Visualizing Glioma Infiltration by the Combination of Multimodality Imaging and Artificial Intelligence, a Systematic Review of the Literature

d’Este, Sabrina Honoré; Nielsen, Michael Bachmann; Hansen, Adam E.

doi:10.3390/diagnostics11040592

Cited by 12 publications

(13 citation statements)

References 52 publications

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“…Closing this topic, we highlight a 2021 systematic review on the integration of multimodality imaging and artificial intelligence to improve the visualization of tumor cell infiltration in glioma [54], which found only two articles that included DLGG (along with HGG) in their cohorts [47,53], depicting the gap between low-grade and high-grade research.…”

Section: Flair Mri Cannot Reflect the Ptz: How Can Dlgg Delineation B...mentioning

confidence: 99%

The Concept of «Peritumoral Zone» in Diffuse Low-Grade Gliomas: Oncological and Functional Implications for a Connectome-Guided Therapeutic Attitude

2022

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Diffuse low-grade gliomas (DLGGs) are heterogeneous and poorly circumscribed neoplasms with isolated tumor cells that extend beyond the margins of the lesion depicted on MRI. Efforts to demarcate the glioma core from the surrounding healthy brain led us to define an intermediate region, the so-called peritumoral zone (PTZ). Although most studies about PTZ have been conducted on high-grade gliomas, the purpose here is to review the cellular, metabolic, and radiological characteristics of PTZ in the specific context of DLGG. A better delineation of PTZ, in which glioma cells and neural tissue strongly interact, may open new therapeutic avenues to optimize both functional and oncological results. First, a connectome-based “supratotal” surgical resection (i.e., with the removal of PTZ in addition to the tumor core) resulted in prolonged survival by limiting the risk of malignant transformation, while improving the quality of life, thanks to a better control of seizures. Second, the timing and order of (neo)adjuvant medical treatments can be modulated according to the pattern of peritumoral infiltration. Third, the development of new drugs specifically targeting the PTZ could be considered from an oncological (such as immunotherapy) and epileptological perspective. Further multimodal investigations of PTZ are needed to maximize long-term outcomes in DLGG patients.

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Section: Flair Mri Cannot Reflect the Ptz: How Can Dlgg Delineation B...mentioning

confidence: 99%

The Concept of «Peritumoral Zone» in Diffuse Low-Grade Gliomas: Oncological and Functional Implications for a Connectome-Guided Therapeutic Attitude

2022

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“…A recent systematic review concluded that the integration of machine learning with multiparametric data was promising for visualization of diffusely infiltrating tumor cells before and after treatment. The review also concluded that because study cohorts are small, further studies are required to determine optimal methodology, and there is a need for larger cohorts to improve model performance ( 90 ). An advantage of machine learning is that wide data can be handled relatively easily ( 91 ) which might allow the wide spectrum of advanced imaging signatures to be captured together and thereby improve performance accuracy.…”

Section: Resultsmentioning

confidence: 99%

High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 2: Spectroscopy, Chemical Exchange Saturation, Multiparametric Imaging, and Radiomics

