Diffusion kurtosis imaging and standard diffusion imaging in the magnetic resonance imaging assessment of prostate cancer
Review Article

Diffusion kurtosis imaging and standard diffusion imaging in the magnetic resonance imaging assessment of prostate cancer

Pierpaolo Palumbo1^, Andrea Martinese2#, Maria Rosaria Antenucci2#, Vincenza Granata3, Roberta Fusco4, Federica De Muzio5, Maria Chiara Brunese5, Eleonora Bicci6, Alessandra Bruno7,8, Federico Bruno1, Andrea Giovagnoni7,8, Nicoletta Gandolfo9,10, Vittorio Miele6, Ernesto Di Cesare11*, Rosa Manetta12,13*

1Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, L’Aquila, Italy; 2Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, L’Aquila, Italy; 3Division of Radiology, “Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli”, Naples, Italy; 4Medical Oncology Division, Igea SpA, Napoli, Italy; 5Diagnostic Imaging Section, Department of Medical and Surgical Sciences & Neurosciences, University of Molise, Campobasso, Italy; 6Department of Emergency Radiology, University Hospital Careggi, Florence, Italy; 7Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Ancona, Italy; 8Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Ancona, Italy; 9Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, Genoa, Italy; 10Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Milan, Italy; 11Department of Life, Health and Environmental Sciences, University of L’Aquila, L’Aquila, Italy; 12Radiology Unit, San Salvatore Hospital, Abruzzo Health Unit 1, L’Aquila, Italy; 13Prostate Unit, San Salvatore Hospital, Abruzzo Health Unit 1, L’Aquila, Italy

Contributions: (I) Conception and design: V Granata, P Palumbo; (II) Administrative support: E Di Cesare; (III) Provision of study materials or patients: R Manetta, V Granata, R Fusco; (IV) Collection and assembly of data: P Palumbo, A Martinese, MR Antenucci; (V) Data analysis and interpretation: P Palumbo; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

#These authors contributed equally to this work.

*These authors contributed equally to this work as co-senior authors.

^ORCID: 0000-0003-1514-0092.

Correspondence to: Pierpaolo Palumbo, MD. Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, via Saragat, Località Campo di Pile, 67100 L’Aquila, Italy. Email: palumbopierpaolo89@gmail.com.

Background and Objective: In recent years, magnetic resonance imaging (MRI) has shown excellent results in the study of the prostate gland. MRI has indeed shown to be advantageous in the prostate cancer (PCa) detection, as in guiding targeting biopsy, improving its diagnostic yield. Although current acquisition protocols provide for multiparametric acquisition, recent evidence has shown that biparametric protocols can be non-inferior in PCa detection. Diffusion-weighted imaging (DWI) sequence, in particular, plays a key role, particularly in the peripheral zone which accounts for the larger part of the prostate. High b-values are generally recommended, although with the possibility of obtaining non-Gaussian diffusion effects, which requires a more sophisticated model for the analysis, namely through the diffusion kurtosis imaging (DKI). Purpose of this narrative review was to analyze the current applications and clinical evidence regarding the use of DKI with a main focus on PCa detection, also in comparison with DWI.

Methods: This narrative review synthesized the findings of literature retrieved from main researches, narrative and systematic reviews, and meta-analyses obtained from PubMed.

Key Content and Findings: DKI analyses the non-Gaussian water diffusivity and describe the effect of signal intensity decay related to high b-value through two main metrics (Dapp and Kapp). Differently from DWI-apparent diffusion coefficient (DWI-ADC) which reflects only water restriction outside of cells, DKI metrics are supposed to represent also the direct interaction of water molecules with cell membranes and intracellular compounds. This review describes current evidence on ADC and DKI metrics in clinical imaging, and finally collect the results derived from the main articles focused on DWI and DKI models in detecting PCa.

Conclusions: DKI advantages, compared to conventional ADC analysis, still remain controversial. Wider application and greater technical knowledge of DKI, however, may help in proving its intrinsic validity in the field of oncology and therefore in the study of clinically significant PCa. Finally, a deep understanding of DKI is important for radiologists to better understand what Kapp and Dapp mean in the context of different cancer and how these metrics may vary specifically in PCa imaging.

