Noninvasive diffusion magnetic resonance imaging of brain tumour cell size for the early detection of therapeutic response.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
08 06 2020
Historique:
received: 21 05 2018
accepted: 07 05 2020
entrez: 10 6 2020
pubmed: 10 6 2020
medline: 15 12 2020
Statut: epublish

Résumé

Cancer cells differ in size from those of their host tissue and are known to change in size during the processes of cell death. A noninvasive method for monitoring cell size would be highly advantageous as a potential biomarker of malignancy and early therapeutic response. This need is particularly acute in brain tumours where biopsy is a highly invasive procedure. Here, diffusion MRI data were acquired in a GL261 glioma mouse model before and during treatment with Temozolomide. The biophysical model VERDICT (Vascular Extracellular and Restricted Diffusion for Cytometry in Tumours) was applied to the MRI data to quantify multi-compartmental parameters connected to the underlying tissue microstructure, which could potentially be useful clinical biomarkers. These parameters were compared to ADC and kurtosis diffusion models, and, measures from histology and optical projection tomography. MRI data was also acquired in patients to assess the feasibility of applying VERDICT in a range of different glioma subtypes. In the GL261 gliomas, cellular changes were detected according to the VERDICT model in advance of gross tumour volume changes as well as ADC and kurtosis models. VERDICT parameters in glioblastoma patients were most consistent with the GL261 mouse model, whilst displaying additional regions of localised tissue heterogeneity. The present VERDICT model was less appropriate for modelling more diffuse astrocytomas and oligodendrogliomas, but could be tuned to improve the representation of these tumour types. Biophysical modelling of the diffusion MRI signal permits monitoring of brain tumours without invasive intervention. VERDICT responds to microstructural changes induced by chemotherapy, is feasible within clinical scan times and could provide useful biomarkers of treatment response.

Identifiants

pubmed: 32514049
doi: 10.1038/s41598-020-65956-4
pii: 10.1038/s41598-020-65956-4
pmc: PMC7280197
doi:

Substances chimiques

Antineoplastic Agents, Alkylating 0
Temozolomide YF1K15M17Y

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

9223

Subventions

Organisme : Cancer Research UK
ID : 16463
Pays : United Kingdom
Organisme : Cancer Research UK
ID : 29458
Pays : United Kingdom
Organisme : Medical Research Council
ID : C1519/A10331
Pays : United Kingdom
Organisme : Wellcome Trust
ID : WT100247MA
Pays : United Kingdom

