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SU‐F‐R‐17: Advancing Glioblastoma Multiforme (GBM) Recurrence Detection with MRI Image Texture Feature Extraction and Machine Learning
Author(s) -
Yu V,
Ruan D,
Nguyen D,
Kaprealian T,
Chin R,
Sheng K
Publication year - 2016
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4955789
Subject(s) - support vector machine , artificial intelligence , time point , glioblastoma , pattern recognition (psychology) , fluid attenuated inversion recovery , contrast (vision) , gray level , mathematics , entropy (arrow of time) , nuclear medicine , feature extraction , medicine , computer science , magnetic resonance imaging , radiology , image (mathematics) , physics , cancer research , quantum mechanics , acoustics
Purpose: To test the potential of early Glioblastoma Multiforme (GBM) recurrence detection utilizing image texture pattern analysis in serial MR images post primary treatment intervention. Methods: MR image‐sets of six time points prior to the confirmed recurrence diagnosis of a GBM patient were included in this study, with each time point containing T1 pre‐contrast, T1 post‐contrast, T2‐Flair, and T2‐TSE images. Eight Gray‐level co‐occurrence matrix (GLCM) texture features including Contrast, Correlation, Dissimilarity, Energy, Entropy, Homogeneity, Sum‐Average, and Variance were calculated from all images, resulting in a total of 32 features at each time point. A confirmed recurrent volume was contoured, along with an adjacent non‐recurrent region‐of‐interest (ROI) and both volumes were propagated to all prior time points via deformable image registration. A support vector machine (SVM) with radial‐basis‐function kernels was trained on the latest time point prior to the confirmed recurrence to construct a model for recurrence classification. The SVM model was then applied to all prior time points and the volumes classified as recurrence were obtained. Results: An increase in classified volume was observed over time as expected. The size of classified recurrence maintained at a stable level of approximately 0.1 cm 3 up to 272 days prior to confirmation. Noticeable volume increase to 0.44 cm 3 was demonstrated at 96 days prior, followed by significant increase to 1.57 cm 3 at 42 days prior. Visualization of the classified volume shows the merging of recurrence‐susceptible region as the volume change became noticeable. Conclusion: Image texture pattern analysis in serial MR images appears to be sensitive to detecting the recurrent GBM a long time before the recurrence is confirmed by a radiologist. The early detection may improve the efficacy of targeted intervention including radiosurgery. More patient cases will be included to create a generalizable classification model applicable to a larger patient cohort. NIH R43CA183390 and R01CA188300.NSF Graduate Research Fellowship DGE‐1144087