Keywords: Diffusion kurtosis imaging (DKI); diffusion-weighted imaging (DWI); prostate cancer (PCa)

Submitted Feb 12, 2023. Accepted for publication Nov 09, 2023. Published online Dec 22, 2023.

doi: 10.21037/gs-23-53

Introduction

Prostate cancer (PCa) is one of the most diagnosed cancers affecting men and represents one of the major causes of cancer-related death (1).

Current algorithms for PCa diagnosis and management provide different markers in the attempt to precociously diagnose PCa, since it could be asymptomatic in early phase or even associated with benign conditions (2-12).

Biopsy remains the gold standard for a confident diagnosis of PCa, although progression of technologies has led to increasing interest for advanced imaging (13,14) (Figure 1).

Figure 1 A 60-year-old male with increased PSA (last value 9.5 ng/mL). Patient referred nocturia, with a recent TRUS biopsy resulted negative. The patients underwent a mpMRI with a rounded lesion (white arrowheads in all images) of 8 mm localized in apex (left posterolateral peripheral zone). (A-D) Images show axial section. (E,F) Images show coronal and sagittal view, respectively. Low signal intensity in T2 sequences and high DWI and low ADC signal intensity allow to classify the lesion as PI-RADS v2.1: 4. After a fusion biopsy the lesion was classified as acinar adenocarcinoma of prostate, with a GS of 4+4=8 (Grade Grouping 3 according to the WHO 2016). PSA, prostate-specific antigen; TRUS, transrectal ultrasound; mpMRI, multiparametric magnetic resonance imaging; DWI, diffusion-weighted imaging; ADC, apparent diffusion coefficient; PI-RADS, Prostate Imaging-Reporting and Data System; GS, Gleason score; WHO, World Health Organization.

Notably, in latest years, magnetic resonance imaging (MRI) has assumed a primary role in the study of various districts and pathologies, becoming an integral component for diagnosis, risk stratification and staging of different cancers, and lately for targeting treatment (15-32).

Good diagnostic validity of MRI for PCa diagnosis derives from a high capability in combining morphological and functional data (33-43). From the study of Dola et al., multiparametric MRI (mpMRI) reached a sensitivity and specificity of 82.6% and 91.3%, respectively, with a positive and negative predictive value near to 100% (44).

MRI ability to adequately detect prostate lesions translate also in an improved diagnostic yield of biopsies, mostly through targeting the sample, and a good performance to detect local recurrency (45,46).

Current recommendations for prostate MRI acquisition protocol and interpretation, Prostate Imaging-Reporting and Data System (PI-RADS v2 and v2.1), edited by the American College of Radiology and ESUR, advise the acquisition of T2-weighted (T2W), diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) sequences.

However, it remains essential to codify appropriate decision algorithm capable of modeling the pre-test risk of patients in order to help the standardization of the MRI approach.

Controversies in prostate MRI

The improvement of imaging accuracy for PCa diagnosis through new MRI techniques and sequences remains today a primary target for the radiology community, e.g., in the latest years, several studies focused on the use of quantitative analysis and computer-assisted diagnosis (CAD) methods, including artificial intelligence (AI) tools, to mitigate the subjective nature of MRI interpretation (47-55). Besides, high attention is recently posed on deep learning and radiomics application in various district and pathologies, including PCa (16,17,56-66). However, radiomics has shown a translational gap in clinical practice, and many issues remain to be solved.

Meanwhile, main concern regards DCE, considering that PI-RADS v2.1 poses greater emphasis on T2W and DWI as primary sequences for PCa diagnosis, confining DCE as a dichotomic variable (67,68). A wide literature recently highlighted the overlapping diagnostic validity of biparametric and multiparametric protocol in detecting clinically significant PCa (43,69-75).

Actually, mpMRI showed a relative superior sensitivity than biparametric protocol, returning the mpMRI a valuable complement in equivocal cases or smaller lesions, although with the risk for higher indolent cancer detection (69).

On the other hand, biparametric approach needs high standard of image quality and level of expertise than multiparametric ones. In particular, DWI optimization remains crucial for a correct interpretation of prostate MRI.

Current recommendations advise for high b-values acquisition to improve DWI accuracy, although higher b-values pair with a reduced signal-to-noise ratio. Moreover, ultrahigh b-values often reveal the presence of non-Gaussian diffusion effects, which requires a more sophisticated model for the analysis (76,77).

