Mass Effect Deformation Heterogeneity (MEDH) on Gadolinium-contrast T1-weighted MRI is associated with decreased survival in patients with right cerebral hemisphere Glioblastoma: A feasibility study

Prasanna, Prateek; Mitra, Jhimli; Beig, Niha; Nayate, Ameya; Patel, Jay; Ghose, Soumya; Thawani, Rajat; Partovi, Sasan; Madabhushi, Anant; Tiwari, Pallavi

doi:10.1038/s41598-018-37615-2

Cited by 20 publications

(19 citation statements)

References 62 publications

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“…45 Deformation descriptors are another class of functional radiomics markers which seek to measure tissue deformation in the brain parenchyma due to mass effect. 46 These features provide an insight into the microenvironment outside the visible surgical margins.…”

Section: Radiomic Feature Groupsmentioning

confidence: 99%

“…Prasanna et al 46 discussed how mass effect-induced deformation heterogeneity (MEDH) from glioblastoma on multi-sequence MRI affects survival. High expression of MEDH in the areas of language comprehension, social cognition, visual perception, emotion, somatosensory, cognitive and motor-control functions were found to be associated with worse survival (Fig.…”

Section: Prognostic Applicationsmentioning

confidence: 99%

“…The colormaps show the negative correlation values (shown as positive for easier representation). Figure from Prasanna et al 46 ; licensed under a Creative Commons Attribution (CC BY) license. …”

Section: Prognostic Applicationsmentioning

confidence: 99%

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Radiomics and radiogenomics in gliomas: a contemporary update

et al. 2021

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The natural history and treatment landscape of primary brain tumours are complicated by the varied tumour behaviour of primary or secondary gliomas (high-grade transformation of low-grade lesions), as well as the dilemmas with identification of radiation necrosis, tumour progression, and pseudoprogression on MRI. Radiomics and radiogenomics promise to offer precise diagnosis, predict prognosis, and assess tumour response to modern chemotherapy/immunotherapy and radiation therapy. This is achieved by a triumvirate of morphological, textural, and functional signatures, derived from a high-throughput extraction of quantitative voxel-level MR image metrics. However, the lack of standardisation of acquisition parameters and inconsistent methodology between working groups have made validations unreliable, hence multi-centre studies involving heterogenous study populations are warranted. We elucidate novel radiomic and radiogenomic workflow concepts and state-of-the-art descriptors in sub-visual MR image processing, with relevant literature on applications of such machine learning techniques in glioma management.

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Section: Radiomic Feature Groupsmentioning

confidence: 99%

Section: Prognostic Applicationsmentioning

confidence: 99%

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Radiomics and radiogenomics in gliomas: a contemporary update

et al. 2021

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“…The dMRI was fitted with a tensor model to produce an FA map using the FSL diffusion toolbox (FDT) 44 . The FA maps were then nonlinearly co-registered to the MNI-152 space FA template using the deformable function of ANTs, which is shown to outperform the default deformable registration tools FNIRT 45 of TBSS in the co-registration of FA 46 and pathology-bearing T1 images 47 , and more importantly, could mitigate the deformation of the brain with tumor, by accounting for the tumor mass effect 48 . To minimize the bias of the signal-noise ratio introduced by the different MRI acquisition protocols, we normalized the FA map using the MRI intensity histogram-matching method 49 .…”

Section: Methodsmentioning

confidence: 99%

Structural connectome quantifies tumor invasion and predicts survival in glioblastoma patients

Wei

Cui

et al. 2021

Preprint

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Background: Glioblastoma is characterized by extensive invasion into brain parenchymal tissue through white matter tracts. Systematically quantifying invasion, however, is limited by the conventional imaging tools, and could potentially be achieved by a structural connectome approach. Methods: Two prospective patient cohorts of newly diagnosed glioblastoma were included for network construction. A fiber template was firstly derived by employing probabilistic tractography on healthy subjects. Through performing tract-based spatial statistics in patients and age-matched controls, the connectivity strength of each fiber was estimated in patients for network construction. Contrast-enhancing and non-enhancing tumors were segmented and overlaid to the network to identify connectome disruption in lesion and distant areas. The connectome disruption probabilities were calculated across all patients. Disruption indices and network topological features were examined using survival models. Results: Compared to healthy controls, patient networks demonstrated lower clustering coefficient, but higher characteristic path length (each P < 0.001). Higher distant area disruption (HR = 1.43, P = 0.027) and characteristic path length (HR = 1.59, P = 0.031) were associated with worse survival, while higher clustering coefficient (HR = 0.59, P = 0.016) was associated with prolonged survival. Conclusion: The occult invasion in glioblastoma could be identified and quantified using structural connectome, which may confer benefits to precise patient management.

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“…The per-voxel deformation measurements from the BAT region are then used as radiomic features for analysis. Prasanna et al 70 hypothesized that larger variations in deformation within functional areas of the contralateral hemisphere are likely related to decreased survival in GBMs. They demonstrated that decreased OS was found to be associated with increased deformation in areas of language comprehension, visual perception, cognitive, and motor-control functions, particularly in the memory areas in the left hemisphere.…”

Section: Overview Of Radiomic and Radiogenomics Pipelinementioning

confidence: 99%

Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges

Beig

Bera

Tiwari

2020

Neuro-Oncology Advances

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Neuro-oncology largely consists of malignancies of the brain and central nervous system including both primary as well as metastatic tumors. Currently, a significant clinical challenge in neuro-oncology is to tailor therapies for patients based on a priori knowledge of their survival outcome or treatment response to conventional or experimental therapies. Radiomics or the quantitative extraction of subvisual data from conventional radiographic imaging has recently emerged as a powerful data-driven approach to offer insights into clinically relevant questions related to diagnosis, prediction, prognosis, as well as assessing treatment response. Furthermore, radiogenomic approaches provide a mechanism to establish statistical correlations of radiomic features with point mutations and next-generation sequencing data to further leverage the potential of routine MRI scans to serve as “virtual biopsy” maps. In this review, we provide an introduction to radiomic and radiogenomic approaches in neuro-oncology, including a brief description of the workflow involving preprocessing, tumor segmentation, and extraction of “hand-crafted” features from the segmented region of interest, as well as identifying radiogenomic associations that could ultimately lead to the development of reliable prognostic and predictive models in neuro-oncology applications. Lastly, we discuss the promise of radiomics and radiogenomic approaches in personalizing treatment decisions in neuro-oncology, as well as the challenges with clinical adoption, which will rely heavily on their demonstrated resilience to nonstandardization in imaging protocols across sites and scanners, as well as in their ability to demonstrate reproducibility across large multi-institutional cohorts.

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Mass Effect Deformation Heterogeneity (MEDH) on Gadolinium-contrast T1-weighted MRI is associated with decreased survival in patients with right cerebral hemisphere Glioblastoma: A feasibility study

Cited by 20 publications

References 62 publications

Radiomics and radiogenomics in gliomas: a contemporary update

Radiomics and radiogenomics in gliomas: a contemporary update

Structural connectome quantifies tumor invasion and predicts survival in glioblastoma patients

Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges

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