Comprehensive Brain Tumour Characterisation with VERDICT-MRI: Evaluation of Cellular and Vascular Measures Validated by Histology

Castellano, Antonella; Bailo, Michele; Callea, Marcella; Cadioli, Marcello; Bouyagoub, Samira; Palombo, Marco; Pieri, Valentina; Mortini, Pietro; Falini, Andrea; Alexander, Daniel C.; Cercignani, Mara; Panagiotaki, Eleftheria

doi:10.3390/cancers15092490

Cited by 3 publications

(1 citation statement)

References 55 publications

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“…To the best of our knowledge, no data regarding the histopathological comparison of intracellular volume fraction between glioblastomas and brain metastases have been published. A recent imaging study using Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumors (VERDICT) MRI compared two metastatic brain tumors (melanomas) and five glioblastomas, and showed distinctly higher intracellular volume fraction and lower extracellular volume fraction in metastases [ 36 ]. Despite the small sample size and inclusion of only one histological type of metastasis, their results from VERDICT MRI support our speculation.…”

Section: Discussionmentioning

confidence: 99%

Differentiating brain metastasis from glioblastoma by time-dependent diffusion MRI

Kamimura,

Nakano

et al. 2023

Cancer Imaging

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Background This study was designed to investigate the use of time-dependent diffusion magnetic resonance imaging (MRI) parameters in distinguishing between glioblastomas and brain metastases. Methods A retrospective study was conducted involving 65 patients with glioblastomas and 27 patients with metastases using a diffusion-weighted imaging sequence with oscillating gradient spin-echo (OGSE, 50 Hz) and a conventional pulsed gradient spin-echo (PGSE, 0 Hz) sequence. In addition to apparent diffusion coefficient (ADC) maps from two sequences (ADC50Hz and ADC0Hz), we generated maps of the ADC change (cADC): ADC50Hz − ADC0Hz and the relative ADC change (rcADC): (ADC50Hz − ADC0Hz)/ ADC0Hz × 100 (%). Results The mean and the fifth and 95th percentile values of each parameter in enhancing and peritumoral regions were compared between glioblastomas and metastases. The area under the receiver operating characteristic curve (AUC) values of the best discriminating indices were compared. In enhancing regions, none of the indices of ADC0Hz and ADC50Hz showed significant differences between metastases and glioblastomas. The mean cADC and rcADC values of metastases were significantly higher than those of glioblastomas (0.24 ± 0.12 × 10−3mm2/s vs. 0.14 ± 0.03 × 10−3mm2/s and 23.3 ± 9.4% vs. 14.0 ± 4.7%; all p < 0.01). In peritumoral regions, no significant difference in all ADC indices was observed between metastases and glioblastomas. The AUC values for the mean cADC (0.877) and rcADC (0.819) values in enhancing regions were significantly higher than those for ADC0Hz5th (0.595; all p < 0.001). Conclusions The time-dependent diffusion MRI parameters may be useful for differentiating brain metastases from glioblastomas.

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Section: Discussionmentioning

confidence: 99%

Differentiating brain metastasis from glioblastoma by time-dependent diffusion MRI

Kamimura,

Nakano

et al. 2023

Cancer Imaging

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Assessing tumor microstructure with time‐dependent diffusion imaging: Considerations and feasibility on clinical MRI and MRI‐Linac

