Assessing the validity of Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) scoring system in diagnosis of peripheral zone prostate cancer

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

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.

Quantitative ADC: An Additional Tool in the Evaluation of Prostate Cancer?

Prostate cancer is one of the most common tumors among the male population. Magnetic resonance imaging (MRI), standardized by the PI-RADS version 2.1 scoring system, has a fundamental role in detecting prostate cancer and evaluating its aggressiveness. Diffusion-weighted imaging sequences and apparent diffusion coefficient values, in particular, are considered fundamental for the detection and characterization of lesions. In 2016 the International Society of Urological Pathology introduced a new anatomopathological 5-grade scoring system for prostate cancer. The aim of this study is to evaluate the correlation between quantitative apparent diffusion coefficient values (ADC) derived from diffusion-weighted imaging (DWI) sequences and the International Society of Urological Pathology (ISUP) and PI-RADS groups. Our retrospective study included 143 patients with 154 suspicious lesions, observed on prostate magnetic resonance imaging and compared with the histological results of the biopsy. We observed that ADC values can aid in discriminating between not clinically significant (ISUP 1) and clinically significant (ISUP 2-5) prostate cancers. In fact, ADC values were lower in ISUP 5 lesions than in negative lesions. We also found a correlation between ADC values and PI-RADS groups; we noted lower ADC values in the PI-RADS 5 and PI-RADS 4 groups than in the PI-RADS 3 group. In conclusion, quantitative apparent diffusion coefficient values can be useful to assess the aggressiveness of prostate cancer.

Can a prostate biopsy be safely deferred on PI-RADS 1,2 or 3 lesions seen on pre-biopsy mp-MRI?

Introduction

Multi-parametric magnetic resonance imaging (mp-MRI) is currently used to triage patients with suspected prostate cancer, before deciding on prostate biopsies. In our study, we evaluated normal and equivocal pre-biopsy mp-MRIs to see whether it is safe to avoid biopsy with such findings.

Methods

A retrospective study was conducted at a district general hospital in the UK between August 2017 and July 2018. Patients with negative and equivocal prebiopsy mp-MRI with high clinical suspicion of cancer had proceeded to biopsy. MRI reports with prostate imaging reporting and data system (PI-RADS) scores 1, 2, 3 and normal MRI were evaluated against the transrectal ultrasound-guided prostate biopsy (TRUS-PB) outcomes to demonstrate benign pathology, clinically insignificant or clinically significant cancer (csCa). CsCa was defined as Gleason score (GS) ≥3 + 4.

Results

Out of 265 mp-MRIs studied, five (1.9%) were PI-RADS 1, 109 (41.1%) and 84 (31.7%) were PI-RADS 2 and 3 lesions respectively; 67 (25.3%) were reported as normal. Seventy-five (27.3%) patients did not have biopsies following their MRI and 73.3% (51/75) of them had benign feeling prostate. Negative MRIs (PI-RADS 1, 2 and normal MRI) showed 8.8% and PI-RADS 3 lesions demonstrated 11.9% csCa. Negative predictive value for normal MRI was 91.2%. Mean PSA density (PSAD) among the benign, GS 3 + 3 and csCa was 0.14, 0.16 and 0.27 ng/ml/ml respectively and this was statistically significant (p < 0.001). The average percentage of cancer found in GS 3 + 3 and csCa was 3.2% and 20.1%, respectively.

Conclusion

Avoiding TRUS-PB following normal or equivocal mp-MRI should carefully be decided as 18.5% of cancer was demonstrated in this group and 9.8% of those who were diagnosed with cancer were csCa. PSAD and DRE findings provide additional information to help with this decision.

Three-dimensional ultrasound integrating nomogram and the blood flow image for prostate cancer diagnosis and biopsy: A retrospective study

Backgrounds

Prostate cancer (PCa) is the second most common male cancer in the world and based on its high prevalence and overwhelming effect on patients, more precise diagnostic and therapeutic methods are essential research topics. As such, this study aims to evaluate the value of three-dimensional transrectal ultrasound (3D-TRUS) in the detection, diagnosis and biopsy of PCa, and to provide a basis for clinical practice of PCa.

Methods

Retrospective analysis and comparison of a total of 401 male patients who underwent prostate TRUS in our hospital from 2019 to 2020 were conducted, with all patients having prostate biopsy. Nomogram was used to estimate the probability of different ultrasound signs in diagnosing prostate cancer. The ROC curve was used to estimate the screening and diagnosis rates of 3D-TRUS, MRI and TRUS for prostate cancer.