et al. 2022

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ObjectiveTo summarize evidence for use of advanced MRI techniques as monitoring biomarkers in the clinic, and to highlight the latest bench-to-bedside developments.MethodsThe current evidence regarding the potential for monitoring biomarkers was reviewed and individual modalities of metabolism and/or chemical composition imaging discussed. Perfusion, permeability, and microstructure imaging were similarly analyzed in Part 1 of this two-part review article and are valuable reading as background to this article. We appraise the clinic readiness of all the individual modalities and consider methodologies involving machine learning (radiomics) and the combination of MRI approaches (multiparametric imaging).ResultsThe biochemical composition of high-grade gliomas is markedly different from healthy brain tissue. Magnetic resonance spectroscopy allows the simultaneous acquisition of an array of metabolic alterations, with choline-based ratios appearing to be consistently discriminatory in treatment response assessment, although challenges remain despite this being a mature technique. Promising directions relate to ultra-high field strengths, 2-hydroxyglutarate analysis, and the use of non-proton nuclei. Labile protons on endogenous proteins can be selectively targeted with chemical exchange saturation transfer to give high resolution images. The body of evidence for clinical application of amide proton transfer imaging has been building for a decade, but more evidence is required to confirm chemical exchange saturation transfer use as a monitoring biomarker. Multiparametric methodologies, including the incorporation of nuclear medicine techniques, combine probes measuring different tumor properties. Although potentially synergistic, the limitations of each individual modality also can be compounded, particularly in the absence of standardization. Machine learning requires large datasets with high-quality annotation; there is currently low-level evidence for monitoring biomarker clinical application.ConclusionAdvanced MRI techniques show huge promise in treatment response assessment. The clinical readiness analysis highlights that most monitoring biomarkers require standardized international consensus guidelines, with more facilitation regarding technique implementation and reporting in the clinic.

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“…Hyperspectral imaging (HSI) has also been applied in conjunction with deep learning to identify the boundaries of glioblastomas 21 , but entails a much lower resolution (128.7 m pixel size vs. 0.2 m for THG). The same principle applies to MR imaging 18 , aside from the impractical setup, high costs and time-intensive workflow. Hollon et al 22 combined stimulated Raman histology (SRH) with deep learning to demonstrate the potential of label-free microscopy in conjunction with AI.…”

Section: Introductionmentioning

confidence: 95%

“…Computer-assistance of the neurosurgeon during a surgical procedure should result in instant, meaningful histological feedback of the tissue state. Artificial intelligence (AI) is capable of delivering state-of-the-art computer assisted diagnosis 16 – 18 . Deep learning, a subfield of AI, allows for complex pattern recognition by means of an iterative, supervised optimization process.…”

Section: Introductionmentioning

confidence: 99%

Fast intraoperative histology-based diagnosis of gliomas with third harmonic generation microscopy and deep learning

Blokker

Hamer

Wesseling

et al. 2022

Sci Rep

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Management of gliomas requires an invasive treatment strategy, including extensive surgical resection. The objective of the neurosurgeon is to maximize tumor removal while preserving healthy brain tissue. However, the lack of a clear tumor boundary hampers the neurosurgeon’s ability to accurately detect and resect infiltrating tumor tissue. Nonlinear multiphoton microscopy, in particular higher harmonic generation, enables label-free imaging of excised brain tissue, revealing histological hallmarks within seconds. Here, we demonstrate a real-time deep learning-based pipeline for automated glioma image analysis, matching video-rate image acquisition. We used a custom noise detection scheme, and a fully-convolutional classification network, to achieve on average 79% binary accuracy, 0.77 AUC and 0.83 mean average precision compared to the consensus of three pathologists, on a preliminary dataset. We conclude that the combination of real-time imaging and image analysis shows great potential for intraoperative assessment of brain tissue during tumor surgery.

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Visualizing Glioma Infiltration by the Combination of Multimodality Imaging and Artificial Intelligence, a Systematic Review of the Literature

Cited by 12 publications

References 52 publications

The Concept of «Peritumoral Zone» in Diffuse Low-Grade Gliomas: Oncological and Functional Implications for a Connectome-Guided Therapeutic Attitude

The Concept of «Peritumoral Zone» in Diffuse Low-Grade Gliomas: Oncological and Functional Implications for a Connectome-Guided Therapeutic Attitude

High-Grade Glioma Treatment Response Monitoring Biomarkers: A Position Statement on the Evidence Supporting the Use of Advanced MRI Techniques in the Clinic, and the Latest Bench-to-Bedside Developments. Part 2: Spectroscopy, Chemical Exchange Saturation, Multiparametric Imaging, and Radiomics

Fast intraoperative histology-based diagnosis of gliomas with third harmonic generation microscopy and deep learning

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