Références

Differentiation of glioblastoma multiforme from metastatic brain tumor using proton magnetic resonance spectroscopy, diffusion and perfusion metrics at 3 T. Cancer Imaging 12, 423 (2012).
Service, N. H. https://www.england.nhs.uk/pay-syst/national-tariff/tariff-engagement/ . (2016).
Shyamala, K., Girish, H. & Murgod, S. Risk of tumor cell seeding through biopsy and aspiration cytology. Journal of International Society of Preventive & Community Dentistry 4, 5 (2014).
doi: 10.4103/2231-0762.129446
Stupp, R. et al. Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. The lancet oncology 10, 459–466 (2009).
pubmed: 19269895 doi: 10.1016/S1470-2045(09)70025-7
van Linde, M. E. et al. Treatment outcome of patients with recurrent glioblastoma multiforme: a retrospective multicenter analysis. Journal of neuro-oncology 135, 183–192 (2017).
pubmed: 28730289 pmcid: 5658463 doi: 10.1007/s11060-017-2564-z
Macdonald, D. R., Cascino, T. L., Schold, S. C. Jr. & Cairncross, J. G. Response criteria for phase II studies of supratentorial malignant glioma. Journal of Clinical Oncology 8, 1277–1280 (1990).
pubmed: 2358840 doi: 10.1200/JCO.1990.8.7.1277
Young, R. et al. Potential utility of conventional MRI signs in diagnosing pseudoprogression in glioblastoma. Neurology 76, 1918–1924 (2011).
pubmed: 21624991 pmcid: 3115805 doi: 10.1212/WNL.0b013e31821d74e7
Verma, N., Cowperthwaite, M. C., Burnett, M. G. & Markey, M. K. Differentiating tumor recurrence from treatment necrosis: a review of neuro-oncologic imaging strategies. Neuro-oncology 15, 515–534 (2013).
pubmed: 23325863 pmcid: 3635510 doi: 10.1093/neuonc/nos307
Norden, A. D., Drappatz, J. & Wen, P. Y. Novel anti-angiogenic therapies for malignant gliomas. The Lancet Neurology 7, 1152–1160 (2008).
pubmed: 19007739 doi: 10.1016/S1474-4422(08)70260-6
Narayana, A. et al. Antiangiogenic therapy using bevacizumab in recurrent high-grade glioma: impact on local control and patient survival. Journal of neurosurgery 110, 173–180 (2009).
pubmed: 18834263 doi: 10.3171/2008.4.17492
Carr, H. Y. & Purcell, E. M. Effects of diffusion on free precession in nuclear magnetic resonance experiments. Physical review 94, 630 (1954).
doi: 10.1103/PhysRev.94.630
Basser, P. J., Mattiello, J. & LeBihan, D. MR diffusion tensor spectroscopy and imaging. Biophysical journal 66, 259–267 (1994).
pubmed: 8130344 pmcid: 1275686 doi: 10.1016/S0006-3495(94)80775-1
Maier, S. E., Sun, Y. & Mulkern, R. V. Diffusion imaging of brain tumors. Nmr Biomed 23, 849–864 (2010).
pubmed: 20886568 pmcid: 3000221 doi: 10.1002/nbm.1544
Sugahara, T. et al. Usefulness of diffusion‐weighted MRI with echo‐planar technique in the evaluation of cellularity in gliomas. Journal of Magnetic Resonance Imaging 9, 53–60 (1999).
pubmed: 10030650 doi: 10.1002/(SICI)1522-2586(199901)9:1<53::AID-JMRI7>3.0.CO;2-2
Thoeny, H. C. & Ross, B. D. Predicting and monitoring cancer treatment response with diffusion‐weighted MRI. Journal of Magnetic Resonance Imaging 32, 2–16 (2010).
pubmed: 20575076 doi: 10.1002/jmri.22167
Galbán, C., Hoff, B., Chenevert, T. & Ross, B. Diffusion MRI in early cancer therapeutic response assessment. Nmr Biomed 30, e3458 (2017).
doi: 10.1002/nbm.3458
Hoff, B. A. et al. Assessment of multiexponential diffusion features as MRI cancer therapy response metrics. Magnetic resonance in medicine 64, 1499–1509 (2010).
pubmed: 20860004 pmcid: 2965786 doi: 10.1002/mrm.22507
Bedair, R. et al. Assessment of early treatment response to neoadjuvant chemotherapy in breast cancer using non-mono-exponential diffusion models: a feasibility study comparing the baseline and mid-treatment MRI examinations. European radiology 27, 2726–2736 (2017).
pubmed: 27798751 doi: 10.1007/s00330-016-4630-x
Xu, J. et al. A comparative assessment of preclinical chemotherapeutic response of tumors using quantitative non-Gaussian diffusion MRI. Magnetic resonance imaging 37, 195–202 (2017).
pubmed: 27919785 doi: 10.1016/j.mri.2016.12.002
Hu, F. et al. The value of diffusion kurtosis imaging in assessing pathological complete response to neoadjuvant chemoradiation therapy in rectal cancer: a comparison with conventional diffusion-weighted imaging. Oncotarget 8, 75597 (2017).
pubmed: 29088894 pmcid: 5650449 doi: 10.18632/oncotarget.17491
Goshima, S. et al. Diffusion kurtosis imaging to assess response to treatment in hypervascular hepatocellular carcinoma. American Journal of Roentgenology 204, W543–W549 (2015).
pubmed: 25905960 doi: 10.2214/AJR.14.13235
Panagiotaki, E. et al. Noninvasive quantification of solid tumor microstructure using VERDICT MRI. Cancer research 74, 1902–1912 (2014).
pubmed: 24491802 doi: 10.1158/0008-5472.CAN-13-2511
Panagiotaki, E. et al. Microstructural characterization of normal and malignant human prostate tissue with vascular, extracellular, and restricted diffusion for cytometry in tumours magnetic resonance imaging. Investigative radiology 50, 218–227 (2015).
pubmed: 25426656 doi: 10.1097/RLI.0000000000000115