Results

A total of 401 patients were randomly divided into two groups according to different methods of prostate ultrasonography, namely the TRUS group (251 patients) and the 3D-TRUS group (150 patients). Of these cases, 111 patients in 3D-TRUS group underwent MRI scan. The nomogram further determined the value of 3D-TRUS for prostate cancer. The ROC AUC of prostate cancer detected by TRUS, MRI and 3D-TRUS was 0.5580, 0.6216 and 0.6267 respectively. Biopsy complications were lower in 3D-TRUS group than TRUS group, which was statistically significant (P&lt;0.005).

Conclusions

The accuracy of 3D-TRUS was higher in diagnosis and biopsy of prostate cancer. Meanwhile, the positive rate of biopsy could be improved under direct visualization of 3D-TRUS, and the complications could be decreased markedly. Therefore, 3D-TRUS was of high clinical value in diagnosis and biopsy of prostate cancer.

Prediction of Prostate Cancer Aggressiveness Using a Novel Multiparametric Magnetic Resonance Imaging Parameter: Tumor Heterogeneity Index

<b><i>Background and Aims:</i></b> The aim of this study was to evaluate if the tumor heterogeneity index can predict the aggressiveness of prostate cancer (PCa) in patients diagnosed by magnetic resonance imaging (MRI) fusion biopsy. <b><i>Material and Methods:</i></b> Patients who underwent MRI fusion prostatic biopsy between July 2019 and December 2020 were retrospectively reviewed. Tumor heterogeneity index (coefficient of variation [CV]) and PI-RADS v2.1 scoring were analyzed by using multiparametric MRI. The patients were divided into 3 groups according to the risk classification, and the correlation between tumor heterogeneity index and PCa aggressiveness was studied by using apparent diffusion coefficient (ADC_mean and ADC_cv), Gleason score (GS), and risk classifications. <b><i>Results:</i></b> One hundred two patients were included in this study. Patients were evaluated as low-risk (group 1) (<i>n</i> = 35), moderate-risk (group 2) (<i>n</i> = 37), and high-risk (group 3) (<i>n</i> = 30). ADC_mean values for all groups were significantly different (<i>p</i> &#x3c; 0.0001). ADC_cv tumor heterogeneity index values were higher in group 2 and group 3 by the score increases in subgroups according to GS, while being higher than group 1 (<i>p</i> &#x3c; 0.001). The multivariate analysis revealed that prostate-specific antigen, PI-RADS, ADC_mean, and ADC_cv values were predictive for tumor aggressiveness. <b><i>Conclusion:</i></b> ADC_cv value as a tissue texture parameter can be used as a new biomarker to evaluate tumor aggressiveness in patients with PCa.

Magnetic resonance imaging/transrectal ultrasonography fusion guided seed placement in a phantom: Accuracy between 2-seed versus 1-seed strategies

PURPOSE

To investigate whether the 2-seed placement per Magnetic Resonance Imaging (MRI) suspicious lesion yields a higher seed placement accuracy than a 1-seed strategy on a phantom.

METHODS

Eight olives embedded in gelatin, each simulating a prostate, underwent MRI. Three virtual spherical lesions (3, 5, and 8 mm diameters) were marked in each olive on the MRI images and co-registered to the MRI/Transrectal Ultrasonography (TRUS) fusion biopsy system. Two radiologists placed 0.5 mm fiducials, targeting the center of each virtual lesion under fusion image guidance. Half of the 8 olives in each phantom were assigned either to the 1-seed or 2-seeds per lesion strategy. Post-procedure Computed Tomography (CT) images identified each seed and were fused with MR to localize each virtual lesion and collected the seed placement error - distance between the virtual target and the corresponding seed (using the closer seed for the 2-seed strategy). Seed placement success is defined as fiducial placement within a lesion boundary.

RESULTS

Each operator repeated the procedure on three different phantoms, and data from 209 seeds placed for 137 lesions were analyzed, with an overall error of 3.03 ± 1.52 mm. The operator skill, operator phantom procedural experience, lesion size, and number of seeds, were independently associated with the seed placement error. Seed placement success rate was higher for the 2-seed group compared to 1-seed, although the difference was not statistically significant.

CONCLUSIONS

Placing 2 seeds per MRI lesion yielded a significantly lower error compared to 1-seed strategy, although seed placement success rate was not significantly different.

