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dc.contributor.authorMcHugh, Damien J
dc.contributor.authorLipowska-Bhalla, Grazyna
dc.contributor.authorBabur, M
dc.contributor.authorWatson, Y
dc.contributor.authorPeset, Isabel
dc.contributor.authorMistry, Hitesh
dc.contributor.authorHubbard, Cristinacce PL
dc.contributor.authorNaish, JH
dc.contributor.authorHoneychurch, Jamie
dc.contributor.authorWilliams, KJ
dc.contributor.authorO'Connor, James P B
dc.contributor.authorParker, GJM
dc.date.accessioned2020-03-27T09:28:07Z
dc.date.available2020-03-27T09:28:07Z
dc.date.issued2020en
dc.identifier.citationMcHugh DJ, Lipowska-Bhalla G, Babur M, Watson Y, Peset I, Mistry HB, et al. Diffusion model comparison identifies distinct tumor sub-regions and tracks treatment response. Magn Reson Med. 2020.en
dc.identifier.pmid32057115en
dc.identifier.doi10.1002/mrm.28196en
dc.identifier.urihttp://hdl.handle.net/10541/622873
dc.description.abstractPURPOSE: MRI biomarkers of tumor response to treatment are typically obtained from parameters derived from a model applied to pre-treatment and post-treatment data. However, as tumors are spatially and temporally heterogeneous, different models may be necessary in different tumor regions, and model suitability may change over time. This work evaluates how the suitability of two diffusion-weighted (DW) MRI models varies spatially within tumors at the voxel level and in response to radiotherapy, potentially allowing inference of qualitatively different tumor microenvironments. METHODS: DW-MRI data were acquired in CT26 subcutaneous allografts before and after radiotherapy. Restricted and time-independent diffusion models were compared, with regions well-described by the former hypothesized to reflect cellular tissue, and those well-described by the latter expected to reflect necrosis or oedema. Technical and biological validation of the percentage of tissue described by the restricted diffusion microstructural model (termed %MM) was performed through simulations and histological comparison. RESULTS: Spatial and radiotherapy-related variation in model suitability was observed. %MM decreased from a mean of 64% at baseline to 44% 6 days post-radiotherapy in the treated group. %MM correlated negatively with the percentage of necrosis from histology, but overestimated it due to noise. Within MM regions, microstructural parameters were sensitive to radiotherapy-induced changes. CONCLUSIONS: There is spatial and radiotherapy-related variation in different models' suitability for describing diffusion in tumor tissue, suggesting the presence of different and changing tumor sub-regions. The biological and technical validation of the proposed %MM cancer imaging biomarker suggests it correlates with, but overestimates, the percentage of necrosis.en
dc.language.isoenen
dc.relation.urlhttps://dx.doi.org/10.1002/mrm.28196en
dc.titleDiffusion model comparison identifies distinct tumor sub-regions and tracks treatment responseen
dc.typeArticleen
dc.contributor.departmentQuantitative Biomedical Imaging Laboratory, The University of Manchester, Manchesteren
dc.identifier.journalMagnetic Resonance in Medicineen
dc.description.noteen]
refterms.dateFOA2020-03-31T09:52:52Z


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