Multiparametric MRI in the Diagnosis of Prostate Cancer: Physical Foundations, Limitations, and Prospective Advances of Diffusion-Weighted MRI

Abstract

Background Diffusion-weighted imaging (DWI) is an essential component of the multiparametric MRI exam for the diagnosis and assessment of prostate cancer (PCa). Over the last two decades, various models have been developed to quantitatively correlate the DWI signal with microstructural characteristics of prostate tissue. The simplest approach (ADC: apparent diffusion coefficient) – currently established as the clinical standard – describes monoexponential decay of the DWI signal. While numerous studies have shown an inverse correlation of ADC values with the Gleason score, the ADC model lacks specificity and is based on water diffusion dynamics that are not true in human tissue. This article aims to explain the biophysical limitations of the standard DWI model and to discuss the potential of more complex, advanced DWI models.

Methods This article is a review based on a selective literature review.

Results Four phenomenological DWI models are introduced: diffusion tensor imaging, intravoxel incoherent motion, biexponential model, and diffusion kurtosis imaging. Their parameters may potentially improve PCa diagnostics but show varying degrees of statistical significance with respect to the detection and characterization of PCa in current studies. Phenomenological model parameters lack specificity, which has motivated the development of more descriptive tissue models that directly relate microstructural features to the DWI signal. Finally, we present two of such structural models, i. e. the VERDICT (Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumors) and RSI (Restriction Spectrum Imaging) model. Both have shown promising results in initial studies regarding the characterization and prognosis of PCa.

Conclusion Recent developments in DWI techniques promise increasing accuracy and more specific statements about microstructural changes of PCa. However, further studies are necessary to establish a standardized DWI protocol for the diagnosis of PCa.

Key Points: 

• DWI is paramount to the mpMRI exam for the diagnosis of PCa.

• Though of clinical value, the ADC model lacks specificity and oversimplifies tissue complexities.

• Advanced phenomenological and structural models have been developed to describe the DWI signal.

• Phenomenological models may improve diagnostics but show inconsistent results regarding PCa assessment.

• Structural models have demonstrated promising results in initial studies regarding PCa characterization.

Citation Format

• Wichtmann BD, Zöllner FG, Attenberger UI et al. Multiparametric MRI in the Diagnosis of Prostate Cancer: Physical Foundations, Limitations, and Prospective Advances of Diffusion-Weighted MRI. Fortschr Röntgenstr 2021; 193: 399 – 409

Zusammenfassung

Hintergrund Die diffusionsgewichtete MRT-Bildgebung (DWI) ist wesentlicher Bestandteil der multiparametrischen MRT zur Diagnostik und Beurteilung des Prostatakarzinoms (PCa). In den letzten 2 Jahrzehnten wurden diverse Modelle entwickelt, um das DWI-Signal quantitativ mit mikrostrukturellen Charakteristiken des Prostatagewebes in Beziehung zu setzen. Der einfachste, heute als klinischer Standard etablierte Ansatz (apparenter Diffusionskoeffizient, ADC) beschreibt eine monoexponentielle Abnahme des DWI-Signals. Obwohl zahlreiche Studien eine inverse Korrelation von ADC-Werten mit dem Gleason-Score zeigen konnten, weist dieses Modell eine geringe Spezifität auf und basiert auf einer Wasserdiffusionsdynamik, die im menschlichen Gewebe nicht zutrifft. Ziel dieses Artikels ist es, die biophysikalischen Grenzen des Standard-DWI-Modells zu erklären und das Potenzial komplexerer, weiterentwickelter DWI-Modelle aufzuzeigen.

Methode Bei diesem Artikel handelt es sich um eine Übersichtsarbeit auf der Basis einer selektiven Literaturaufarbeitung.

Ergebnisse Vorgestellt werden die 4 phänomenologischen DWI-Modelle Diffusion Tensor Imaging, Intravoxel Incoherent Motion, Biexponential Model und Diffusion Kurtosis Imaging, deren Parameter möglicherweise einen Mehrwert in der PCa-Diagnostik darstellen, jedoch in aktuellen Studien eine variierende statistische Signifikanz hinsichtlich der Detektion und Charakterisierung des PCa zeigen. Phänomenologischen Modellparametern mangelt es an Spezifität, was die Entwicklung von deskriptiveren Gewebemodellen motivierte, die mikrostrukturelle Merkmale direkt mit dem Signal in Zusammenhang bringen. Abschließend stellen wir 2 dieser strukturellen Modelle vor, d. h. das VERDICT (Vascular, Extracellular and Restricted Diffusion for Cytometry in Tumors) sowie das RSI (Restriction Spectrum Imaging)-Modell. Beide zeigten in ersten Studien vielversprechende Ergebnisse bezüglich der Charakterisierung und Prognose des PCa.

Schlussfolgerung Neuere Entwicklungen der DWI-Techniken lassen eine zunehmende Genauigkeit und spezifischere Aussagen über mikrostrukturelle Veränderungen des PCa erwarten. Weitere Studien sind erforderlich, um ein standardisiertes DWI-Protokoll für die Diagnostik des PCa zu etablieren.

Kernaussagen:

• Die DWI ist für die mpMRI-Untersuchung zur Diagnose des PCa von großer Bedeutung.

• Obwohl von hohem klinischen Wert, weist das ADC-Modell eine geringe Spezifität auf und vereinfacht die Gewebekomplexität zu stark.

• Zur Beschreibung des DWI-Signals wurden weitergehende phänomenologische und strukturelle Modelle entwickelt.

• Phänomenologische Modelle können zu einer Verbesserung der Diagnostik führen, liefern jedoch inkonsistente Ergebnisse hinsichtlich der Beurteilung des PCa.

• Strukturelle Modelle zeigen in ersten Studien vielversprechende Ergebnisse hinsichtlich der Charakterisierung des PCa auf.

Key words

advanced diffusion-weighted imaging models - non-gaussian diffusion-weighted imaging models - compartment diffusion-weighted imaging models - MR-diffusion < METHODS & TECHNIQUES - multiparametric MRI - prostate cancer

Publication History

Received: 02 April 2020

Accepted: 18 September 2020

Article published online:
10 December 2020

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