A comprehensive evaluation and impact of normalization of generalized tracer kinetic model parameters to characterize blood-brain-barrier permeability in normal-appearing and tumor tissue regions of patients with glioma

S, Dinil Sasi; Gupta, Rakesh K.; Patir, Rana; Ahlawat, Suneeta; Vaishya, Sandeep; Singh, Anup

doi:10.1016/j.mri.2021.07.005

Cited by 7 publications

(2 citation statements)

References 28 publications

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“…A key limitation of the ETM, however, is that the derived parameter K trans is a hybrid parameter reflecting both capillary plasma flow (F p ) and the capillary permeability-surface area product (PS) [ 22 ]. The ETM is also unable to accurately measure low-level permeability within normal-appearing brain tissue, which is of increasing importance in neuro-oncology and may help predict both prognosis and treatment response [ 23 , 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Model High Spatial Resolution Method for Analysis of DCE MRI Data: Insights from Vestibular Schwannoma Responses to Antiangiogenic Therapy in Type II Neurofibromatosis

Li,

Lewis,

Zhu

et al. 2023

Pharmaceuticals

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This study aimed to develop and evaluate a new DCE-MRI processing technique that combines LEGATOS, a dual-temporal resolution DCE-MRI technique, with multi-kinetic models. This technique enables high spatial resolution interrogation of flow and permeability effects, which is currently challenging to achieve. Twelve patients with neurofibromatosis type II-related vestibular schwannoma (20 tumours) undergoing bevacizumab therapy were imaged at 1.5 T both before and at 90 days following treatment. Using the new technique, whole-brain, high spatial resolution images of the contrast transfer coefficient (Ktrans), vascular fraction (vp), extravascular extracellular fraction (ve), capillary plasma flow (Fp), and the capillary permeability-surface area product (PS) could be obtained, and their predictive value was examined. Of the five microvascular parameters derived using the new method, baseline PS exhibited the strongest correlation with the baseline tumour volume (p = 0.03). Baseline ve showed the strongest correlation with the change in tumour volume, particularly the percentage tumour volume change at 90 days after treatment (p < 0.001), and PS demonstrated a larger reduction at 90 days after treatment (p = 0.0001) when compared to Ktrans or Fp alone. Both the capillary permeability-surface area product (PS) and the extravascular extracellular fraction (ve) significantly differentiated the ‘responder’ and ‘non-responder’ tumour groups at 90 days (p < 0.05 and p < 0.001, respectively). These results highlight that this novel DCE-MRI analysis approach can be used to evaluate tumour microvascular changes during treatment and the need for future larger clinical studies investigating its role in predicting antiangiogenic therapy response.

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Section: Introductionmentioning

confidence: 99%

A Novel Multi-Model High Spatial Resolution Method for Analysis of DCE MRI Data: Insights from Vestibular Schwannoma Responses to Antiangiogenic Therapy in Type II Neurofibromatosis

Li,

Lewis,

Zhu

et al. 2023

Pharmaceuticals

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“…They are easily affected by certain objective factors, such as the doctor's experience, mental state, and the quality of CT imaging. This is not only time-consuming and labor-intensive but also causes risk of misdiagnosis and missed diagnosis [ 15 – 17 ].…”

Section: Introductionmentioning

confidence: 99%

Exploration of CT Images Based on the BN‐U‐net‐W Network Segmentation Algorithm in Glioma Surgery

