Quantitative diffusion MRI in prostate cancer: Image quality, what we can measure and how it improves clinical assessment

Fennessy, Fiona M.; Maier, Stephan E.

doi:10.1016/j.ejrad.2023.111066

Cited by 4 publications

(3 citation statements)

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“…Differences in ADC values are used in clinical examinations to probe abnormal tissue morphology because of presence of cancer within the prostate. [5][6][7] As DW-MRI can operate on time-scales compatible with water molecules traversing (sub)cellular lengths, it is sensitive to tissue features at spatial scales far below the voxel size. 7 Therefore, going beyond conventional macroscopic assessments, various diffusion or diffusion/relaxation MRI approaches have been explored to probe microscopic tissue properties such as compartmental volume fractions, cell diameters, and cellularity.…”

Section: Introductionmentioning

confidence: 99%

“…[5][6][7] As DW-MRI can operate on time-scales compatible with water molecules traversing (sub)cellular lengths, it is sensitive to tissue features at spatial scales far below the voxel size. 7 Therefore, going beyond conventional macroscopic assessments, various diffusion or diffusion/relaxation MRI approaches have been explored to probe microscopic tissue properties such as compartmental volume fractions, cell diameters, and cellularity. [7][8][9][10][11][12][13][14] However, the selectivity and specificity of these assessments depend on the compartment models used to extract these structural properties and their validation relies on correlations with histopathology of biopsies or whole mount sections that are often challenged by biased matchings with MR images.…”

Section: Introductionmentioning

confidence: 99%

“…7 Therefore, going beyond conventional macroscopic assessments, various diffusion or diffusion/relaxation MRI approaches have been explored to probe microscopic tissue properties such as compartmental volume fractions, cell diameters, and cellularity. [7][8][9][10][11][12][13][14] However, the selectivity and specificity of these assessments depend on the compartment models used to extract these structural properties and their validation relies on correlations with histopathology of biopsies or whole mount sections that are often challenged by biased matchings with MR images. 15 Besides DW-MRI, it is also possible to gauge the apparent diffusivity of tissue metabolites by diffusion-weighted 1 H-MR spectroscopy (DW-MRS).…”

Section: Introductionmentioning

confidence: 99%

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Diffusion‐weighted MR spectroscopy of the prostate

Stamatelatou,

Rizzo,

Simsek

et al. 2024

Magnetic Resonance in Med

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PurposeProstate tissue has a complex microstructure, mainly composed of epithelial and stromal cells, and of extracellular (acinar‐luminal) spaces. Diffusion‐weighted MR spectroscopy (DW‐MRS) is ideally suited to explore complex microstructure in vivo with metabolites selectively distributed in different subspaces. To date, this technique has been applied to brain and muscle. This study presents the development and pioneering utilization of 1H‐DW‐MRS in the prostate, accompanied by in vitro studies to support interpretations of in vivo findings.MethodsNine healthy volunteers underwent a prostate MR examination (mean age, 56 years; range, 31–66). Metabolic complexation was studied in vitro using solutions with major compounds found in prostatic fluid of the lumen. DW‐MRS was performed at 3 T with a non–water‐suppressed single‐voxel sequence with metabolite‐cycling to concurrently measure metabolite and water signals. The water signal was used in postprocessing as a reference in a motion‐compensation scheme. The spectra were fitted simultaneously in the spectral and diffusion‐weighting dimensions. Apparent diffusion coefficients (ADCs) were derived by fitting signal decays that were assumed to be mono‐exponential for metabolites and biexponential for water.ResultsDW‐MRS of the prostate revealed relatively low ADCs for Cho and Cr compounds, aligning with their intracellular location and higher ADCs for citrate and spermine supporting their luminal origin. In vitro assessments of the ADCs of citrate and spermine demonstrated their complex formation and protein binding. Tissue concentrations of MRS‐detectable metabolites were as expected for the voxel location.ConclusionsThis work successfully demonstrates the feasibility of 1H‐DW‐MRS of the prostate and its potential for providing valuable microstructural information.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Diffusion‐weighted MR spectroscopy of the prostate

Stamatelatou,

Rizzo,

Simsek

et al. 2024

Magnetic Resonance in Med

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Magnetic Resonance Elastography Combined With PI‐RADS v2.1 for the Identification of Clinically Significant Prostate Cancer

Chen,

Chen

et al. 2024

Magnetic Resonance Imaging

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BackgroundMultiparametric MRI may cause overdiagnosis of clinically significant prostate cancer (csPCa) with the Prostate Imaging Reporting and Data System version 2.1 (PI‐RADS v2.1).ObjectivesTo investigate the diagnostic performance of stiffness as a standalone and complementary marker to PI‐RADS v2.1 for diagnosing csPCa.Study TypeProspective.SubjectsOne hundred forty‐seven participants with pathologically confirmed prostate lesions (≥1 cm), including 71 with csPCa.Field Strength/SequenceT1‐weighted fast spin‐echo, T2‐weighted fast spin‐echo, single‐shot echo‐planar diffusion‐weighted imaging, fast 3D gradient‐echo T1‐weighted dynamic contrast‐enhanced imaging, and 3D single‐shot spin‐echo based echo‐planar MR elastography at 3.0 T.AssessmentThe PI‐RADS v2.1 score was assessed by three radiologists independently. Lesion shear stiffness (SS) values at 60 Hz and 90 Hz were measured. A modified PI‐RADS integrating stiffness with PI‐RADS v2.1 was developed. Diagnostic performance for csPCa was compared between stiffness, PI‐RADS v2.1 and the modified PI‐RADS.Statistical TestSpearman's correlation, Fleiss κ and intraclass correlation, Pearson correlation, one‐way analysis of variance, area under the receiver operating characteristic curve (AUC), and the Delong test. Significance level was P < 0.05.ResultsIn the peripheral zone, csPCa (N = 35) had significantly higher SS than non‐csPCa at 60 Hz (3.22 ± 0.66 kPa vs. 2.56 ± 0.56 kPa) and at 90 Hz (5.64 ± 1.30 kPa vs. 4.48 ± 0.84 kPa). PI‐RADS v2.1 showed 100% sensitivity, 58% specificity, and 0.79 AUC for detecting csPCa. SS achieved 97% sensitivity, 52% specificity, and 0.80 AUC at 60 Hz, while SS had 63% sensitivity, 87% specificity, and 0.78 AUC at 90 Hz. The modified PI‐RADS, combing SS at 60 Hz with PI‐RADS v2.1, resulted in a significantly increased AUC (0.86) compared to that of PI‐RADS v2.1, with a sensitivity of 97% and specificity of 75%.Data ConclusionStiffness can help identifying csPCa in the peripheral zone. Combining stiffness with the PI‐RADS v2.1 improved the diagnostic accuracy and specificity for csPCa.Evidence Level1Technical EfficacyStage 2

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