A Multiparametric Model for Mapping Cellularity in Glioblastoma Using Radiographically Localized Biopsies

Chang, Peter D.; Malone, Hani; Bowden, Stephen; Chow, Daniel; Gill, Brian J A; Ung, Timothy H.; Samanamud, Jorge; Englander, Zachary; Sonabend, Aarón; Sheth, Sameer A.; McKhann, Guy M.; Sisti, Michael B.; Schwartz, Larry; Lignelli, Angela; Grinband, Jack; Bruce, Jeffrey N.; Canoll, Peter

doi:10.3174/ajnr.a5112

Cited by 101 publications

(81 citation statements)

References 27 publications

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“…Other work 16 proposed a novel concept of imaging habitats that quantified distinctive tumor sub-regions by its local contrast enhancement, edema, and cellularity in MRI. Moreover, a recent study mearured the relationships between MR signal and cell desity using radiographically localized biopsies 40 , revealing that T2-FLAIR and ADC sequences were inversely correlated with cell density. Table 1 highlights several feature descriptors with their clinical potential.…”

Section: Quantitative Image Feature Extractionmentioning

confidence: 99%

Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches

Zhou¹,

Scott

Chaudhury

et al. 2017

AJNR Am J Neuroradiol

327

213

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Radiomics describes a broad set of computational methods that extract quantitative features from radiographic images. The resulting features can be used to inform imaging diagnosis, prognosis, and therapy response in oncology. However, major challenges remain for methodological developments to optimize feature extraction and provide rapid information flow in clinical settings. Equally important, to be clinically useful, predictive radiomic properties must be clearly linked to meaningful biological characteristics as well as qualitative imaging properties familiar to radiologist. Here we use a cross-disciplinary approach to highlight studies in radiomics. We review brain tumor radiological studies (e.g., imaging interpretation) through computational models (e.g., computer vision and machine learning) that provide novel clinical insights. In particular, we outline current quantitative image feature extraction and prediction strategies with different level of available clinical classes for supporting clinical decision making. We further discuss machine learning challenges and data opportunities to advance radiomic studies.

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Section: Quantitative Image Feature Extractionmentioning

confidence: 99%

Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches

Zhou¹,

Scott

Chaudhury

et al. 2017

AJNR Am J Neuroradiol

327

213

View full text Add to dashboard Cite

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“…Nevertheless, it is possible that the BOLD value extracted from the presumed biopsy site could be displaced by Ն1 voxel from the true biopsy location. However, we recently showed that our sampling method has registration accuracy to within ϳ2 mm, 18 suggesting that this type of error would have minimal effect on our results.…”

Section: Figmentioning

confidence: 99%

“…We have recently shown that this method results in spatial accuracy to within ϳ2 mm. 18 Biopsies that were taken from radiologically defined areas of necrosis or those that were predominantly histologically necrotic (Ͼ75% of the tissue sample) were excluded from the analysis.…”

Section: Biopsy Acquisitionmentioning

confidence: 99%

“…Total cell density was calculated using an automated cellcounting algorithm. 18 Briefly, the H&E-stained sections were digitalized using a Leica scn400 system (Leica Biosystems, Buffalo Grove, Illinois), with hematoxylin-stained nuclei counted in iteratively processed high-power fields. Total cell density was computed as the median number of hematoxylin-stained cells across all high-power fields that contained at least 1 nucleus.…”

Section: Histopathologic Analysismentioning

confidence: 99%

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Local Glioma Cells Are Associated with Vascular Dysregulation

Bowden

Gill

Englander

et al. 2018

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE:Malignant glioma is a highly infiltrative malignancy that causes variable disruptions to the structure and function of the cerebrovasculature. While many of these structural disruptions have known correlative histopathologic alterations, the mechanisms underlying vascular dysfunction identified by resting-state blood oxygen level-dependent imaging are not yet known. The purpose of this study was to characterize the alterations that correlate with a blood oxygen level-dependent biomarker of vascular dysregulation.

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“…DWI and PWI can give some guidance: DWI due to a decrease in the Brownian motion of water molecules if cell density in the tumour is high and PWI attributable to an increase in rCBV due to a pathological neoangiogenesis. [64,101] There is, however, no perfect imaging technique when it comes to identifying non-enhancing infiltrative tumour; histopathology is the gold standard.…”

Section: Paper IImentioning

confidence: 99%

Clinical Applications of Synthetic MRI of the Brain

Blystad¹

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Magnetic Resonance Imaging (MRI) has a high soft-tissue contrast with a high sensitivity for detecting pathological changes in the brain. Conventional MRI is a time-consuming method with multiple scans that relies on the visual assessment of the neuroradiologist. Synthetic MRI uses one scan to produce conventional images, but also quantitative maps based on relaxometry, that can be used to quantitatively analyse tissue properties and pathological changes. The studies presented here apply the use of synthetic MRI of the brain in different clinical settings. In the first study, synthetic MR images were compared to conventional MR images in 22 patients. The contrast, the contrast-to-noise ratio, and the diagnostic quality were assessed. Image quality was perceived to be inferior in the synthetic images, but synthetic images agreed with the clinical diagnoses to the same extent as the conventional images. Patients with early multiple sclerosis were analysed in the second study. In patients with multiple sclerosis, contrast-enhancing white matter lesions are a sign of active disease and can indicate a need for a change in therapy. Gadolinium-based contrast agents are used to detect active lesions, but concern has been raised regarding the long-term effects of repeated use of gadolinium. In this study, relaxometry was used to evaluate whether pre-contrast injection tissue-relaxation rates and proton density can identify active lesions without gadolinium. The findings suggest that active lesions often have relaxation times and proton density that differ from non-enhancing lesions, but with some overlap. This makes it difficult to replace gadolinium-based contrast agent injection with synthetic MRI in the monitoring of MS patients. Malignant gliomas are primary brain tumours with contrast enhancement due to a defective blood-brain barrier. However, they also grow in an infiltrative, diffuse manner, making it difficult to clearly delineate them from surrounding normal brain tissue in the diagnostic workup, at surgery, and during follow-up. The contrast-enhancing part of the tumour is easily visualised, but not the diffuse infiltration. In studies three and four, synthetic MRI was used to analyse the peritumoral area of malignant gliomas, and revealed quantitative findings regarding peritumoral relaxation changes and non-visible contrast enhancement suggestive of non-visible infiltrative tumour growth. In conclusion, synthetic MRI provides quantitative information about the brain tissue and this could improve the diagnosis and treatment for patients

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A Multiparametric Model for Mapping Cellularity in Glioblastoma Using Radiographically Localized Biopsies

Cited by 101 publications

References 27 publications

Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches

Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches

Local Glioma Cells Are Associated with Vascular Dysregulation

Clinical Applications of Synthetic MRI of the Brain

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