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

Abstract

Purpose Proton therapy could benefit from noninvasively gaining tumor microstructure information, at both planning and monitoring stages. The anatomical location of brain tumors, such as meningiomas, often hinders the recovery of such information from histopathology, and conventional noninvasive imaging biomarkers, like the apparent diffusion coefficient (ADC) from diffusion‐weighted MRI (DW‐MRI), are nonspecific. 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 tumor grading and tumor response assessment. Methods DW‐MRIs from patients affected by meningioma and enrolled in proton therapy were collected before (n = 35) and 3 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 modeled 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 tumor 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 nonparametric 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 tumor microstructure implying important advantages for patient‐specific proton therapy workflows.

Massively Multidimensional Diffusion-Relaxation Correlation MRI

“…In the rodent brain, the granule and pyramidal cell layers are mostly tightly packed cells with a regular geometry [62] in contrast with other brain areas (e.g., cortex, thalamus) where the cells are more scattered and share space with an array of neuropil in multiple orientations. Hence, the observation of elevated E[D iso ] in the granule cell layer results from the combined effects of the cell density, nuclear volume fractions, geometry, and molecular exchange [5,[95][96][97][98].…”

Diffusion MRI signal cumulants and hepatocyte microstructure at fixed diffusion time: Insights from simulations, 9.4T imaging, and histology

“…Moreover, may be difficult to measure accurately on noisy data (e.g., can be unstable when is low, being computed by dividing the second cumulant by ) 74 . In the future, more advanced signal‐to‐microstructure mappings will be explored (e.g., machine learning 75–77 ).…”

Hadron Therapy Achievements and Challenges: The CNAO Experience

“…Lastly, as part of a collaboration with the Center of Medical Image Computing (CMIC) of the University College London (UCL), a microstructure model has been applied starting from diffusion-weighted imaging techniques. The model is capable of deriving quantitative indices on a microscopic scale (for instance, radius of the cells), both for non-invasive histological characterization of meningiomas and for the evaluation of structural changes induced by proton therapy [63].…”