Noninvasive Quantification of Axonal Loss in the Presence of Tissue Swelling in Traumatic Spinal Cord Injury Mice

Lin, Tsen-Hsuan; Sun, Peng; Hallman, Mitchell; Hwang, Fay C; Wallendorf, Michael; Ray, Wilson Z.; Spees, William M.; Song, Sheng‐Kwei

doi:10.1089/neu.2018.6016

Cited by 21 publications

(22 citation statements)

References 43 publications

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“…To address this critically important unmet need, we developed diffusion basis spectrum imaging (DBSI), which utilizes a data-driven multiple-tensor modeling approach to disentangle pathology and structural profiles within an image voxel (13,18-22). Although DBSI-derived structural metrics distinguish and quantify various tissue pathologies in an array of CNS disorders (13,19,23-26), the ability of DBSI to detect tissue microstructure alone is insufficient to accurately identify the underlying GBM pathologies of high tumor cellularity, tumor necrosis, and tumor-infiltrated white matter. Thus, we have developed a novel Diffusion Histology Imaging (DHI) approach , which applies machine/deep learning algorithms (27,28) using DBSI structural metrics as the input classifiers, to accurately model underlying GBM pathologies.…”

Section: Methodsmentioning

confidence: 99%

Diffusion Histology Imaging Combining Diffusion Basis Spectrum Imaging (DBSI) and Machine Learning Improves Detection and Classification of Glioblastoma Pathology

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Liu

et al. 2019

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PurposeGlioblastoma (GBM) is one of the deadliest cancers with no cure. While conventional MRI has been widely adopted for examining GBM clinically, accurate neuroimaging assessment of tumor histopathology for improved diagnosis, surgical planning, and treatment evaluation, remains an unmet need in the clinical management of GBMs.Experimental DesignWe employ a novel Diffusion Histology Imaging (DHI) approach, combining diffusion basis spectrum imaging (DBSI) and machine learning, to detect, differentiate, and quantify areas of high cellularity, tumor necrosis, and tumor infiltration in GBM.ResultsGd-enhanced T1W or hyper-intense FLAIR failed to reflect the morphological complexity underlying tumor in GBM patients. Contrary to the conventional wisdom that apparent diffusion coefficient (ADC) negatively correlates with increased tumor cellularity, we demonstrate disagreement between ADC and histologically confirmed tumor cellularity in glioblastoma specimens, whereas DBSI-derived restricted isotropic diffusion fraction positively correlated with tumor cellularity in the same specimens. By incorporating DBSI metrics as classifiers for a supervised machine learning algorithm, we accurately predicted high tumor cellularity, tumor necrosis, and tumor infiltration with 87.5%, 89.0% and 93.4% accuracy, respectively.ConclusionOur results suggest that DHI could serve as a favorable alternative to current neuroimaging techniques for guiding biopsy or surgery as well as monitoring therapeutic response in the treatment of glioblastoma.Translational RelevanceCurrent clinical diagnosis, surgical planning, and assessment of treatment response for GBM patients relies heavily on gadolinium-enhanced T1-weighted MRI, which is non-specific for tumor growth and merely reflects a disrupted blood-brain barrier. The complex tumor microenvironment and spatial heterogeneity make GBM difficult to characterize using current clinical imaging modalities. In this study, we developed a novel imaging technique to characterize and accurately predict key histological features of GBM - high tumor cellularity, tumor necrosis, and tumor infiltration. While further validation in a larger cohort of patients is needed, the current proof-of-concept approach could provide a solution to resolve important clinical questions such as the identification of true tumor progression vs. pseudoprogression or radiation necrosis.

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Section: Methodsmentioning

confidence: 99%

Diffusion Histology Imaging Combining Diffusion Basis Spectrum Imaging (DBSI) and Machine Learning Improves Detection and Classification of Glioblastoma Pathology

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Liu

et al. 2019

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“…Consistent with previous reports (2,17,20,21,31,32), we followed individual mice over time using DBSI. We observed that daily oral ngolimod markedly reduced in ammation, evidenced by the decrease in DBSI restricted isotropic fraction (re ecting cellular in ammation) and non-restricted isotropic fraction (re ecting vasogenic edema), in accordance with the action of ngolimod to sequester lymphocytes in secondary lymphoid tissue and thus inhibit lymphocytes from entering the CNS (33).…”

Section: Discussionmentioning

confidence: 55%

Diffusion Basis Spectrum Imaging Measures Anti-Inflammatory and Neuroprotective Effects of Fingolimod on Murine Optic Neuritis

