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Tytuł pozycji:

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

Tytuł :
Improving the characterization of meningioma microstructure in proton therapy from conventional apparent diffusion coefficient measurements using Monte Carlo simulations of diffusion MRI.
Autorzy :
Buizza G; Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, 20133, Italy.
Paganelli C; Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, 20133, Italy.
Ballati F; Diagnostic Radiology Residency School, University of Pavia, Pavia, 27100, Italy.
Sacco S; Diagnostic Radiology Residency School, University of Pavia, Pavia, 27100, Italy.
Preda L; Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, 27100, Italy.
Iannalfi A; Clinical Department, National Center of Oncological Hadrontherapy (CNAO), Pavia, 27100, Italy.
Alexander DC; Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London (UCL), London, WC1V6LJ, UK.
Baroni G; Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, 20133, Italy.; Bioengineering Unit, National Center of Oncological Hadrontherapy (CNAO), Pavia, 27100, Italy.
Palombo M; Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London (UCL), London, WC1V6LJ, UK.
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Źródło :
Medical physics [Med Phys] 2021 Mar; Vol. 48 (3), pp. 1250-1261. Date of Electronic Publication: 2021 Feb 05.
Typ publikacji :
Journal Article
Język :
Imprint Name(s) :
Publication: 2017- : Hoboken, NJ : John Wiley and Sons, Inc.
Original Publication: Lancaster, Pa., Published for the American Assn. of Physicists in Medicine by the American Institute of Physics.
MeSH Terms :
Meningeal Neoplasms*/diagnostic imaging
Meningeal Neoplasms*/radiotherapy
Meningioma*/diagnostic imaging
Proton Therapy*
Diffusion Magnetic Resonance Imaging ; Humans ; Monte Carlo Method ; Neoplasm Grading
References :
Durante M, Orecchia R, Loeffler JS. Charged-particle therapy in cancer: clinical uses and future perspectives. Nat Rev Clin Oncol. 2017;14:483-495.
Jaffray DA. Image-guided radiotherapy: from current concept to future perspectives. Nat Rev Clin Oncol. 2012;9:688-699.
Kurz C, Buizza G, Landry G, et al. Medical physics challenges in clinical MR-guided radiotherapy. Radiat Oncol. 2020;15:93.
Louis DN, Perry A, Reifenberger G, et al. The 2016 world health organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. 2016;131:803-820.
Voß KM, Spille DC, Sauerland C, et al. The Simpson grading in meningioma surgery: does the tumor location influence the prognostic value? J Neurooncol. 2017;133:641-651.
Weber DC, Ares C, Villa S, et al. Adjuvant postoperative high-dose radiotherapy for atypical and malignant meningioma: a phase-II parallel non-randomized and observation study (EORTC 22042-26042). Radiother Oncol. 2018;128:260-265.
Weber DC, Schneider R, Goitein G, et al. Spot scanning-based proton therapy for intracranial meningioma: long-term results from the Paul scherrer institute. Int J Radiat Oncol. 2012;83:865-871.
Coggins WS, Pham NK, Nguyen AV, et al. A systematic review of ion radiotherapy in maintaining local control regarding atypical and anaplastic meningiomas. World Neurosurg 2019;132:282-291.
Rackwitz T, Debus J. Clinical applications of proton and carbon ion therapy. Semin Oncol 2019;46:226-232.
Barresi V, Caffo M, Tuccari G. Classification of human meningiomas: lights, shadows, and future perspectives. J Neurosci Res. 2016;94:1604-1612.
Harter PN, Braun Y, Plate KH. Classification of meningiomas-advances and controversies. Chinese Clin Oncol. 2017;6:S2.
Surov A, Ginat DT, Lim T, et al. Histogram analysis parameters apparent diffusion coefficient for distinguishing high and low-grade meningiomas: a multicenter study. Transl Oncol 2018;11:1074-1079.
Zhang T, Yu J, Wang Y, Yin D, Fang L. WHO grade I meningioma subtypes: MRI features and pathological analysis. Life Sci. 2018;213:50-56.
Rogers L, Gilbert M, Vogelbaum MA. Intracranial meningiomas of atypical (WHO grade II) histology. J Neurooncol. 2010;99:393-405.