Purpose of this narrative review is to analyze the application of DWI and diffusion kurtosis imaging (DKI) for prostate analysis and discuss current evidence of DKI approach in prostate field. We present this article in accordance with the Narrative Review reporting checklist (available at https://gs.amegroups.com/article/view/10.21037/gs-23-53/rc).

Methods

Information used to write this paper was collected from PubMed (keywords: DKI; DWI; prostate cancer; and combination of this words) and included narrative overview; clinical research; systematic review and meta-analysis. The sources are also listed in Table 1.

Table 1

The search strategy summary

Items Specification
Date of search Start of research: June 2021; last editing: July 2023
Databases and other sources searched PubMed
Search terms used DKI, DWI, prostate, prostate cancer
Timeframe 2000–May 2023
Inclusion and exclusion criteria Inclusion criteria: research article, narrative review, systematic review, meta-analysis (only English article)
Selection process The research of literature was performed independently by four different authors (one senior researcher and three junior researchers) who then compared searches to avoid overlap. The resulting articles was then analyzed by the senior researcher who cataloged the evidence of each paper dividing them by category as follows: paper concerning the state of the art of MRI and prostate cancer; paper concerning the basic principles of DWI-ADC and DKI sequences; paper concerning evidence of DWI-ADC and DKI performance in PCa diagnosis. The final manuscript was compiled following the evidence and cataloging method

DKI, diffusion kurtosis imaging; DWI, diffusion-weighted imaging; MRI, magnetic resonance imaging; ADC, apparent diffusion coefficient; PCa, prostate cancer.

Discussion

DWI

Owing to its unique sensitivity to the evaluation of molecular self-diffusion of water, DWI is a powerful tool for the non-invasive study of micro-structural properties of biological tissue in vivo.

Specifically, DWI analyzes the spontaneous mobility of water molecules (termed Brownian motion) reflecting their degree of motion (termed diffusion), thus potentially mirroring a cellular abnormality in a specific biologic tissue (78-82).

As diffusion is mostly restricted by cell membranes, the extent of restriction of free motion could be indeed proportionate to the cellular density of a tissue (83-85).

DWI currently results as one of the main sequences in detection and characterization of cancer lesion (86-93).

In prostate gland, as example, reduction in the movements of free water can derived from the replacement of large interstitial spaces and glandular lumens by nests of tumor cells and fibrous stroma as in PCa (46,94-99).

DWI is recognized as a primary determining sequence to assign the PI-RADS score for lesions within the peripheral zone (which account for 70–75% of the glandular tissue, and where the 85% of PCa cases are localized), identifying 5 scores based on the degree of restriction and the size of the restricted area (68,100).

DW-MRI most commonly relies on single-shot echo-planar-imaging spin-echo sequences with an application of two rectangular gradient pulses of an equal strength, applied before and after a 180° refocusing pulse (101). “Restricted” water molecules are dephased by the first pulse and completely rephrased by the second pulse (which gives back high signal) (102).

The strength and duration of the gradient pulses is expressed by the b-value.

High b-value are helpful for the visualization of clinically significant PCa by preserving the signal intensity only in the highly restricted area, especially in sub-capsular lesions (103-116).

Usually three b-values are obtained in clinical practice (with low values of about 50 s/mm2, and the higher one of at least 1,000 s/mm2), with current recommendation suggesting also high b-value for an adequate acquisition, although there is no widely accepted “high b-value” available in literature. Maximum b-value ranges from 2,000 to 3,000 seconds/mm2 (29), while higher b-values are not recommended.

From the study of Metens et al., highest tumor visibility was reached using b-values ranging from 1,500 to 2,000 seconds/mm2, with the best contrast-to-noise ratio (CNR) for b 1,500 seconds/mm2 using a 3-T magnetic resonance (MR) scanner (117). These results were confirmed by Katahira et al., who found the highest sensitivity (73.2%), specificity (89.7%) and accuracy (84.2%) for PCa detection using a b-value of 2,000 seconds/mm2 in addition to T2W imaging (T2WI) (106).

However, sensitivity in detecting clinically significant PCa tend to decrease with b-value higher than 3,200 seconds/mm2 (0.871 to 0.800), considering that signal-to-noise ratio (SNR) decreases as the b-value increases (118).