Yang

Lin

Zhan

et al. 2021

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Background A readily implemented noninvasive imaging modality for evaluating underlying disease pathology of optic neuritis (ON) and effectiveness of therapeutics in people with CNS demyelinating diseases is currently lacking. This study aims to prospectively determine whether diffusion basis spectrum imaging (DBSI) detects, differentiates and quantitates coexisting inflammation, demyelination, axonal injury and axon loss in mice with ON due to experimental autoimmune encephalomyelitis (EAE), and to determine if DBSI accurately measures effects of fingolimod on underlying pathology.Methods EAE was induced in 7-week-old C57BL/6 female mice. Visual acuity (VA) was assessed daily to detect onset of ON (VA ≤ 0.25 cycle/degree of either eye) after which daily oral treatment with either fingolimod (1 mg/kg) or saline was given for ten weeks. In vivo DBSI scans of optic nerves were performed at baseline (before immunization), 2-, 6- and 10-weeks post treatment. DBSI-derived metrics including restricted isotropic diffusion tensor fraction (putatively reflecting cellularity), non-restricted isotropic diffusion tensor fraction (putative reflecting vasogenic edema), DBSI-derived axonal volume, axial diffusivity, λ∥ (putative reflecting axonal integrity), and increased radial diffusivity, λ⊥ (putatively reflecting demyelination). Mice were killed immediately after the last DBSI scan for immunohistochemical assessment.Results Optic nerves of fingolimod-treated mice exhibited significantly higher (p < 0.05) VA scores than saline-treated group at each time point. During ten-week of treatment, DBSI-derived non-restricted and restricted isotropic diffusion tensor fractions, and axonal volumes were not significantly different (p > 0.05) from the baseline values in fingolimod-treated mice, suggesting protection in the fingolimod treated mice. In contrast, in the saline-treated mice, transient DBSI-λ∥ decrease and DBSI-λ⊥ increase were also detected during Fingolimod treatment. DBSI-derived metrics assessed in vivo significantly correlated (p < 0.05) with the corresponding histological markers.Conclusions DBSI was used to assess changes of the underlying optic nerve pathologies in EAE mice with ON, exhibiting great potential as a noninvasive outcome measure for monitoring disease progression and therapeutic efficacy for MS.

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“…We previously developed diffusion basis spectrum imaging (DBSI), which utilizes a data-driven multiple-tensor modeling approach to deconvolute cellular and structural profiles within an image voxel (24). DBSI-derived structural metrics distinguish and quantify various pathologies in an array of central nervous system (CNS) disorders (25)(26)(27)(28)(29). In this study, we examine whether DBSI-derived metrics reflect structural and cellular changes associated with PCa.…”

Section: Introductionmentioning

confidence: 99%

Deep neural network analysis employing diffusion basis spectrum imaging metrics as classifiers improves prostate cancer detection and grading

Yang

et al. 2021

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Structural and cellular complexity of prostatic histopathology limits the accuracy of noninvasive detection and grading of prostate cancer (PCa). We addressed this limitation by employing a novel diffusion basis spectrum imaging (DBSI) to derive structurally-specific diffusion fingerprints reflecting various underlying prostatic structural and cellular components. We further developed diffusion histology imaging (DHI) by combining DBSI-derived structural fingerprints with a deep neural network (DNN) algorithm to more accurately classify different histopathological features and predict tumor grade in PCa. We examined 243 patients suspected with PCa using in vivo DBSI. The in vivo DBSI-derived diffusion metrics detected coexisting prostatic pathologies distinguishing inflammation, PCa, and benign prostatic hyperplasia. DHI distinguished PCa from benign peripheral and transition zone tissues with over 95% sensitivity and specificity. DHI also demonstrated over 90% sensitivity and specificity for Gleason score noninvasively. We present DHI as a novel diagnostic tool capable of noninvasive detection and grading of PCa.

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Noninvasive Quantification of Axonal Loss in the Presence of Tissue Swelling in Traumatic Spinal Cord Injury Mice

Cited by 21 publications

References 43 publications

Diffusion Histology Imaging Combining Diffusion Basis Spectrum Imaging (DBSI) and Machine Learning Improves Detection and Classification of Glioblastoma Pathology

Diffusion Histology Imaging Combining Diffusion Basis Spectrum Imaging (DBSI) and Machine Learning Improves Detection and Classification of Glioblastoma Pathology

Diffusion Basis Spectrum Imaging Measures Anti-Inflammatory and Neuroprotective Effects of Fingolimod on Murine Optic Neuritis

Deep neural network analysis employing diffusion basis spectrum imaging metrics as classifiers improves prostate cancer detection and grading

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