Jokivuolle,

Mahmood,

Madsen

et al. 2024

Medical Physics

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BackgroundQuantitative imaging biomarkers (QIBs) can characterize tumor heterogeneity and provide information for biological guidance in radiotherapy (RT). Time‐dependent diffusion MRI (TDD‐MRI) derived parameters are promising QIBs, as they describe tissue microstructure with more specificity than traditional diffusion‐weighted MRI (DW‐MRI). Specifically, TDD‐MRI can provide information about both restricted diffusion and diffusional exchange, which are the two time‐dependent effects affecting diffusion in tissue, and relevant in tumors. However, exhaustive modeling of both effects can require long acquisitions and complex model fitting. Furthermore, several introduced TDD‐MRI measurements can require high gradient strengths and/or complex gradient waveforms that are possibly not available in RT settings.PurposeIn this study, we investigated the feasibility of a simple analysis framework for the detection of restricted diffusion and diffusional exchange effects in the TDD‐MRI signal. To promote the clinical applicability, we use standard gradient waveforms on a conventional 1.5 T MRI system with moderate gradient strength (Gmax = 45 mT/m), and on a hybrid 1.5 T MRI‐Linac system with low gradient strength (Gmax = 15 mT/m).MethodsRestricted diffusion and diffusional exchange were simulated in geometries mimicking tumor microstructure to investigate the DW‐MRI signal behavior and to determine optimal experimental parameters. TDD‐MRI was implemented using pulsed field gradient spin echo with the optimized parameters on a conventional MRI system and a MRI‐Linac. Experiments in green asparagus and 10 patients with brain lesions were performed to evaluate the time‐dependent diffusion (TDD) contrast in the source DW‐images.ResultsSimulations demonstrated how the TDD contrast was able to differentiate only dominating diffusional exchange in smaller cells from dominating restricted diffusion in larger cells. The maximal TDD contrast in simulations with typical cancer cell sizes and in asparagus measurements exceeded 5% on the conventional MRI but remained below 5% on the MRI‐Linac. In particular, the simulated TDD contrast in typical cancer cell sizes (r = 5–10 µm) remained below or around 2% with the MRI‐Linac gradient strength. In patients measured with the conventional MRI, we found sub‐regions reflecting either dominating restricted diffusion or dominating diffusional exchange in and around brain lesions compared to the noisy appearing white matter.ConclusionsOn the conventional MRI system, the TDD contrast maps showed consistent tumor sub‐regions indicating different dominating TDD effects, potentially providing information on the spatial tumor heterogeneity. On the MRI‐Linac, the available TDD contrast measured in asparagus showed the same trends as with the conventional MRI but remained close to typical measurement noise levels when simulated in common cancer cell sizes. On conventional MRI systems with moderate gradient strengths, the TDD contrast could potentially be used as a tool to identify which time‐dependent effects to include when choosing a biophysical model for more specific tumor characterization.

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ssVERDICT: Self‐supervised VERDICT‐MRI for enhanced prostate tumor characterization

Sen,

Singh,

Pye

et al. 2024

Magnetic Resonance in Med

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PurposeDemonstrating and assessing self‐supervised machine‐learning fitting of the VERDICT (vascular, extracellular and restricted diffusion for cytometry in tumors) model for prostate cancer.MethodsWe derive a self‐supervised neural network for fitting VERDICT (ssVERDICT) that estimates parameter maps without training data. We compare the performance of ssVERDICT to two established baseline methods for fitting diffusion MRI models: conventional nonlinear least squares and supervised deep learning. We do this quantitatively on simulated data by comparing the Pearson's correlation coefficient, mean‐squared error, bias, and variance with respect to the simulated ground truth. We also calculate in vivo parameter maps on a cohort of 20 prostate cancer patients and compare the methods' performance in discriminating benign from cancerous tissue via Wilcoxon's signed‐rank test.ResultsIn simulations, ssVERDICT outperforms the baseline methods (nonlinear least squares and supervised deep learning) in estimating all the parameters from the VERDICT prostate model in terms of Pearson's correlation coefficient, bias, and mean‐squared error. In vivo, ssVERDICT shows stronger lesion conspicuity across all parameter maps, and improves discrimination between benign and cancerous tissue over the baseline methods.ConclusionssVERDICT significantly outperforms state‐of‐the‐art methods for VERDICT model fitting and shows, for the first time, fitting of a detailed multicompartment biophysical diffusion MRI model with machine learning without the requirement of explicit training labels.

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Comprehensive Brain Tumour Characterisation with VERDICT-MRI: Evaluation of Cellular and Vascular Measures Validated by Histology

Cited by 3 publications

References 55 publications

Differentiating brain metastasis from glioblastoma by time-dependent diffusion MRI

Differentiating brain metastasis from glioblastoma by time-dependent diffusion MRI

Assessing tumor microstructure with time‐dependent diffusion imaging: Considerations and feasibility on clinical MRI and MRI‐Linac

ssVERDICT: Self‐supervised VERDICT‐MRI for enhanced prostate tumor characterization

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