Evaluation of Diffusion Weighted Imaging in the Context of Multi-Parametric MRI of the Prostate in the assessment of suspected low volume prostatic carcinoma

TL;DR

This study evaluates the effectiveness of combining T2-weighted imaging and diffusion-weighted imaging in multi-parametric MRI for early prostate cancer detection, emphasizing a simplified, cost-effective diagnostic protocol.

Contribution

It demonstrates that a mono-exponential model suffices for DWI analysis and proposes a simplified mp-MRI protocol combining T2w and DWI for early prostate cancer diagnosis.

Findings

ADC scores are significantly lower in malignant regions in most patients.

Mono-exponential model fits DWI data well up to b=1000 s/mm^2.

Non-Gaussian diffusion measures are not supported as biomarkers in this context.

Abstract

Data from a multi-parametric MRI study of patients with possible early-stage prostate cancer was assessed with a view to creating an efficient clinical protocol. Based on a correlation analysis suggesting that diffusion-weighted imaging (DWI) scores are more strongly correlated with overall PIRADS scores than other modalities such as dynamic contrast enhanced imaging or spectroscopy, we investigate the combination of T2-weighted imaging (T2w) and DWI as a potential diagnostic tool for prostate cancer detection, staging and guided biopsies. Quantification of the noise floor in the DWI images and careful fitting of the data suggests that the mono-exponential model provides a very good fit to the data and there is no evidence of non-Gaussian diffusion for $b$-values up to 1000 s/mm$^2$. This precludes the use of kurtosis or other non-Gaussian measures as a biomarker for prostate cancer in our case. However, the ADC scores for healthy and probably malignant regions are significantly lower for the latter in all 20 but one patient. The results suggest that a simplified mp-MRI protocol combining T2w and DWI may be a good compromise for a cost and time efficient, early-stage prostate cancer diagnostic programme, combining robust MR biomarkers for prostate cancer that can be reliably quantified and appear well-suited for general clinical practice.