Historically, one of the main limits of MRI lies in fact, in the low ability to obtain good images quality at b-values greater than 1,000 seconds/mm2 due to insufficient SNR. Improvements in hardware and software however has recently enable the acquisition of ultrahigh b-value (119).

Moreover, acquiring DWI at ultrahigh b-values often reveals the presence of non-Gaussian diffusion effects, thus requiring a more sophisticated model for analysis (e.g., DKI) (120).

Other significant advantages derive from apparent diffusion coefficient (ADC) (121) maps. ADC refers to the measure of the magnitude of diffusion, resulting as the expression of the signal decay with increased b-value.

Water molecules restriction derived from areas with densely packed tumor cells, shows bright signal on DW-MRI and darker on the ADC map during visual qualitative assessment.

At least two b-values allows the calculation of ADC.

According to the ESUR guidelines, lower recommended values is 50–100 seconds/mm2 while high b-value is recommended in the range from 800–1,000 to 2,000 seconds/mm2.

However, besides the qualitative analysis, ADC allows also a quantitative assessment, proving to be a useful marker of tumor aggressiveness (122).

In fact, ADC values showed high correlation with cellularity in different study.

Different options are available for quantitative analysis, with ADC-ratio showing the higher accuracy (123), thus improving MRI accuracy in detection and localization of PCa.

Uncertainty regarding the reproducibility of the ADC hampers the use of quantitative DWI in PCa-MRI. From the study of Boss et al., test-retest repeatability and multi-day reproducibility were largely equivalent, with an inter-reader reliability for focal lesion ADC high across time points. However, controversial results derive from literature, and a quantitative ADC analysis results still limited (124-126).

Noteworthy, in latest years radiomics and machine learning (ML) have emerged as novel techniques for MRI analysis, through a quantitative assessment of intra- and intertumoral heterogeneities in the effort to extract latent information from standard acquisition (the so-called “radiomics hypothesis”).

In particular, texture analysis, as part of radiomics, allows grey-level intensity and pixels’ position, arrangement evaluation, and voxel intensities interrelation (127).

Several researchers have reported the usefulness of ML models using texture features extracted from DWI and T2WI for detecting and grading PCa. From the study of Fehr et al., PCa diagnosis can be improved by combining data-augmented classification together with ML model, compared with using ADC mean or T2 signal intensities alone [e.g., combined data reached an accuracy of 93% in differentiating Gleason Score (GS) of 6 and ≥7 for cancers occurring in both peripheral and transition zones vs. 58% using ADC mean only] (128).

Literature however is still lacking extensive studies including texture analysis for PCa, and validation studies in large cohorts are needed.

DKI

DKI analyses the non-Gaussian water diffusivity.

Specifically, DKI model describe the effect of signal intensity (SI) decay related to high b-value. Logarithmic SI decay plot for high b-value exhibits a non-linear shape, with a positive deviation from the plot of the mono-exponential model [mono-exponential model, valuable for low b-values up to 600–1,000 seconds/mm2, applies a linear fit to the natural logarithm of the signal intensity (SI)]. This deviation indicates the presence of water diffusion behaviors different from Gaussian predictions. Accordingly, both models should be applied (129).

DKI model uses two main metrics, defined as Dapp and Kapp.

Kapp refers to the apparent diffusional kurtosis (unitless) and reflects the more peaked distribution of tissue diffusivities occurring within the setting of non-Gaussian diffusion behavior.

Dapp is the diffusion coefficient (unit: ×10−3 mm2/s, µm2/millisecond, or ×103 µm2/s) corrected to account for the observed non-Gaussian behavior (130). Dapp is determined by the slope of the SI decay plot as b approaches to 0 (131).

Differently from other mathematical model including the bi-exponential one, DKI model potentially better describes water diffusivity in tissues at ultrahigh b-values, providing also an additional parameter (i.e., Kapp) that contains specific information on the non-Gaussian diffusion behavior. However, up to date, all models for high b-value diffusion-weighted images in PCa, including the biexponential, kurtosis, stretched exponential, and gamma distribution models achieve similar areas under the curve (AUCs) for discrimination of normal and cancer tissue, although biexponential and gamma distribution models produce statistically preferred fits (132).

Kapp is a phenomenological parameter with no biophysical correlate, similar to ADC.

ADC, as mentioned, reflects only water restriction outside of cells, which is influenced by tissue architectural properties. Therefore, besides the increasing cellular density, greater concentration of macromolecules and increased viscosity also can affect ADC.