Zaccagna, F. et al. Non-invasive assessment of glioma microstructure using VERDICT MRI: correlation with histology. European Radiology, (2019).
Szatmári, T. et al. Detailed characterization of the mouse glioma 261 tumor model for experimental glioblastoma therapy. Cancer science 97, 546–553 (2006).
pubmed: 16734735 doi: 10.1111/j.1349-7006.2006.00208.x
Wen, P. Y. et al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. Journal of Clinical Oncology 28, 1963–1972 (2010).
pubmed: 20231676 doi: 10.1200/JCO.2009.26.3541
O’connor, J. P. et al. Imaging biomarker roadmap for cancer studies. Nature reviews Clinical oncology 14, 169 (2017).
pubmed: 27725679 doi: 10.1038/nrclinonc.2016.162
Panagiotaki, E. et al. Compartment models of the diffusion MR signal in brain white matter: a taxonomy and comparison. Neuroimage 59, 2241–2254 (2012).
pubmed: 22001791 doi: 10.1016/j.neuroimage.2011.09.081
Johnston, E. et al. INNOVATE: A prospective cohort study combining serum and urinary biomarkers with novel diffusion-weighted magnetic resonance imaging for the prediction and characterization of prostate cancer. BMC cancer 16, 816 (2016).
pubmed: 27769214 pmcid: 5073433 doi: 10.1186/s12885-016-2856-2
Slator, P. J. et al. Placenta microstructure and microcirculation imaging with diffusion MRI. Magnetic resonance in medicine 80, 756–766 (2018).
pubmed: 29230859 doi: 10.1002/mrm.27036
Zhang, H., Schneider, T., Wheeler-Kingshott, C. A. & Alexander, D. C. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 61, 1000–1016 (2012).
pubmed: 22484410 doi: 10.1016/j.neuroimage.2012.03.072
Colgan, N. et al. Application of neurite orientation dispersion and density imaging (NODDI) to a tau pathology model of Alzheimer’s disease. NeuroImage 125, 739–744 (2016).
pubmed: 26505297 pmcid: 4692518 doi: 10.1016/j.neuroimage.2015.10.043
Morse, D. L. et al. MRI‐measured water mobility increases in response to chemotherapy via multiple cell‐death mechanisms. NMR in Biomedicine: An International Journal Devoted to the Development and Application of Magnetic Resonance In vivo 20, 602–614 (2007).
doi: 10.1002/nbm.1127
Bortner, C. & Cidlowski, J. Apoptotic volume decrease and the incredible shrinking cell. Cell Death & Differentiation 9, 1307–1310 (2002).
doi: 10.1038/sj.cdd.4401126
Hutter, J. et al. Integrated and efficient diffusion-relaxometry using ZEBRA. Scientific reports 8, 15138 (2018).
pubmed: 30310108 pmcid: 6181938 doi: 10.1038/s41598-018-33463-2
Price, S. & Gillard, J. Imaging biomarkers of brain tumour margin and tumour invasion. The British journal of radiology 84, S159–S167 (2011).
pubmed: 22433826 pmcid: 3473903 doi: 10.1259/bjr/26838774
Smits, M. Imaging of oligodendroglioma. The British journal of radiology 89, 20150857 (2016).
pubmed: 26849038 pmcid: 4846213 doi: 10.1259/bjr.20150857
Makariou, E. & Patsalides, A. D. Intracranial calcifications. Appl Radiol 38, 48–50 (2009).
Reynaud, O. Time-dependent diffusion MRI in cancer: tissue modeling and applications. Frontiers in Physics 5, 58 (2017).
doi: 10.3389/fphy.2017.00058
Lampinen, B. et al. Searching for the neurite density with diffusion MRI: challenges for biophysical modeling. Human brain mapping 40, 2529–2545 (2019).
pubmed: 30802367 pmcid: 6503974 doi: 10.1002/hbm.24542
Novikov, D. S., Kiselev, V. G. & Jespersen, S. N. On modeling. Magnetic resonance in medicine 79, 3172–3193 (2018).
pubmed: 29493816 pmcid: 5905348 doi: 10.1002/mrm.27101
Lampinen, B. et al. Neurite density imaging versus imaging of microscopic anisotropy in diffusion MRI: a model comparison using spherical tensor encoding. Neuroimage 147, 517–531 (2017).
pubmed: 27903438 doi: 10.1016/j.neuroimage.2016.11.053
Veraart, J., Novikov, D. S. & Fieremans, E. TE dependent Diffusion Imaging (TEdDI) distinguishes between compartmental T2 relaxation times. NeuroImage 182, 360–369 (2018).
pubmed: 28935239 doi: 10.1016/j.neuroimage.2017.09.030
Workman, P. et al. Guidelines for the welfare and use of animals in cancer research. British journal of cancer 102, 1555 (2010).
pubmed: 20502460 pmcid: 2883160 doi: 10.1038/sj.bjc.6605642
Murday, J. & Cotts, R. M. Self‐diffusion coefficient of liquid lithium. The Journal of Chemical Physics 48, 4938–4945 (1968).
doi: 10.1063/1.1668160
Cook, P. et al. Camino: open-source diffusion-MRI reconstruction and processing. 14th Scientific Meeting of the International Society for Magnetic Resonance in Medicine 2759 (2006).
Mills, R. Self-diffusion in normal and heavy water in the range 1-45. deg. The Journal of Physical Chemistry 77, 685–688 (1973).
doi: 10.1021/j100624a025
Mohammadi, S., Möller, H. E., Kugel, H., Müller, D. K. & Deppe, M. Correcting eddy current and motion effects by affine whole‐brain registrations: Evaluation of three‐dimensional distortions and comparison with slicewise correction. Magnetic Resonance in Medicine 64, 1047–1056 (2010).
pubmed: 20574966 doi: 10.1002/mrm.22501
Penny, W. D., Friston, K. J., Ashburner, J. T., Kiebel, S. J. & Nichols, T. E. Statistical parametric mapping: the analysis of functional brain images. (Academic press (2011).

