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Improving the characterization of meningioma microstructure in proton therapy from conventional apparent diffusion coefficient measurements using Monte Carlo simulations of diffusion MRI

Buizza, G; Paganelli, C; Ballati, F; Sacco, S; Preda, L; Iannalfi, A; Alexander, DC; ... Palombo, M; + view all (2020) Improving the characterization of meningioma microstructure in proton therapy from conventional apparent diffusion coefficient measurements using Monte Carlo simulations of diffusion MRI. Medical Physics 10.1002/mp.14689. (In press).

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Abstract

Purpose: Proton therapy could benefit from non‐invasively gaining tumour microstructure information, at both planning and monitoring stages. The anatomical location of brain tumours, such as meningiomas, often hinders the recovery of such information from histopathology, and conventional non‐invasive imaging biomarkers, like the apparent diffusion coefficient (ADC) from diffusion‐weighted MRI (DW‐MRI), are non‐specific. The aim of this study was to retrieve discriminative microstructural markers from conventional ADC for meningiomas treated with proton therapy. These markers were employed for tumour grading and tumour response assessment. Methods: DW‐MRI from patients affected by meningioma and enrolled in proton therapy were collected before (n=35) and three months after (n=25) treatment. For the latter group, the risk of an adverse outcome was inferred by their clinical history. Using Monte Carlo methods, DW‐MRI signals were simulated from packings of synthetic cells built with well‐defined geometrical and diffusion properties. Patients’ ADC was modelled as a weighted sum of selected simulated signals. The weights that best described a patient’s ADC were determined through an optimization procedure and used to estimate a set of markers of tumour microstructure: diffusion coefficient (D), volume fraction (vf) and radius (R). Apparent cellularity (ρapp) was estimated from vf and R for an easier clinical interpretability. Differences between meningothelial and atypical subtypes, and low‐ and high‐grade meningiomas were assessed with non‐parametric statistical tests, whereas sensitivity and specificity with ROC analyses. Similar analyses were performed for patients showing low‐ or high‐risk of an adverse outcome to preliminary evaluate response to treatment. Results: Significant (p<0.05) differences in median ADC, D, vf, R and ρapp values were found when comparing meningiomas’ subtypes and grades. ROC analyses showed that estimated microstructural parameters reached higher specificity than ADC for subtyping (0.93 for D and vf vs. 0.80 for ADC) and grading (0.75 for R vs. 0.67 for ADC). High‐ and low‐risk patients showed significant differences in ADC and microstructural parameters. The skewness of ρapp was the parameter with highest AUC (0.90) and sensitivity (0.75). Conclusions: Matching measured with simulated ADC yielded a set of potential imaging markers for meningiomas grading and response monitoring in proton therapy, showing higher specificity than conventional ADC. These markers can provide discriminative information about spatial patterns of tumour microstructure implying important advantages for patient‐specific proton therapy workflows.

Type: Article
Title: Improving the characterization of meningioma microstructure in proton therapy from conventional apparent diffusion coefficient measurements using Monte Carlo simulations of diffusion MRI
Location: United States
DOI: 10.1002/mp.14689
Publisher version: https://doi.org/10.1002/mp.14689
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Diffusion MRI, Particle therapy, Quantitative imaging, Microstructure, Meningioma
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10118814
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