Zampini MA, Buizza G, Paganelli C, et al. Perfusion and diffusion in meningioma tumors: a preliminary multiparametric analysis with dynamic susceptibility contrast and intravoxel incoherent motion MRI. Magn Reson Imaging. 2019;2020:69-78.
Moffat BA, Chenevert TL, Lawrence TS, et al. Functional diffusion map: A noninvasive MRI biomarker for early stratification of clinical brain tumor response. Proc Natl Acad Sci. 2005;102:5524-5529.
Lin L, Bhawana R, Xue Y, et al. Comparative analysis of diffusional kurtosis imaging, diffusion tensor imaging, and diffusion-weighted imaging in grading and assessing cellular proliferation of meningiomas. Am J Neuroradiol. 2018;39:1032-1038.
Szczepankiewicz F, van Westen D, Englund E, et al. The link between diffusion MRI and tumor heterogeneity: mapping cell eccentricity and density by diffusional variance decomposition (DIVIDE). NeuroImage. 2016;142:522-532.
Aslan K, Gunbey HP, Tomak L, Incesu L. The diagnostic value of using combined MR diffusion tensor imaging parameters to differentiate between low- and high-grade meningioma. Br J Radiol. 2018:20180088.
Gihr GA, Horvath-Rizea D, Garnov N, et al. Diffusion profiling via a histogram approach distinguishes low-grade from high-grade meningiomas, can reflect the respective proliferative potential and progesterone receptor status. Mol Imaging Biol. 2018;20:632-640.
Nilsson M, Englund E, Szczepankiewicz F, van Westen D, Sundgren PC. Imaging brain tumour microstructure. NeuroImage. 2018;182:232-250.
Wu W, Miller KL. Image formation in diffusion MRI: a review of recent technical developments. J Magn Reson Imaging. 2017;46:646-662.
Padhani AR, Liu G, Mu-Koh D, et al. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia. 2009;11:102-125.
Novikov DS, Kiselev VG, Jespersen SN. On modeling. Magn Reson Med. 2018;79:3172-3193.
Tang L, Zhou XJ. Diffusion MRI of cancer: from low to high b-values. J Magn Reson Imaging. 2019;49:23-40.
Panagiotaki E, Walker-Samuel S, Siow B, et al. Noninvasive quantification of solid tumor microstructure using VERDICT MRI. Cancer Res. 2014;74:1902-1912.
Le Bihan D. What can we see with IVIM MRI? NeuroImage. 2017;187:56-67.
Reynaud O. Time-dependent diffusion MRI in cancer: tissue modeling and applications. Front Phys. 2017;5:1-16.
Tommasino F, Nahum A, Cella L. Increasing the power of tumour control and normal tissue complication probability modelling in radiotherapy: recent trends and current issues. Transl Cancer Res. 2017;6:S807-S821.
Buizza G, Molinelli S, D’Ippolito E, et al. MRI-based tumour control probability in skull-base chordomas treated with carbon-ion therapy. Radiother Oncol 2019;137:32-37.
van der Heide UA, Houweling AC, Groenendaal G, Beets-Tan RGH, Lambin P. Functional MRI for radiotherapy dose painting. Magn Reson Imaging. 2012;30:1216-1223.
Shackleford J, Shusharina N, Verberg J. Plastimatch 1.6: current capabilities and future directions. Proc MICCAI; 2012.
Pećina-Šlaus N, Kafka A, Lechpammer M. Molecular genetics of intracranial meningiomas with emphasis on canonical Wnt signalling. Cancers (Basel). 2016;8:67.
Roberts TA, Hyare H, Agliardi G, et al. Noninvasive diffusion magnetic resonance imaging of brain tumour cell size for the early detection of therapeutic response. Sci Rep 2020;10:9223.
Donev A, Torquato S, Stillinger F. Neighbor list collision-driven molecular dynamics simulation for nonspherical hard particles.II. Applications to ellipses and ellipsoids. J Comput Phys. 2005;202:765-793.
Blender Online Community. Blender - a 3D modelling and rendering package. 2017;
Hall MG, Alexander DC. Convergence and parameter choice for Monte-Carlo simulations of diffusion MRI. IEEE Trans Med Imaging. 2009;28:1354-1364.
