Advanced MRI Protocols to Discriminate Glioma From Treatment Effects: State of the Art and Future Directions

Malik, Dania G.; Rath, Tanya J.; Acevedo, Javier C. Urcuyo; Canoll, Peter; Swanson, Kristin R.; Boxerman, Jerrold L.; Quarles, C. Chad; Schmainda, Kathleen M.; Burns, Terry C.; Hu, Leland

doi:10.3389/fradi.2022.809373

Cited by 7 publications

(5 citation statements)

References 121 publications

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“…However, RN and TP can mimic each other and often coexist. Therefore, advanced MRI techniques are required to correctly interpret MRI findings [45,46]. DWI and DTI have been assessed to differentiate TP and/or residual tumor from RN; ADC values were noted to be higher in RN than in TP and like in PsP, some studies have demonstrated lower FA values in RN than in TP [34,47].…”

Section: Imaging: Conventional and Advanced Mri Sequencesmentioning

confidence: 99%

Advanced Magnetic Resonance Imaging in the Evaluation of Treated Glioblastoma: A Pictorial Essay

Martucci,

Russo,

Giordano

et al. 2023

Cancers

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MRI plays a key role in the evaluation of post-treatment changes, both in the immediate post-operative period and during follow-up. There are many different treatment’s lines and many different neuroradiological findings according to the treatment chosen and the clinical timepoint at which MRI is performed. Structural MRI is often insufficient to correctly interpret and define treatment-related changes. For that, advanced MRI modalities, including perfusion and permeability imaging, diffusion tensor imaging, and magnetic resonance spectroscopy, are increasingly utilized in clinical practice to characterize treatment effects more comprehensively. This article aims to provide an overview of the role of advanced MRI modalities in the evaluation of treated glioblastomas. For a didactic purpose, we choose to divide the treatment history in three main timepoints: post-surgery, during Stupp (first-line treatment) and at recurrence (second-line treatment). For each, a brief introduction, a temporal subdivision (when useful) or a specific drug-related paragraph were provided. Finally, the current trends and application of radiomics and artificial intelligence (AI) in the evaluation of treated GB have been outlined.

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Section: Imaging: Conventional and Advanced Mri Sequencesmentioning

confidence: 99%

Advanced Magnetic Resonance Imaging in the Evaluation of Treated Glioblastoma: A Pictorial Essay

Martucci,

Russo,

Giordano

et al. 2023

Cancers

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“…Early identification of recurrence following surgery optimizes the prospects for salvage interventions to preserve survival and quality of life 10,11 . Despite frequent and long-term MRI surveillance, recurrence can be difficult to assess by diagnosticians given the need to synthesize longitudinal changes over many time points, and, resultingly, recurrence can first present symptomatically 12 .…”

Section: Introductionmentioning

confidence: 99%

Longitudinal risk prediction for pediatric glioma with temporal deep learning

Tak,

Garomsa,

Zapaishchykova

et al. 2024

Preprint

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Pediatric glioma recurrence can cause morbidity and mortality; however, recurrence pattern and severity are heterogeneous and challenging to predict with established clinical and genomic markers. Resultingly, almost all children undergo frequent, long-term, magnetic resonance (MR) brain surveillance regardless of individual recurrence risk. Deep learning analysis of longitudinal MR may be an effective approach for improving individualized recurrence prediction in gliomas and other cancers but has thus far been infeasible with current frameworks. Here, we propose a self-supervised, deep learning approach to longitudinal medical imaging analysis, temporal learning, that models the spatiotemporal information from a patient’s current and prior brain MRs to predict future recurrence. We apply temporal learning to pediatric glioma surveillance imaging for 715 patients (3,994 scans) from four distinct clinical settings. We find that longitudinal imaging analysis with temporal learning improves recurrence prediction performance by up to 41% compared to traditional approaches, with improvements in performance in both low- and high-grade glioma. We find that recurrence prediction accuracy increases incrementally with the number of historical scans available per patient. Temporal deep learning may enable point-of-care decision-support for pediatric brain tumors and be adaptable more broadly to patients with other cancers and chronic diseases undergoing surveillance imaging.

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“…GBMs represent the most lethal central nervous system (CNS) tumors in adults [ 7 , 8 ]. However, characterizing their vascularity presents significant challenges due to their heterogeneity at both intratumoral [ 4 , 5 , 6 , 9 , 10 , 11 , 12 ] and interpatient [ 10 , 13 , 14 ] levels. Moreover, this vascular heterogeneity in glioblastomas is further accentuated by tumor dynamics, resulting in significant variations in vascularity between newly diagnosed and recurrent glioblastomas [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…It also aids in determining tumor grade, distinguishing between tumor recurrence and radiation necrosis, and assessing treatment responses [ 18 , 19 ]. Multiple studies consistently show a strong connection between measurements of relative cerebral blood volume (rCBV) and microvascular structures in various types of glioma tumors [ 9 , 11 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Unlocking Bevacizumab’s Potential: rCBVmax as a Predictive Biomarker for Enhanced Survival in Glioblastoma IDH-Wildtype Patients

Álvarez-Torres,

Balaña,

Fuster-García

et al. 2023

Cancers

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Background: Aberrant vascular architecture and angiogenesis are hallmarks of glioblastoma IDH-wildtype, suggesting that these tumors are suitable for antiangiogenic therapy. Bevacizumab was FDA-approved in 2009 following promising results in two clinical trials. However, its use for recurrent glioblastomas remains a subject of debate, as it does not universally improve patient survival. Purposes: In this study, we aimed to analyze the influence of tumor vascularity on the benefit provided by BVZ and propose preoperative rCBVmax at the high angiogenic tumor habitat as a predictive biomarker to select patients who can benefit the most. Methods: Clinical and MRI data from 106 patients with glioblastoma IDH-wildtype have been analyzed. Thirty-nine of them received BVZ, and the remaining sixty-seven did not receive a second-line treatment. The ONCOhabitats method was used to automatically calculate rCBV. Results: We found a median survival from progression of 305 days longer for patients with moderate vascular tumors who received BVZ than those who did not receive any second-line treatment. This contrasts with patients with high-vascular tumors who only presented a median survival of 173 days longer when receiving BVZ. Furthermore, better responses to BVZ were found for the moderate-vascular group with a higher proportion of patients alive at 6, 12, 18, and 24 months after progression. Conclusions: We propose rCBVmax as a potential biomarker to select patients who can benefit more from BVZ after tumor progression. In addition, we propose a threshold of 7.5 to stratify patients into moderate- and high-vascular groups to select the optimal second-line treatment.

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Advanced MRI Protocols to Discriminate Glioma From Treatment Effects: State of the Art and Future Directions

Cited by 7 publications

References 121 publications

Advanced Magnetic Resonance Imaging in the Evaluation of Treated Glioblastoma: A Pictorial Essay

Advanced Magnetic Resonance Imaging in the Evaluation of Treated Glioblastoma: A Pictorial Essay

Longitudinal risk prediction for pediatric glioma with temporal deep learning

Unlocking Bevacizumab’s Potential: rCBVmax as a Predictive Biomarker for Enhanced Survival in Glioblastoma IDH-Wildtype Patients

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