Advanced MRI for Pediatric Brain Tumors with Emphasis on Clinical Benefits

Goo, Hyun Woo; Ra, Young-Shin

doi:10.3348/kjr.2017.18.1.194

Cited by 21 publications

(24 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Diffusion weighted imaging (DWI) and dynamic susceptibility contrast imaging (DSC) are two advanced magnetic resonance imaging (MRI) techniques available to understand tissue microstructure and perfusion on a cellular and tissue level (Goo and Ra, 2017;Shah et al, 2016;Zhou et al, 2011). These techniques have been used extensively to understand the role of cellularity and microvascular perfusion, in both paediatric and adult brain tumours (Hales et al, 2019;Poussaint et al, 2016), with strong correlations with histology for the aforementioned.…”

Section: Introductionmentioning

confidence: 99%

Distinguishing between paediatric brain tumour types using multi-parametric magnetic resonance imaging and machine learning: A multi-site study

Grist

Withey

MacPherson

et al. 2020

NeuroImage: Clinical

View full text Add to dashboard Cite

The imaging and subsequent accurate diagnosis of paediatric brain tumours presents a radiological challenge, with magnetic resonance imaging playing a key role in providing tumour specific imaging information. Diffusion weighted and perfusion imaging are commonly used to aid the non-invasive diagnosis of children's brain tumours, but are usually evaluated by expert qualitative review. Quantitative studies are mainly single centre and single modality.The aim of this work was to combine multi-centre diffusion and perfusion imaging, with machine learning, to develop machine learning based classifiers to discriminate between three common paediatric tumour types.The results show that diffusion and perfusion weighted imaging of both the tumour and whole brain provide significant features which differ between tumour types, and that combining these features gives the optimal machine learning classifier with >80% predictive precision. This work represents a step forward to aid in the non-invasive diagnosis of paediatric brain tumours, using advanced clinical imaging.

show abstract

Section: Introductionmentioning

confidence: 99%

Distinguishing between paediatric brain tumour types using multi-parametric magnetic resonance imaging and machine learning: A multi-site study

Grist

Withey

MacPherson

et al. 2020

NeuroImage: Clinical

View full text Add to dashboard Cite

show abstract

“…Evaluating pediatric brain tumors is often a diagnostic challenge due to their diverse tumor pathologies, nonspecific or overlapping imaging findings, susceptibility artifacts from intratumoral calcification or hemorrhage, and motion artifacts in young children (7). Conventional MRI-based diagnoses also fail to offer adequate information regarding the specific tumor type, tumor grade, tumor viability, and treatment response of lesions.…”

Section: Introductionmentioning

confidence: 99%

“…Conventional MRI-based diagnoses also fail to offer adequate information regarding the specific tumor type, tumor grade, tumor viability, and treatment response of lesions. Although advanced MRI techniques like diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), perfusion MRI, MR spectroscopy (MRS), and susceptibility-weighted imaging (SWI), are incorporated into clinical MRI protocols, they still fall short (7)(8)(9). The multiparametric imaging (mpMRI) approach still often fail to accurately reflect tumor histopathology such as lesion cellular density, necrosis, hemorrhage, and infiltrative edges.…”

Section: Introductionmentioning

confidence: 99%

Diffusion Basis Spectrum Imaging with Deep Neural Network Differentiates Distinct Histology in Pediatric Brain Tumors

Ye¹,

Srinivasa²,

Lin³

et al. 2020

Preprint

View full text Add to dashboard Cite

High-grade pediatric brain tumors constitute the highest mortality of cancer-death in children.While conventional MRI has been widely adopted for examining pediatric high-grade brain tumor clinically, accurate neuroimaging detection and differentiation of tumor histopathology for improved diagnosis, surgical planning, and treatment evaluation, remains an unmet need in the clinical management of pediatric brain tumor. We employed a novel Diffusion Histology Imaging (DHI) approach that incorporates diffusion basis spectrum imaging (DBSI) and deep neural network. DHI aims to detect, differentiate, and quantify heterogenous areas in pediatric highgrade brain tumors, which include normal white matter (WM), densely cellular tumor (DC tumor), less densely cellular tumor (LDC tumor), infiltrating edge, necrosis, and hemorrhage. Distinct diffusion metric combination would thus indicate the unique distributions of each distinct tumor histology features. DHI, by incorporating DBSI metrics and the deep neural network algorithm, classified pediatric tumor histology with an overall accuracy of 83.3%. Receiver operating analysis (ROC) analysis suggested DHI's great capability in distinguishing individual tumor histology with AUC values (95%CI) of 0.983 (0.985-0.989), 0.961 (0.957-0.964), 0.993 (0.992-0.994), 0.953 (0.947-0.958), 0.974 (0.970-0.978) and 0.980 (0.977-0.983) for normal WM, DC tumor, LDCtumor, infiltrating edge, necrosis and hemorrhage, respectively. Our results suggest that DBSI-DNN, or DHI, accurately characterized and classified multiple tumor histologic features in pediatric high-grade brain tumors. If further validated in patients, the novel DHI might emerge as a favorable alternative to the current neuroimaging techniques to better guide biopsy and resection as well as monitor therapeutic response in patients with high-grade brain tumors.

