Microstructural models for diffusion MRI in breast cancer and surrounding stroma: an <i>ex vivo</i> study

Bailey, Colleen; Siow, Bernard; Panagiotaki, Eleftheria; Hipwell, John H.; Mertzanidou, Thomy; Owen, Julie; Gazinska, Patrycja; Pinder, Sarah; Alexander, Daniel C.; Hawkes, David J.

doi:10.1002/nbm.3679

Cited by 27 publications

(24 citation statements)

References 59 publications

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“…Except for some ex vivo investigations of fixed breast tissues with strong gradients, 37 a previous in vivo study involving 81 patients reported that water diffusion is nearly isotropic in various human breast tumors. 38 Therefore, the DWI signals for IMPULSED acquisitions are obtained by Table 1 lists tissue parameters that can be determined by analyses of DWI data.…”

Section: Diffusion Anisotropymentioning

confidence: 99%

Magnetic resonance imaging of mean cell size in human breast tumors

Jiang

et al. 2019

Magnetic Resonance in Med

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Purpose Cell size is a fundamental characteristic of all tissues, and changes in cell size in cancer reflect tumor status and response to treatments, such as apoptosis and cell‐cycle arrest. Unfortunately, cell size can currently be obtained only by pathological evaluation of tumor tissue samples obtained invasively. Previous imaging approaches are limited to preclinical MRI scanners or require relatively long acquisition times that are impractical for clinical imaging. There is a need to develop cell‐size imaging for clinical applications. Methods We propose a clinically feasible IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion) approach that can characterize mean cell sizes in solid tumors. We report the use of a combination of pulse sequences, using different gradient waveforms implemented on clinical MRI scanners and analytical equations based on these waveforms to analyze diffusion‐weighted MRI signals and derive specific microstructural parameters such as cell size. We also describe comprehensive validations of this approach using computer simulations, cell experiments in vitro, and animal experiments in vivo and demonstrate applications in preoperative breast cancer patients. Results With fast acquisitions (~7 minutes), IMPULSED can provide high‐resolution (1.3 mm in‐plane) mapping of mean cell size of human tumors in vivo on clinical 3T MRI scanners. All validations suggest that IMPULSED provides accurate and reliable measurements of mean cell size. Conclusion The proposed IMPULSED method can assess cell‐size variations in tumors of breast cancer patients, which may have the potential to assess early response to neoadjuvant therapy.

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Section: Diffusion Anisotropymentioning

confidence: 99%

Magnetic resonance imaging of mean cell size in human breast tumors

Jiang

et al. 2019

Magnetic Resonance in Med

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“…Recently, VERDICT parameters from ex vivo breast tissue were compared to histology, revealing an association between the intracellular fraction from MRI and the cell fraction from histology. The remaining signal component, assumed to be extracellular, was anisotropic, with directions similar to collagen fibre patterns in the stroma, but fibres were coherent only over distances of 1–2 mm, less than a typical clinical voxel size.…”

Section: Introductionmentioning

confidence: 99%

VERDICT MRI validation in fresh and fixed prostate specimens using patient‐specific moulds for histological and MR alignment

et al. 2019

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The VERDICT framework for modelling diffusion MRI data aims to relate parameters from a biophysical model to histological features used for tumour grading in prostate cancer. Validation of the VERDICT model is necessary for clinical use. This study compared VERDICT parameters obtained ex vivo with histology in five specimens from radical prostatectomy. A patient‐specific 3D‐printed mould was used to investigate the effects of fixation on VERDICT parameters and to aid registration to histology. A rich diffusion data set was acquired in each ex vivo prostate before and after fixation. At both time points, data were best described by a two‐compartment model: the model assumes that an anisotropic tensor compartment represents the extracellular space and a restricted sphere compartment models the intracellular space. The effect of fixation on model parameters associated with tissue microstructure was small. The patient‐specific mould minimized tissue deformations and co‐localized slices, so that rigid registration of MRI to histology images allowed region‐based comparison with histology. The VERDICT estimate of the intracellular volume fraction corresponded to histological indicators of cellular fraction, including high values in tumour regions. The average sphere radius from VERDICT, representing the average cell size, was relatively uniform across samples. The primary diffusion direction from the extracellular compartment of the VERDICT model aligned with collagen fibre patterns in the stroma obtained by structure tensor analysis. This confirmed the biophysical relationship between ex vivo VERDICT parameters and tissue microstructure from histology.

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“…Although extended diffusion protocols and more complex fitting models have proven useful in revealing non-invasive microstructure indices in breast ( 9 ) and prostate ( 10 ), they have not yet been examined in bone metastases. This study explores the potential of microstructure characterization in bone metastases using a rich diffusion protocol with b -values up to 3,000 s/mm 2 and a range of diffusion times, then fitting with a variety of diffusion models.…”

Section: Introductionmentioning

confidence: 99%

Microstructure Characterization of Bone Metastases from Prostate Cancer with Diffusion MRI: Preliminary Findings

et al. 2018

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PurposeTo examine the usefulness of rich diffusion protocols with high b-values and varying diffusion time for probing microstructure in bone metastases. Analysis techniques including biophysical and mathematical models were compared with the clinical apparent diffusion coefficient (ADC).MethodsFour patients were scanned using 13 b-values up to 3,000 s/mm2 and diffusion times ranging 18–52 ms. Data were fitted to mono-exponential ADC, intravoxel incoherent motion (IVIM), Kurtosis and Vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT) models. Parameters from the models were compared using correlation plots.ResultsADC and IVIM did not fit the data well, failing to capture the signal at high b-values. The Kurtosis model best explained the data in many voxels, but in voxels exhibiting a more time-dependent signal, the VERDICT model explained the data best. The ADC correlated significantly (p < 0.004) with the intracellular diffusion coefficient (r = 0.48), intracellular volume fraction (r = −0.21), and perfusion fraction (r = 0.46) parameters from VERDICT, suggesting that these factors all contribute to ADC contrast. The mean kurtosis correlated with the intracellular volume fraction parameter (r = 0.26) from VERDICT, consistent with the hypothesis that kurtosis relates to cellularity, but also correlated weakly with the intracellular diffusion coefficient (r = 0.18) and cell radius (r = 0.16) parameters, suggesting that it may be difficult to attribute physical meaning to kurtosis.ConclusionBoth Kurtosis and VERDICT explained the diffusion signal better than ADC and IVIM, primarily due to poor fitting at high b-values in the latter two models. The Kurtosis and VERDICT models captured information at high b using parameters (Kurtosis or intracellular volume fraction and radius) that do not have a simple relationship with ADC and that may provide additional microstructural information in bone metastases.

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Microstructural models for diffusion MRI in breast cancer and surrounding stroma: an ex vivo study

Cited by 27 publications

References 59 publications

Magnetic resonance imaging of mean cell size in human breast tumors

Magnetic resonance imaging of mean cell size in human breast tumors

VERDICT MRI validation in fresh and fixed prostate specimens using patient‐specific moulds for histological and MR alignment

Microstructure Characterization of Bone Metastases from Prostate Cancer with Diffusion MRI: Preliminary Findings

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