Auteurs

Thomas A Roberts (TA)

Centre for Advanced Biomedical Imaging, University College London, London, UK.

Harpreet Hyare (H)

Centre for Medical Imaging, Division of Medicine, University College London, London, UK.
Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, London, UK.

Giulia Agliardi (G)

Centre for Advanced Biomedical Imaging, University College London, London, UK.

Ben Hipwell (B)

Centre for Advanced Biomedical Imaging, University College London, London, UK.

Angela d'Esposito (A)

Centre for Advanced Biomedical Imaging, University College London, London, UK.

Andrada Ianus (A)

Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.

James O Breen-Norris (JO)

Centre for Advanced Biomedical Imaging, University College London, London, UK.

Rajiv Ramasawmy (R)

Centre for Advanced Biomedical Imaging, University College London, London, UK.

Valerie Taylor (V)

Centre for Advanced Biomedical Imaging, University College London, London, UK.

David Atkinson (D)

Centre for Medical Imaging, Division of Medicine, University College London, London, UK.

Shonit Punwani (S)

Centre for Medical Imaging, Division of Medicine, University College London, London, UK.

Mark F Lythgoe (MF)

Centre for Advanced Biomedical Imaging, University College London, London, UK.

Bernard Siow (B)

Centre for Advanced Biomedical Imaging, University College London, London, UK.

Sebastian Brandner (S)

Division of Neuropathology, UCL Institute of Neurology, London, UK.

Jeremy Rees (J)

National Hospital for Neurology and Neurosurgery, London, UK.

Eleftheria Panagiotaki (E)

Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.

Daniel C Alexander (DC)

Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.

Simon Walker-Samuel (S)

Centre for Advanced Biomedical Imaging, University College London, London, UK. simon.walkersamuel@ucl.ac.uk.

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