Cook PA, Bai Y, Seunarine KK, Hall MG, Parker GJ, Alexander DC. Camino Open-Source Diffusion-MRI Reconstruction and Processing. 14th Sci Meet Int Soc Magn Reson Med 2006;14:2759.
Panagiotaki E, Chan RW, Dikaios N, et al. Microstructural characterization of normal and malignant human prostate tissue with vascular, extracellular, and restricted diffusion for cytometry in tumours magnetic resonance imaging. Invest Radiol. 2015;50:218-227.
Youden WJ. Index for rating diagnostic tests. Cancer 1950;3:32-35.
Li C, Gore JC, Davatzikos C. Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. Magn Reson Imaging. 2014;32:913-923.
Kessler LG, Barnhart HX, Buckler AJ, et al. The emerging science of quantitative imaging biomarkers terminology and definitions for scientific studies and regulatory submissions. Stat Methods Med Res. 2015;24:9-26.
Yin B, Liu L, Zhang BY, Li YX, Li Y, Geng DY. Correlating apparent diffusion coefficients with histopathologic findings on meningiomas. Eur J Radiol. 2012;81:4050-4056.
Nagar VA, Ye JR, Ng WH, et al. Diffusion-weighted MR imaging: diagnosing atypical or malignant meningiomas and detecting tumor dedifferentiation. Am J Neuroradiol. 2008;29:1147-1152.
Surov A, Meyer HJ, Wienke A. Correlation between apparent diffusion coefficient (ADC) and cellularity is different in several tumors: a meta-analysis. Oncotarget. 2017;8:59492-59499.
McHugh DJ, Lipowska-Bhalla G, Babur M, et al. Diffusion model comparison identifies distinct tumor sub-regions and tracks treatment response. Magn Reson Med. 2020;84:1250-1263.
Madani I, Lomax AJ, Albertini F, Trnková P, Weber DC. Dose-painting intensity-modulated proton therapy for intermediate- and high-risk meningioma. Radiat Oncol. 2015;10:1-7.
Orlandi M, Botti A, Sghedoni R, et al. Feasibility of voxel-based dose painting for recurrent Glioblastoma guided by ADC values of diffusion-weighted MR imaging. Phys Medica. 2016;32:1651-1658.
Orman AG, Pollack A, Stoyanova R, Wang K, Abramowitz M. A phase 3 randomized trial of MRI-mapped dose-escalated salvage radiation therapy postprostatectomy: the MAPS trial, an initial dosimetric assessment. Int J Radiat Oncol. 2014;90:S413-S414.
Ray S, Cekanaviciute E, Lima IP, Sørensen BS, Costes SV. Comparing photon and charged particle therapy using DNA damage biomarkers. Int J Part Ther [Internet]. 2018;5:15-24.
Surov A, Caysa H, Wienke A, Spielmann RP, Fiedler E. Correlation between different ADC fractions, cell count, Ki-67, total nucleic areas and average nucleic areas in meningothelial meningiomas. Anticancer Res. 2015;35:6841-6846.
Gonzalez-Segura A, Morales JM, Gonzalez-Darder JM, et al. Magnetic resonance microscopy at 14 Tesla and correlative histopathology of human brain tumor tissue. PLoS One. 2011;6:e27442.
Commins DL, Atkinson RD, Burnett ME. Review of meningioma histopathology. Neurosurg Focus. 2007;23:E3.
Nedjati-Gilani GL, Schneider T, Hall MG, et al. Machine learning based compartment models with permeability for white matter microstructure imaging. NeuroImage 2017;150:119-135.
Togao O, Hiwatashi A, Yamashita K, et al. Measurement of the perfusion fraction in brain tumors with intravoxel incoherent motion MR imaging: validation with histopathologic vascular density in meningiomas. Br J Radiol. 2018:20170912.
Grant Information :
MR/T020296/1 United Kingdom MRC_ Medical Research Council; EP/N018702-1 EPSRC; MR/T020296/1 UKRI Future Leaders Fellowship
Contributed Indexing :
Keywords: Meningioma; diffusion MRI; microstructure; particle therapy; quantitative imaging
Entry Date(s) :
Date Created: 20201228 Date Completed: 20210514 Latest Revision: 20210514
Update Code :
Czasopismo naukowe
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 (ρ app ) 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 ρ app 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 ρ app 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.
(© 2020 American Association of Physicists in Medicine.)

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