Comparison of double inversion recovery magnetic resonance imaging (DIR-MRI) and dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) in detection of prostate cancer: A pilot study

INTRODUCTION

DCE-MRI is established for detecting prostate cancer (PCa). However, it requires a gadolinium contrast agent, with potential risks for patients. The application of DIR-MRI is simple and may allow cancer detection without the use of an intravenous contrast agent by differentially nullifying signal from normal and abnormal prostate tissue, creating contrast between the cancer and background normal prostate. In this pilot study we gathered data from DIR-MRI and DCE-MRI of the prostate for an equivalence trial. We also looked at how the DIR-MRI appearance varies with the aggressiveness of PCa.

METHOD

DIR-MRI and DCE-MRI were acquired. The images were assessed by an experienced Consultant Radiologist and a novice reporter (Radiographer). The potential PCa lesions were quantified using a lesion to normal ratio (LNR). Radiological pathological correlation was made to identify the MRI lesions that represented significant PCa. A Wilcoxon sign rank was used to compare DCE-LNR and DIR-LNR for PCa containing lesions. Pearson's correlation was used to look at the relationship between DIR-LNR and PCa grade group (aggressiveness).

RESULTS

DCE-LNR and DIR-LNR were found to be significantly different (Z = -5.910, p < 0.001). However, a significant correlation was found between PCa grade group and DIR-LNR.

CONCLUSION

DIR and DCE sequences are not equivalent and significant cancer is more conspicuous on the DCE sequence. However, DIR-LNR does correlate with PCa aggressiveness.

IMPLICATIONS FOR PRACTICE

With the correlation of PCa grade group with DIR-LNR this may be a useful sequence in evaluation of the prostate; stratifying the risk of there being clinically significant PCa before biopsy is performed. Furthermore, given that DIR-LNR appears to predict PCa aggressiveness DIR might be used as part of a multiparametric MRI protocol designed to avoid biopsy.

Using the prostate imaging reporting and data system version 2 (PI-RIDS v2) to detect prostate cancer can prevent unnecessary biopsies and invasive treatment

Prostate cancer (PCa) is one of the most common cancers among men globally. The authors aimed to evaluate the ability of the Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) to classify men with PCa, clinically significant PCa (CSPCa), or no PCa, especially among those with serum total prostate-specific antigen (tPSA) levels in the “gray zone” (4–10 ng ml⁻¹). A total of 308 patients (355 lesions) were enrolled in this study. Diagnostic efficiency was determined. Univariate and multivariate analyses, receiver operating characteristic curve analysis, and decision curve analysis were performed to determine and compare the predictors of PCa and CSPCa. The results suggested that PI-RADS v2, tPSA, and prostate-specific antigen density (PSAD) were independent predictors of PCa and CSPCa. A PI-RADS v2 score ≥4 provided high negative predictive values (91.39% for PCa and 95.69% for CSPCa). A model of PI-RADS combined with PSA and PSAD helped to define a high-risk group (PI-RADS score = 5 and PSAD ≥0.15 ng ml^-1 cm^-3, with tPSA in the gray zone, or PI-RADS score ≥4 with high tPSA level) with a detection rate of 96.1% for PCa and 93.0% for CSPCa while a low-risk group with a detection rate of 6.1% for PCa and 2.2% for CSPCa. It was concluded that the PI-RADS v2 could be used as a reliable and independent predictor of PCa and CSPCa. The combination of PI-RADS v2 score with PSA and PSAD could be helpful in the prediction and diagnosis of PCa and CSPCa and, thus, may help in preventing unnecessary invasive procedures.

Correlation between ADC values and Gleason score in evaluation of prostate cancer: multicentre experience and review of the literature

Prostate cancer (PCa) is one of the most common cancers in male population. Multiparametric prostate magnetic resonance imaging (mp-MRI) has assumed a primary role in the diagnosis of PCa, combining morphological and functional data. Among different sequences, functional diffusion weighted imaging (DWI) is a powerful clinical tool which provides information about tissue on a cellular level. However, there is a considerable overlap between either BPH (Benign Prostate Hypertrophy) and prostatic cancer condition, as a different DWI signal intensity could be shown in the normal architecture gland. Apparent diffusion coefficient (ADC) has shown an increasing accuracy in addition to the DWI analysis in detection and localization of PCa. Notably, ADC maps derived DWI sequences has shown an overall high correlation with Gleason score (GS), considering the importance of an accurate grading of focal lesion, as main predictor factor. Furthermore, beyond the comparative analysis with DWI, ADC values has proven to be an useful marker of tumor aggressiveness, providing quantitative information on tumor characteristics according with GS and Gleason pattern, even more strenuous data are needed in order to verify which ADC analysis is more accurate.