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Deep-learned time-signal intensity pattern analysis using an autoencoder captures magnetic resonance perfusion heterogeneity for brain tumor differentiation

 Ji Eun Park  ;  Ho Sung Kim  ;  Junkyu Lee  ;  E-Nae Cheong  ;  Ilah Shin  ;  Sung Soo Ahn  ;  Woo Hyun Shim 
 SCIENTIFIC REPORTS, Vol.10(1) : 21485, 2020-12 
Journal Title
Issue Date
Adult ; Aged ; Brain Neoplasms / diagnosis ; Central Nervous System Neoplasms / diagnosis ; Contrast Media ; Databases, Factual ; Diagnosis, Differential ; Female ; Glioblastoma / diagnosis ; Humans ; Image Processing, Computer-Assisted / methods* ; Lymphoma / diagnosis ; Lymphoma, Non-Hodgkin / diagnosis ; Magnetic Resonance Imaging / methods* ; Male ; Middle Aged ; Neoplasms / diagnostic imaging* ; Perfusion ; Retrospective Studies
Current image processing methods for dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) do not capture complex dynamic information of time-signal intensity curves. We investigated whether an autoencoder-based pattern analysis of DSC MRI captured representative temporal features that improves tissue characterization and tumor diagnosis in a multicenter setting. The autoencoder was applied to the time-signal intensity curves to obtain representative temporal patterns, which were subsequently learned by a convolutional neural network. This network was trained with 216 preoperative DSC MRI acquisitions and validated using external data (n = 43) collected with different DSC acquisition protocols. The autoencoder applied to time-signal intensity curves and clustering obtained nine representative clusters of temporal patterns, which accurately identified tumor and non-tumoral tissues. The dominant clusters of temporal patterns distinguished primary central nervous system lymphoma (PCNSL) from glioblastoma (AUC 0.89) and metastasis from glioblastoma (AUC 0.95). The autoencoder captured DSC time-signal intensity patterns that improved identification of tumoral tissues and differentiation of tumor type and was generalizable across centers.
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1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Shin, Ilah(신일아)
Ahn, Sung Soo(안성수) ORCID logo https://orcid.org/0000-0002-0503-5558
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