show abstract

“…However, these discoveries were developed largely for adult brain tumor patients, which are most often biologically distinct from those that occur in children, which exhibit unique genomic and imaging characteristics [5]. Evaluating pediatric brain tumors is often a diagnostic challenge due to their diverse tumor pathologies, nonspeci c or overlapping imaging ndings, susceptibility artifacts from intratumoral calci cation or hemorrhage, and motion artifacts in young children [6]. Conventional MRI-based diagnoses also fail to offer adequate information regarding the speci c tumor type, tumor grade, tumor viability, and treatment response of lesions.…”

Section: Introductionmentioning

confidence: 99%

“…Conventional MRI-based diagnoses also fail to offer adequate information regarding the speci c tumor type, tumor grade, tumor viability, and treatment response of lesions. Although advanced MRI techniques like diffusion tensor imaging (DTI), perfusion MRI, MR spectroscopy (MRS), and susceptibility-weighted imaging (SWI) are incorporated into clinical MRI protocols, they still fall short [6][7][8]. The widely used multiparametric magnetic resonance imaging (mpMRI) approach fails to accurately re ect tumor histopathology such as tumor cellular density, necrosis, hemorrhage, or in ltrative edges.…”

Section: Introductionmentioning

confidence: 99%

Diffusion Histology Imaging Differentiates Distinct Pediatric Brain Tumor Histology

Sriniv

Meyer

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: High-grade pediatric brain tumors exhibit the highest cancer mortality rates in children. While conventional MRI has been widely adopted for examining pediatric high-grade brain tumors clinically, accurate neuroimaging detection and differentiation of tumor histopathology for improved diagnosis, surgical planning, and treatment evaluation, remains an unmet need in their clinical management. Methods: We employed a novel Diffusion Histology Imaging (DHI) approach employing diffusion basis spectrum imaging (DBSI) derived metrics as the input classifiers for deep neural network analysis. DHI aims to detect, differentiate, and quantify heterogeneous areas in pediatric high-grade brain tumors, which include normal white matter (WM), densely cellular tumor, less densely cellular tumor, infiltrating edge, necrosis, and hemorrhage. Distinct diffusion metric combination would thus indicate the unique distributions of each distinct tumor histology features. Results: DHI, by incorporating DBSI metrics and the deep neural network algorithm, classified pediatric tumor histology with an overall accuracy of 83.3%. Receiver operating analysis (ROC) analysis suggested DHI’s great capability in distinguishing individual tumor histology with AUC values (95%CI) of 0.983 (0.985 - 0.989), 0.961 (0.957 - 0.964), 0.993 (0.992 - 0.994), 0.953 (0.947 - 0.958), 0.974 (0.970 - 0.978) and 0.980 (0.977 - 0.983) for normal WM, densely cellular tumor, less densely cellular tumor, infiltrating edge, necrosis and hemorrhage, respectively. Conclusions: Our results suggest that DBSI-DNN, or DHI, accurately characterized and classified multiple tumor histologic features in pediatric high-grade brain tumors. If these results could be further validated in patients, the novel DHI might emerge as a favorable alternative to the current neuroimaging techniques to better guide biopsy and resection as well as monitor therapeutic response in patients with high-grade brain tumors.

show abstract

Advanced MRI for Pediatric Brain Tumors with Emphasis on Clinical Benefits

Cited by 21 publications

References 41 publications

Distinguishing between paediatric brain tumour types using multi-parametric magnetic resonance imaging and machine learning: A multi-site study

Distinguishing between paediatric brain tumour types using multi-parametric magnetic resonance imaging and machine learning: A multi-site study

Diffusion Basis Spectrum Imaging with Deep Neural Network Differentiates Distinct Histology in Pediatric Brain Tumors

Diffusion Histology Imaging Differentiates Distinct Pediatric Brain Tumor Histology

Contact Info

Product

Resources

About