Kapp is supposed to represent the direct interaction of water molecules with cell membranes and intracellular compounds, although other factors could influence these interactions. From the studies of Le Bihan (129), complex interaction of water molecules, interfaces and protein, with a polar nature of micromolecular components, may result in a significant restriction to water motion and contribution to non-Gaussian diffusion observations. Increased kurtosis could occur in the setting of more irregular and heterogeneous environments with many or large interfaces, including the increased nuclear-cytoplasmic ratio of tumor cells (133-135).

However, some technical aspect should be considered for DKI analysis.

First, DKI analysis needs separate post-processing software since current MR systems do not offer in-line DKI post-processing options.

DKI assessment should offer two maps (Dapp and Kapp).

Dapp map is similar to ADC map. Therefore, ADC and mean diffusivity values in PCa resulted lower than the regular parenchyma, while mean kurtosis value resulted higher (136-138) (Figures 2,3). Nevertheless, quantitative analysis of both Dapp and Kapp values is recommended since a reduced of Dapp not necessarily pairs with elevated kapp, such as for viscous or turbid fluid.

Figure 2 Prostate of considerably increased volume. Evidence of multiple nodular lesions scattered in the glandular parenchyma, the largest in the central and left lateral area with a diameter of 40 mm (thick white arrow). DWI sequences confirm diffusion restriction of water molecules at this level, classified as PI-RADS 4. ADC, apparent diffusion coefficient; DT, tissue pure diffusion; FP, perfusion fraction; DP, pseudo-diffusion; MD, mean diffusivity; MK, mean kurtosis; DWI, diffusion-weighted imaging; PI-RADS, Prostate Imaging-Reporting and Data System.
Figure 3 Prostate of normal volume, with a nodular lesion (thick white arrow) at level of the left anterior median-paramedian portion (diameter of 20 mm). DWI sequences confirm diffusion restriction of water molecules at this level. The lesion was classified as PI-RADS 5. ADC, apparent diffusion coefficient; DT, tissue pure diffusion; FP, perfusion fraction; DP, pseudo-diffusion; MD, mean diffusivity; MK, mean kurtosis; DWI, diffusion-weighted imaging; PI-RADS, Prostate Imaging-Reporting and Data System.

Second, to increase DKI metrics accuracy, sufficient SNR is critical. In fact, low SI leads to biased estimation of Kapp (139,140). Therefore, excessively high b-values (i.e., over 3,000 s/mm2) are therefore discouraged (141). Also, the use of a 3-T system, when available, could be a successful strategy to improves SNR (142).

In this regard, obtaining adequate SNR using high b-value is often difficult in body imaging. In fact, the use of sequences with faster acquisitions to avoid typical artifacts (e.g., breathing artifact) is associated with a faster decay of the signal. Moreover, DKI requires a minimum of three b-values, although with the risk of increasing the overall scan time and the likelihood of motion artifacts (142).

On the other hand, b-values including both high (500–1,000 s/mm2) and ultrahigh (1,500–2,000 s/mm2) ranges, may be useful for successfully capturing the mono-exponential and non-Gaussian components of the SI decay curve, respectively.

Therefore, the optimal number of b-values to obtain cannot be strictly prescribed and will depend on the clinical application, given the pronounced risk of longer acquisition and subsequent artifact (143,144).

DKI and PCa

DKI was first described in 2004 (145) and 2005 (131), and initially applied exclusively for brain imaging. Among multiple extra-cranial sites, DKI was recently explored also in PCa. However, the relatively young age of this analysis pairs with contrasting results especially about its added value compared to standard DWI protocol.

First interesting results came from the study of Rosenkrantz et al. (146), including a comparative analysis between diffusion imaging metrics and 121 cancerous sextants from 47 prostate patients (70 with a GS of 6, and the remaining 51 cancerous sextants with a GS greater than 6).

From a mixed-model analysis of variance and ROC analysis, DKI metrics resulted significantly altered both in tumor compared to normal parenchyma as well as in tumor, with respect of the GS grading. Notably, Kapp showed a higher sensitivity than ADC and Dapp for tumor vs. regular parenchyma differentiation (93.3% vs. 78.5% of ADC, P<0.001; and vs. 83.5% of Dapp, P<0.001), as a higher AUC for GS differentiation (146).

