio; University Hospitals Case Medical Center (A.P.N., A.G., L.W., M.C., A.S., L.R.), Cleveland, Ohio; and University of Texas Southwestern Medical Center (M.P., K.H.), Dallas, Texas.
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M. Pinho
From the Department of Biomedical Engineering (P.T., P.P., G.S., A.M.), Case Western Reserve University, Cleveland, Ohio; University Hospitals Case Medical Center (A.P.N., A.G., L.W., M.C., A.S., L.R.), Cleveland, Ohio; and University of Texas Southwestern Medical Center (M.P., K.H.), Dallas, Texas.
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M. Cohen
From the Department of Biomedical Engineering (P.T., P.P., G.S., A.M.), Case Western Reserve University, Cleveland, Ohio; University Hospitals Case Medical Center (A.P.N., A.G., L.W., M.C., A.S., L.R.), Cleveland, Ohio; and University of Texas Southwestern Medical Center (M.P., K.H.), Dallas, Texas.
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A.P. Nayate
From the Department of Biomedical Engineering (P.T., P.P., G.S., A.M.), Case Western Reserve University, Cleveland, Ohio; University Hospitals Case Medical Center (A.P.N., A.G., L.W., M.C., A.S., L.R.), Cleveland, Ohio; and University of Texas Southwestern Medical Center (M.P., K.H.), Dallas, Texas.
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A. Gupta
From the Department of Biomedical Engineering (P.T., P.P., G.S., A.M.), Case Western Reserve University, Cleveland, Ohio; University Hospitals Case Medical Center (A.P.N., A.G., L.W., M.C., A.S., L.R.), Cleveland, Ohio; and University of Texas Southwestern Medical Center (M.P., K.H.), Dallas, Texas.
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G. Singh
From the Department of Biomedical Engineering (P.T., P.P., G.S., A.M.), Case Western Reserve University, Cleveland, Ohio; University Hospitals Case Medical Center (A.P.N., A.G., L.W., M.C., A.S., L.R.), Cleveland, Ohio; and University of Texas Southwestern Medical Center (M.P., K.H.), Dallas, Texas.
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K. Hattanpaa
From the Department of Biomedical Engineering (P.T., P.P., G.S., A.M.), Case Western Reserve University, Cleveland, Ohio; University Hospitals Case Medical Center (A.P.N., A.G., L.W., M.C., A.S., L.R.), Cleveland, Ohio; and University of Texas Southwestern Medical Center (M.P., K.H.), Dallas, Texas.
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A. Sloan
From the Department of Biomedical Engineering (P.T., P.P., G.S., A.M.), Case Western Reserve University, Cleveland, Ohio; University Hospitals Case Medical Center (A.P.N., A.G., L.W., M.C., A.S., L.R.), Cleveland, Ohio; and University of Texas Southwestern Medical Center (M.P., K.H.), Dallas, Texas.
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L. Rogers
From the Department of Biomedical Engineering (P.T., P.P., G.S., A.M.), Case Western Reserve University, Cleveland, Ohio; University Hospitals Case Medical Center (A.P.N., A.G., L.W., M.C., A.S., L.R.), Cleveland, Ohio; and University of Texas Southwestern Medical Center (M.P., K.H.), Dallas, Texas.
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A. Madabhushi
From the Department of Biomedical Engineering (P.T., P.P., G.S., A.M.), Case Western Reserve University, Cleveland, Ohio; University Hospitals Case Medical Center (A.P.N., A.G., L.W., M.C., A.S., L.R.), Cleveland, Ohio; and University of Texas Southwestern Medical Center (M.P., K.H.), Dallas, Texas.
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Abstract

BACKGROUND AND PURPOSE: Despite availability of advanced imaging, distinguishing radiation necrosis from recurrent brain tumors noninvasively is a big challenge in neuro-oncology. Our aim was to determine the feasibility of radiomic (computer-extracted texture) features in differentiating radiation necrosis from recurrent brain tumors on routine MR imaging (gadolinium T1WI, T2WI, FLAIR).

MATERIALS AND METHODS: A retrospective study of brain tumor MR imaging performed 9 months (or later) post-radiochemotherapy was performed from 2 institutions. Fifty-eight patient studies were analyzed, consisting of a training (n = 43) cohort from one institution and an independent test (n = 15) cohort from another, with surgical histologic findings confirmed by an experienced neuropathologist at the respective institutions. Brain lesions on MR imaging were manually annotated by an expert neuroradiologist. A set of radiomic features was extracted for every lesion on each MR imaging sequence: gadolinium T1WI, T2WI, and FLAIR. Feature selection was used to identify the top 5 most discriminating features for every MR imaging sequence on the training cohort. These features were then evaluated on the test cohort by a support vector machine classifier. The classification performance was compared against diagnostic reads by 2 expert neuroradiologists who had access to the same MR imaging sequences (gadolinium T1WI, T2WI, and FLAIR) as the classifier.

RESULTS: On the training cohort, the area under the receiver operating characteristic curve was highest for FLAIR with 0.79; 95% CI, 0.77–0.81 for primary (n = 22); and 0.79, 95% CI, 0.75–0.83 for metastatic subgroups (n = 21). Of the 15 studies in the holdout cohort, the support vector machine classifier identified 12 of 15 studies correctly, while neuroradiologist 1 diagnosed 7 of 15 and neuroradiologist 2 diagnosed 8 of 15 studies correctly, respectively.

CONCLUSIONS: Our preliminary results suggest that radiomic features may provide complementary diagnostic information on routine MR imaging sequences that may improve the distinction of radiation necrosis from recurrence for both primary and metastatic brain tumors.

Abbreviations

AUC
area under receiver operating characteristic curve
Gd
gadolinium
mRmR
minimum redundancy and maximum relevance
RN
radiation necrosis
RT
radiation therapy
SVM
support vector machine
  • © 2016 American Society of Neuroradiology

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Computer-Extracted Texture Features to Distinguish Cerebral Radionecrosis from Recurrent Brain Tumors on Multiparametric MRI: A Feasibility Study
P. Tiwari, P. Prasanna, L. Wolansky, M. Pinho, M. Cohen, A.P. Nayate, A. Gupta, G. Singh, K. Hattanpaa, A. Sloan, L. Rogers, A. Madabhushi
American Journal of Neuroradiology Sep 2016, DOI: 10.3174/ajnr.A4931

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Computer-Extracted Texture Features to Distinguish Cerebral Radionecrosis from Recurrent Brain Tumors on Multiparametric MRI: A Feasibility Study
P. Tiwari, P. Prasanna, L. Wolansky, M. Pinho, M. Cohen, A.P. Nayate, A. Gupta, G. Singh, K. Hattanpaa, A. Sloan, L. Rogers, A. Madabhushi
American Journal of Neuroradiology Sep 2016, DOI: 10.3174/ajnr.A4931
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