‘Low grade glioma’: an update for radiologists

Larsen, Jennifer L.; Wharton, Stephen B.; McKevitt, Fiona; Romanowski, C.A.J.; Bridgewater, Caroline; Zaki, Hesham; Hoggard, Nigel

doi:10.1259/bjr.20160600

Cited by 23 publications

(15 citation statements)

References 86 publications

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“…Accurate segmentation of gliomas on clinical magnetic resonance imaging (MRI) scans plays an important role in the quantification and objectivation of diagnosis, treatment decision, and prognosis [2][3][4]. In current clinical practice, T1-weighted, post-contrast T1-weighted, T2-weighted, and T2-fluid attenuated inversion recovery (FLAIR) sequences are required to characterize the different components and to assess the infiltration of the surrounding brain parenchyma [5,6]. Glioma segmentation requires the distinguishing of tumor tissue from healthy surrounding tissues by the radiologist [7] and the segmented region of interest or volume of interest can be used to compute featurebased radiomics and quantifiable measurements [8,9].…”

Section: Introductionmentioning

confidence: 99%

Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis

et al. 2021

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Objectives Different machine learning algorithms (MLAs) for automated segmentation of gliomas have been reported in the literature. Automated segmentation of different tumor characteristics can be of added value for the diagnostic work-up and treatment planning. The purpose of this study was to provide an overview and meta-analysis of different MLA methods. Methods A systematic literature review and meta-analysis was performed on the eligible studies describing the segmentation of gliomas. Meta-analysis of the performance was conducted on the reported dice similarity coefficient (DSC) score of both the aggregated results as two subgroups (i.e., high-grade and low-grade gliomas). This study was registered in PROSPERO prior to initiation (CRD42020191033). Results After the literature search (n = 734), 42 studies were included in the systematic literature review. Ten studies were eligible for inclusion in the meta-analysis. Overall, the MLAs from the included studies showed an overall DSC score of 0.84 (95% CI: 0.82–0.86). In addition, a DSC score of 0.83 (95% CI: 0.80–0.87) and 0.82 (95% CI: 0.78–0.87) was observed for the automated glioma segmentation of the high-grade and low-grade gliomas, respectively. However, heterogeneity was considerably high between included studies, and publication bias was observed. Conclusion MLAs facilitating automated segmentation of gliomas show good accuracy, which is promising for future implementation in neuroradiology. However, before actual implementation, a few hurdles are yet to be overcome. It is crucial that quality guidelines are followed when reporting on MLAs, which includes validation on an external test set. Key Points • MLAs from the included studies showed an overall DSC score of 0.84 (95% CI: 0.82–0.86), indicating a good performance. • MLA performance was comparable when comparing the segmentation results of the high-grade gliomas and the low-grade gliomas. • For future studies using MLAs, it is crucial that quality guidelines are followed when reporting on MLAs, which includes validation on an external test set.

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Section: Introductionmentioning

confidence: 99%

Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis

et al. 2021

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“…Examples include perfusion MRI, diffusion tensor imaging (DTI) ( 34 ), functional MRI (resting state and task-based), MR spectroscopy (MRS) ( 35 ), and PET imaging with amino acid tracers ( 36 ). Intraoperative MRI has also been used to improve the extent of resection of LGGs when compared to traditional approaches ( 37 ). These imaging modalities have been influential in better understanding brain physiology and pathology.…”

Section: Toward a Possible Rct In Dlggsmentioning

confidence: 99%

“…However, we have yet to consolidate these modalities into a standard multi-parametric imaging protocol for DLGG management. This is further compounded by the difficulty of distinguishing tumor from peritumoral edema prior to surgery, which is an ongoing difficulty with current imaging techniques ( 37 ). This issue will only become more difficult with the advent of novel molecular signatures that might reveal further heterogeneity in DLGGs.…”

Section: Toward a Possible Rct In Dlggsmentioning

confidence: 99%

Management of Diffuse Low-Grade Glioma: The Renaissance of Robust Evidence

et al. 2020

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The surgical management of diffuse low-grade gliomas (DLGGs) has undergone a paradigm shift toward striving for maximal safe resection when feasible. While extensive observational data supports this transition, unbiased evidence in the form of high quality randomized-controlled trials (RCTs) is lacking. Furthermore, despite a high volume of molecular, genetic, and imaging data, the field of neuro-oncology lacks personalized care algorithms for individuals with DLGGs based on a robust foundation of evidence. In this manuscript, we (1) discuss the logistical and philosophical challenges hindering the development of surgical RCTs for DLGGs, (2) highlight the potential impact of well-designed international prospective observational registries, (3) discuss ways in which cutting-edge computational techniques can be harnessed to generate maximal insight from high volumes of multi-faceted data, and (4) outline a comprehensive plan of action that will enable a multi-disciplinary approach to future DLGG management, integrating advances in clinical medicine, basic molecular research and large-scale data mining.

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“…Due to the relative low aggressiveness of LGG, a more conservative management ("waitand-watch") approach (Whittle, 2004) is sometimes adopted. Segmentation is important in this scenario also to monitor temporal morphological and volumetric alterations of the tumors during observation, prior to elective tumor resection (Larsen et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Stratification by Tumor Grade Groups in a Holistic Evaluation of Machine Learning for Brain Tumor Segmentation

et al. 2021

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Accurate and consistent segmentation plays an important role in the diagnosis, treatment planning, and monitoring of both High Grade Glioma (HGG), including Glioblastoma Multiforme (GBM), and Low Grade Glioma (LGG). Accuracy of segmentation can be affected by the imaging presentation of glioma, which greatly varies between the two tumor grade groups. In recent years, researchers have used Machine Learning (ML) to segment tumor rapidly and consistently, as compared to manual segmentation. However, existing ML validation relies heavily on computing summary statistics and rarely tests the generalizability of an algorithm on clinically heterogeneous data. In this work, our goal is to investigate how to holistically evaluate the performance of ML algorithms on a brain tumor segmentation task. We address the need for rigorous evaluation of ML algorithms and present four axes of model evaluation—diagnostic performance, model confidence, robustness, and data quality. We perform a comprehensive evaluation of a glioma segmentation ML algorithm by stratifying data by specific tumor grade groups (GBM and LGG) and evaluate these algorithms on each of the four axes. The main takeaways of our work are—(1) ML algorithms need to be evaluated on out-of-distribution data to assess generalizability, reflective of tumor heterogeneity. (2) Segmentation metrics alone are limited to evaluate the errors made by ML algorithms and their describe their consequences. (3) Adoption of tools in other domains such as robustness (adversarial attacks) and model uncertainty (prediction intervals) lead to a more comprehensive performance evaluation. Such a holistic evaluation framework could shed light on an algorithm's clinical utility and help it evolve into a more clinically valuable tool.

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‘Low grade glioma’: an update for radiologists

Cited by 23 publications

References 86 publications

Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis

Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis

Management of Diffuse Low-Grade Glioma: The Renaissance of Robust Evidence

Stratification by Tumor Grade Groups in a Holistic Evaluation of Machine Learning for Brain Tumor Segmentation

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