Yuan

et al. 2022

Contrast Media & Molecular Imaging

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This study aimed to explore the application value of computed tomography (CT) imaging features based on the deep learning batch normalization (batch normalization, BN) U-net-W network image segmentation algorithm in evaluating and diagnosing glioma surgery. 72 patients with glioma who were admitted to hospital were selected as the research subjects. They were divided into a low-grade group (grades I-II, N = 27 cases) and high-grade group (grades III-IV, N = 45 cases) according to postoperative pathological examination results. The CT perfusion imaging (CTPI) images of patients were processed by using the deep learning-based BN-U-net-W network image segmentation algorithm. The application value of the algorithm was comprehensively evaluated by comparing the average Dice coefficient, average recall rate, and average precision of the BN-U-net-W network image segmentation algorithm with the U-net and BN-U-net network algorithms. The results showed that the Dice coefficient, recall, and precision of the BN-U-net-W network were 86.31%, 88.43%, and 87.63% respectively, which were higher than those of the U-net and BN-U-net networks, and the differences were statistically significant ( P < 0.05 ). Cerebral blood flow (CBF), cerebral blood volume (CBV), and capillary permeability (PMB) in the glioma area were 56.85 mL/(min·100 g), 18.03 mL/(min·100 g), and 8.57 mL/100 g, respectively, which were significantly higher than those of normal brain tissue, showing statistically significant differences ( P < 0.05 ). The mean transit time (MTT) difference between the two was not statistically significant ( P > 0.05 ). The receiver operating characteristic (ROC) curves of CBF, CBV, and PMB in CTPI parameters of glioma had area under the curve (AUC) of 0.685, 0.724, and 0.921, respectively. PMB parameters were significantly higher than those of CBF and CVB, and the differences were statistically obvious ( P < 0.05 ). It showed that the BN-U-net-W network model had a better image segmentation effect, and CBF, CBV, and PMB showed better sensitivity in diagnosing glioma tissue and normal brain tissue and high-grade and low-grade gliomas, among which PBM showed the highest predictability.

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Evaluating the Role of Leakage Correction of Hemodynamic Parameters derived from Dynamic Contrast Enhanced MRI for Glioma Grading

Sankaralayam,

Ramaniharan,

Gupta

et al. 2024

Magnetic Resonance Imaging

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To the Editor:Quantitative dynamic-contrast-enhanced (DCE)-MRI technique is being used for brain tumor characterization. 1,2 Traditionally, kinetic parameters derived from DCE-MRI such as the volumetransfer-constant (K trans ) and volume fraction of extracellular-extravascular-space are used to quantify blood-brain-barrier breakdown in brain tumors. However, recent developments have expanded our understanding by enabling the simultaneous measurement of hemodynamic parameters cerebral-blood-flow (CBF) and cerebralblood-volume (CBV), alongside the conventional kinetic parameters obtained from DCE-MRI data. 3,4 Early attempts by Singh et al and Li et al utilized first-pass analysis as a model-free approach to quantify CBV and CBF from DCE-MRI data. 3,5 Nonetheless, these initial efforts faced challenges, as CBV and CBF exhibited leakage profiles attributed to contributions from the extracellular-extravascular leakage space. To address this limitation, Sourbron et al introduced a two-compartmental-tissue-uptake-model (CTUM) for the simultaneous and absolute quantification of leakage-corrected CBV and CBF along with K trans . 4 Despite these advancements, the clinical importance of leakage-corrected CBV and CBF in characterizing brain tumors remains uncertain. This study aims to evaluate the influence of leakage correction on hemodynamic parameters in glioma grading.

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A comprehensive evaluation and impact of normalization of generalized tracer kinetic model parameters to characterize blood-brain-barrier permeability in normal-appearing and tumor tissue regions of patients with glioma

Cited by 7 publications

References 28 publications

A Novel Multi-Model High Spatial Resolution Method for Analysis of DCE MRI Data: Insights from Vestibular Schwannoma Responses to Antiangiogenic Therapy in Type II Neurofibromatosis

A Novel Multi-Model High Spatial Resolution Method for Analysis of DCE MRI Data: Insights from Vestibular Schwannoma Responses to Antiangiogenic Therapy in Type II Neurofibromatosis

Exploration of CT Images Based on the BN‐U‐net‐W Network Segmentation Algorithm in Glioma Surgery

Evaluating the Role of Leakage Correction of Hemodynamic Parameters derived from Dynamic Contrast Enhanced MRI for Glioma Grading

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