The obvious clinical impact of a correct differentiation of the degree of cancer aggressiveness is also shown by the recent evidences regarding the post-operative upgrading of the GS, widely recognized as an unfavorable prognostic factor both for a worse patient prognosis and for the risk of retreatment (147-149).

Therefore, imaging metrics capable in correcting staging a higher tumor aggressiveness and potential post-operative GS upgrading should be considered of primary importance in regular analysis.

In this regard, some interesting evidences are suggested by the study of Hectors et al., in which DKI shows a good correlation with the histopathological parameters of PCa (150); and the study of Wu et al. showed that a comprehensive consideration of DKI and prostate-specific antigen (PSA) may be a promising approach to predicting GS upgrade, with an AUC of the model Kapp-PSA reaching 0.868 vs. 0.819 shown by the single Kapp parameters (151).

A better estimation of tumor aggressiveness should be mandatory also in active surveillance patients, with preliminary results of a different study of Rosenkrantz et al., suggesting that diffusional kurtosis imaging findings may have more value than standard DWI as a marker of adverse final pathologic outcome among active surveillance candidates (152).

Of note, two other study corroborate an advantageous DKI impact.

From the study of Park et al., comparing the diagnostic performance of DKI metrics and ADC for determination of clinically significant prostate cancer (csPCa) (i.e., GS >7) in 92 patients, a major sensitivity of DKI was highlighted compared to mono-exponential ADC (153).

And similarly, robust results derived also from a recent meta-analysis of Shen et al., including 14 studies involving 1,847 lesions in 1,107 patients.

Pooled analysis showed an overall AUC of 0.89 for Kapp and 0.92 for Dappvs. 0.89 of DWI, with the superiority of Dapp to Kapp and ADC in separating malignant cancers from benign lesions, also confirmed by their subgroup analysis of PCa (154).

These finding could suggest an added value of DKI to the routine imaging protocol for screening cancer.

However, the body of literature resulted still controversial on DKI superiority considering that different works showed also a failed superiority of DKI metrics than mono-exponential ADC.

Firstly, from the study of Quentin et al., including 14 PCa patients and 10 healthy volunteers, although the mean kurtosis value was significantly higher in PCa than in the normal peripheral and central zones, DKI metrics weakly correlated with GS (155).

Secondly, from the study of Roethke et al., prostate DKI yields no significant added value for cancer detection compared with a standard DWI-derived mono-exponential ADC measurement (156).

Similar result was shown also from the study of Tamada et al. which included a larger population sample (255 patients), with the purpose to compare the value of DW-MRI and DKI for detection and characterization of PCa (157,158).

ADC and Kapp, in fact, were highly correlated, had similar diagnostic performance, and were concordant for the various outcomes in the large majority of cases, with non-different AUC for csPCa differentiation (P=0.15).

Finally, from a recent meta-analysis of Si et al., DKI does not provide significant added value for tumor detection in the peripheral zone. Because of the significant overlap in quantitative values between different tissue types, neither DKI nor ADC alone seems promising for a patient-based assessment of tumor aggressiveness. Therefore, for routine clinical application, ADC derived from single-exponential DWI remains the standard (156,159,160). A summary of the mentioned studies published focusing on DKI in prostate analysis is shown in Table 2.

