Imaging of intratumoral heterogeneity in high-grade glioma

Hu, Leland; Hawkins-Daarud, Andrea; Wang, Lujia; Li, Jing; Swanson, Kristin R.

doi:10.1016/j.canlet.2020.02.025

Cited by 95 publications

(144 citation statements)

References 97 publications

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“…At another end, new methods will emerge to provide better representations for the encoded inputs via concepts like networks deconvolution, inversion, and dissection, among others (see [ 95 ]). Fifth and last, in order to face the challenge of intratumor heterogeneity, the quantification of tumor abundance at the voxel level is becoming an important direction in response assessment and recurrence risk studies [ 4 , 95 , 96 , 97 ]. This might help the identification of subregions, for instance, those metabolically active and defined as high risk [ 92 ], and may also inspire strategy to mitigate the effects of unbalanced data (for instance, when an outcome is over-represented) and thus decisional bias [ 12 ].…”

Section: Discussionmentioning

confidence: 99%

Radiomics at a Glance: A Few Lessons Learned from Learning Approaches

Capobianco

Deng

2020

Cancers

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Processing and modeling medical images have traditionally represented complex tasks requiring multidisciplinary collaboration. The advent of radiomics has assigned a central role to quantitative data analytics targeting medical image features algorithmically extracted from large volumes of images. Apart from the ultimate goal of supporting diagnostic, prognostic, and therapeutic decisions, radiomics is computationally attractive due to specific strengths: scalability, efficiency, and precision. Optimization is achieved by highly sophisticated statistical and machine learning algorithms, but it is especially deep learning that stands out as the leading inference approach. Various types of hybrid learning can be considered when building complex integrative approaches aimed to deliver gains in accuracy for both classification and prediction tasks. This perspective reviews some selected learning methods by focusing on both their significance for radiomics and their unveiled potential.

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Section: Discussionmentioning

confidence: 99%

Radiomics at a Glance: A Few Lessons Learned from Learning Approaches

Capobianco

Deng

2020

Cancers

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“…The authors also described different cell densities in the necrotic center, the contrast-enhancing rim, and in the perilesional area [ 34 , 35 ], which are radiological regions of interest in all image exams in the context of neuro-oncology. Although the radiological aspects of glioma heterogeneity are beyond the scope of this review, it is important to note that advances in neuroimaging techniques such as magnetic resonance image (MRI) sequences improved the guidance for stereotactic biopsies, and predictions based on texture analysis and machine learning algorithms are advancing radiomics to a new field of radiogenomics [ 36 , 37 , 38 ].…”

Section: Reviewmentioning

confidence: 99%

Tumor Heterogeneity in Glioblastomas: From Light Microscopy to Molecular Pathology

Becker

Sells

Haque

et al. 2021

Cancers

117

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One of the main reasons for the aggressive behavior of glioblastoma (GBM) is its intrinsic intra-tumor heterogeneity, characterized by the presence of clonal and subclonal differentiated tumor cell populations, glioma stem cells, and components of the tumor microenvironment, which affect multiple hallmark cellular functions in cancer. “Tumor Heterogeneity” usually encompasses both inter-tumor heterogeneity (population-level differences); and intra-tumor heterogeneity (differences within individual tumors). Tumor heterogeneity may be assessed in a single time point (spatial heterogeneity) or along the clinical evolution of GBM (longitudinal heterogeneity). Molecular methods may detect clonal and subclonal alterations to describe tumor evolution, even when samples from multiple areas are collected in the same time point (spatial-temporal heterogeneity). In GBM, although the inter-tumor mutational landscape is relatively homogeneous, intra-tumor heterogeneity is a striking feature of this tumor. In this review, we will address briefly the inter-tumor heterogeneity of the CNS tumors that yielded the current glioma classification. Next, we will take a deeper dive in the intra-tumor heterogeneity of GBMs, which directly affects prognosis and response to treatment. Our approach aims to follow technological developments, allowing for characterization of intra-tumor heterogeneity, beginning with differences on histomorphology of GBM and ending with molecular alterations observed at single-cell level.

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“…This process can be studied using non‐invasive techniques, such as MRI, from the time of tumour diagnosis 6–11 . Hence, functional MRI techniques allow researchers and clinicians to study relevant vascular markers with prognostic, predictive and stratification capabilities.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, functional MRI techniques allow researchers and clinicians to study relevant vascular markers with prognostic, predictive and stratification capabilities. In particular, the relative cerebral blood volume (rCBV) which is the most consistently recognized independent predictor of survival 6–11 . For this, the haemodynamic tissue signature (HTS), a machine learning technology, is able to automatically define regions of interest within the tumour and the oedema based on the morphologic and perfusion analysis of the MRI of the patient.…”

Section: Introductionmentioning

confidence: 99%

“…The evident efficacy of the rCBV has already been proven when dealing with clinical challenges such as diagnosis, non‐invasive characterization of molecular profile and prediction of prognosis, amongst others, 20 but the influence of this marker for LTSs and STSs has not yet been fully assessed. To elucidate this, we analyse in our work the prognostic and stratification capabilities of rCBV mean at the HAT habitat for the LTS and STS groups, independently.…”

Section: Introductionmentioning

confidence: 99%

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Differential effect of vascularity between long‐ and short‐term survivors with IDH1/2 wild‐type glioblastoma

et al. 2021

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Introduction IDH1/2 wt glioblastoma (GB) represents the most lethal tumour of the central nervous system. Tumour vascularity is associated with overall survival (OS), and the clinical relevance of vascular markers, such as rCBV, has already been validated. Nevertheless, molecular and clinical factors may have different influences on the beneficial effect of a favourable vascular signature. Purpose To evaluate the association between the rCBV and OS of IDH1/2 wt GB patients for long‐term survivors (LTSs) and short‐term survivors (STSs). Given that initial high rCBV may affect the patient's OS in follow‐up stages, we will assess whether a moderate vascularity is beneficial for OS in both groups of patients. Materials and methods Ninety‐nine IDH1/2 wt GB patients were divided into LTSs (OS ≥ 400 days) and STSs (OS < 400 days). Mann‐Whitney and Fisher, uni‐ and multiparametric Cox, Aalen’s additive regression and Kaplan‐Meier tests were carried out. Tumour vascularity was represented by the mean rCBV of the high angiogenic tumour (HAT) habitat computed through the haemodynamic tissue signature methodology (available on the ONCOhabitats platform). Results For LTSs, we found a significant association between a moderate value of rCBVmean and higher OS (uni‐ and multiparametric Cox and Aalen’s regression) (p = 0.0140, HR = 1.19; p = 0.0085, HR = 1.22) and significant stratification capability (p = 0.0343). For the STS group, no association between rCBVmean and survival was observed. Moreover, no significant differences (p > 0.05) in gender, age, resection status, chemoradiation, or MGMT methylation were observed between LTSs and STSs. Conclusion We have found different prognostic and stratification effects of the vascular marker for the LTS and STS groups. We propose the use of rCBVmean at HAT as a vascular marker clinically relevant for LTSs with IDH1/2 wt GB and maybe as a potential target for randomized clinical trials focused on this group of patients.

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Imaging of intratumoral heterogeneity in high-grade glioma

Cited by 95 publications

References 97 publications

Radiomics at a Glance: A Few Lessons Learned from Learning Approaches

Radiomics at a Glance: A Few Lessons Learned from Learning Approaches

Tumor Heterogeneity in Glioblastomas: From Light Microscopy to Molecular Pathology

Differential effect of vascularity between long‐ and short‐term survivors with IDH1/2 wild‐type glioblastoma

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