Inter-modality correlation of prostatic microenvironmental tissue stiffness and water diffusivity using quantitative functional imaging techniques.
Hugh Harvey1, Jeremie Fromageau2, Veronica Morgan1, Liz Bancroft3, Ros Eeles3, Jeff Bamber2, and Nandita deSouza1

1Radiotherapy & Imaging, The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom, 2The Institute of Cancer Research, London, United Kingdom, 3Oncogenetics, The Institute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom


Location-matched ROI analysis of tissue stiffness (SWE) and water diffusivity (ADC) in 9 normal prostates demonstrated that both techniques can adequately differentiate between peripheral and transitional zonal microenvironments, and that there is a weak negative correlation between tissue stiffness and water diffusivity in the peripheral zone. This suggests that factors such as microvascularity, cell size, extracellular matrix and macromolecules may have a differential effect on tissue stiffness and diffusivity. Transitional zone stiffness is too heterogeneous to demonstrate significant inter-modality correlation.


To ascertain whether zonal prostate tissue stiffness (quantitatively measured with SWE) negatively correlates to tissue water diffusion (quantitatively measured with DWI).


The relationship between tissue stiffness and water diffusivity in the prostate microenvironment has not been formally explored. Shear Wave Elastography (SWE) interrogates tissue stiffness using low-frequency, low amplitude ultrasound waves, producing a Young’s modulus estimate (kPa) which increases as tissue elasticity decreases [1]. Water diffusivity becomes increasingly restricted as tissues increase in cellularity and density, and is measured using Diffusion Weighted MRI quantitatively analysed by calculating Apparent Diffusion Coefficient (ADC mm2/s) maps [2]. Both functional techniques can be used to detect changes in the prostatic microenvironment, but it is not known whether a relationship exists between them which could be exploited in future for differentiating cancerous from non-cancerous regions.


10 patients with a family history of prostate cancer were prospectively recruited to a screening trial and underwent SWE (SuperSonic Imagine, France) and 3T endorectal mpMRI (Achieva, Philips, Best, Netherlands) in the same session. Two independent operators performed SWE imaging and region-of-interest (ROI) analysis using 5mm circular ROIs placed in peripheral (PZ) and transitional zones (TZ) in a sextant pattern at the base, midgland, and apex, as well in the seminal vesicles. Young’s modulus values were calculated for ROIs from each observer and interobserver differences documented. DWI images were obtained in the transverse plane (single shot EPI, TR 5000ms, TE 54ms, b = 0, 100, 300, 500, 800 s/mm2, FOV 100 mm, slice thickness 2.2 mm, matrix 80 × 79 extrapolated to 176 × 176). Isotropic ADCall maps were generated from all b values with the system software using mono-exponential fitting. Mean ADCall values were obtained from location-matched 5mm circular ROIs in the same sextant format as for the SWE studies using a method of cognitive fusion by an operator for ROI placement. Paired t-tests were performed between PZ and TZ values for each modality to ascertain zonal differences for each technique. Pearson’s correlation between kPa and ADCall values were calculated for each sextant location, and for the whole gland.


1 patient was excluded from correlation analysis due to a lack of ADC data (no MRI performed due to claustrophobia). No patients were found to have clinically significant disease on mpMRI or subsequent biopsy, therefore all sextants were considered to comprise normal prostate tissue. Paired t-tests demonstrated significant differences between PZ and TZ for both modalities (SWE mean difference -18.21 ± 13.24 kPa, p<0.0001; ADCall mean difference 245 ± 388, p<0.0001) (Table 1), implying that both techniques can successfully delineate PZ from TZ based on the functional properties being measured, and that the PZ is more elastic and has greater diffusion than the TZ. PZ also demonstrated less variation in stiffness than TZ (SD PZ 8.3, SD TZ 15.8). Whole gland peripheral zone ADCall and kPa values demonstrated a weak but significant negative correlation (r= -0.029, p=0.035, Figure 1), confirming that as tissue stiffness decreases, diffusivity increases. However, this was not significant at the per-sextant level (Table 2).

Discussion and Conclusions

The functional tissue parameters ADCall and kPa are not equivalent in their assessment of the microenvironment as shown by their overall weak correlation in the PZ. This suggests that factors such as microvascularity, cell size, extracellular matrix and macromolecules may have a differential effect on the measured parameters. No significant correlation was found between ADCall and kPa within the TZ (r=0.20, p=0.1375, Figure 2) suggesting that TZ regional heterogeneity is greater than any measurable correlation. Further studies will investigate a relationship between stiffness and true diffusion (by excluding low b-values in the ADC calculation) in order to eliminate perfusional effects from estimates of diffusivity. An increase in subject numbers would also increase powering. It is worth noting that the mean TZ kPa was 39.9 ± 15.8, which is greater than the cited cut-off value for tumour detection of 35kPa [3], implying that SWE is likely to be poorly specific for the identification of tumour within the TZ, but may be adequate in the PZ (mean kPa 21.7 ± 8.3).


CRUK and EPSRC support to the Cancer Imaging Centre at ICR and RMH in association with MRC & Dept of Health C1060/A10334, C1060/A16464 and NHS funding to the NIHR Biomedical Research Centre and the Clinical Research Facility in Imaging.


1. Woo, S. et al, Shear wave elastography assessment in the prostate: an intraobserver reproducibility study. Clinical imaging, 2014.

2. Tamada, T. et al, Diffusion-weighted MRI and its role in prostate cancer. NMR in biomedicine, 2014. 27(1): p. 25-38.

3. Correas, J.-M. et al, Prostate Cancer: Diagnostic Performance of Real-time Shear-Wave Elastography. Radiology, 2015. 275(1): p. 280-289.


Figure 1: Intermodality correlation for whole gland peripheral zone

Figure 2: Intermodality correlation for whole gland transitional zone

Table 1: ADCall and Young’s modulus values for the whole gland (excluding seminal vesicles)

Table 2: Mean ADCall and Young’s modulus values per peripheral zone sextant and seminal vesicles

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)