Table 2

Overview of main studies included in the review

Study Journal Country No. of patients Type of paper Validation of results Results
Rosenkrantz et al. (146) Radiology USA 47 Original Systematic sextant needle biopsy K higher in cancerous sextants than in benign PZ
K higher in cancerous sextants with higher rather than lower GS
K showed greater SE than ADC or D (93.3% vs. 78.5% and 83.5%, respectively), with equal SP
K had significantly greater AUC for differentiating sextants with low- and high-grade cancer than ADC
Hectors et al. (150) Radiology USA 24 Original Prostatectomy DWI parameters (including DKI) were significantly different between prostate cancer and PZ
Kurtosis showed significant correlations with histopathologic parameters (P<0.04)
Wu et al. (151) AJR Am J Roentgenol China 52 Original Prostatectomy K max had the highest ROC AUC value (0.819, P<0.05)
PSA-K max had the highest AUC (0.868, P<0.05) and Youden index (0.652)
Rosenkrantz et al. (152) AJR Am J Roentgenol USA 58 Original Biopsy cores Only D was significantly lower in patients with adverse final pathologic findings
Park et al. (153) Abdom Radiol (NY) Korea 92 Original Pathologic topographic maps or systemic biopsy results Similar ROC-AUC of K, ADC and D for discriminating CSC from non-CSC
Shen et al. (154) Clin Imaging China NA Meta-analysis NA Pooled analysis showed a superiority of D analysis to K and ADC:
K = SE: 0.83; SP: 0.83; +LR: 4.61; −LR: 0.22; AUC: 0.89
D = SE: 0.85; SP: 0.85; +LR: 6.39; −LR: 0.19; AUC: 0.92
ADC = SE: 0.82; SP: 0.85; +LR: 4.75; −LR: 0.24; AUC: 0.89
Quentin et al. (155) Magn Reson Imaging Germany 24 Original Biopsy proven PCa Monoexponential ADC is sufficient to discriminate prostate cancer from normal tissue (b-values ranging from 0 to 800 s/mm)
Roethke et al. (156) Invest Radiol Germany 55 Original Image-guided targeted biopsy D was significantly lower in tumor compared with control regions
K was significantly higher in tumor
D was significantly higher than standard ADC both in tumor regions and in controls
ROC analyses showed similar capability between DKI and ADC for detection of PCa
ROC analyses showed significant capability between DKI and ADC for discrimination between high- and low-grade findings
Tamada et al. (157) Radiology USA 285 Original Prostatectomy ADC and K showed significant differences for benign vs. tumor tissues
ROC AUC-ADC (0.921) > ROC AUC-K (0.902) for benign vs. malignant tissue but was similar for high GS discrimination
Si et al. (159) AJR Am J Roentgenol China NA Meta-analysis NA ADC = pooled SE: 0.89; pooled SP: 0.86; ROC AUC: 0.93
D = pooled SE: 0.91; pooled SP: 0.78; ROC AUC: 0.89
K = pooled SE: 0.87; pooled SP: 0.85; ROC AUC: 0.93

K, kurtosis; PZ, peripheral zone; GS, Gleason Score; SE, sensitivity; ADC, apparent diffusion coefficient; D, diffusion; SP, specificity; AUC, area under the curve; DWI, diffusion-weighted imaging; DKI, diffusion kurtosis imaging; max, maximum; ROC, receiver operating characteristic; PSA, prostate-specific antigen; CSC, cancer stem cells; NA, not available; LR, likelihood ratio.

Given these premises, DKI did not show a clear added value compared with standard DWI for clinical PCa, therefore remaining debatable whether it should be incorporated into routine clinical imaging, also considering the longer scan time given the need to acquire at least three b-values.

However, the need to optimize MRI protocols for cancer screening is continuously growing, with high attention on non-conventional analysis, as radiomics. And therefore, this evidence may represent the starting point for the development of protocols including the use of DKI, thanks to its promising diagnostic application and interesting preliminary results in PCa detection and staging (161).

Conclusions

DWI and its associated ADC map remain, at present, the most reliable imaging approach to the PCa. Recently, different studies have examined the value of DKI compared with standard DWI in detecting PCa and assessing its aggressiveness. However, the results still remain controversial, probably limited also from the study samples investigated. Wider application and greater technical knowledge of DKI, however, may help prove its intrinsic validity in the field of oncology and therefore in the study of csPCa.

Acknowledgments

The authors wish to thank Angela Martella (Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, L’Aquila, Italy) for the English revision.

Funding: None.

Footnote

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://gs.amegroups.com/article/view/10.21037/gs-23-53/rc

Peer Review File: Available at https://gs.amegroups.com/article/view/10.21037/gs-23-53/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://gs.amegroups.com/article/view/10.21037/gs-23-53/coif). R.F. is an employee at Medical Oncology Division, Igea SpA. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Cite this article as: Palumbo P, Martinese A, Antenucci MR, Granata V, Fusco R, De Muzio F, Brunese MC, Bicci E, Bruno A, Bruno F, Giovagnoni A, Gandolfo N, Miele V, Di Cesare E, Manetta R. Diffusion kurtosis imaging and standard diffusion imaging in the magnetic resonance imaging assessment of prostate cancer. Gland Surg 2023;12(12):1806-1822. doi: 10.21037/gs-23-53

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