Diagnostic performance of diffusion-weighted imaging for prostate cancer: Peripheral zone versus transition zone

Meeting Cancer Detection Benchmarks in MRI/Ultrasound Fusion Biopsy for Prostate Cancer: Insights from a Retrospective Analysis of Experienced Urologists

BACKGROUND

The relationship between case volume and clinical outcomes is well established for most urological procedures but remains underexplored in prostate ultrasound/MRI fusion biopsy (UMFB). UMFB aims to detect clinically significant prostate cancer (csPCa) by adhering to cancer detection benchmarks for PI-RADS lesions identified via multiparametric MRI (mpMRI). These benchmarks, defined by Ahmed et al., include cumulative cancer detection rate (C-CDR) targets of >80% for PI-RADS 5, >50% for PI-RADS 4, and <20% for PI-RADS 1-3.

METHODS

This retrospective, single-center study analyzed the case volumes required for two experienced urologists (U1 and U2, each with >15 years of practice) to consistently achieve the Ahmed-defined C-CDR benchmarks for csPCa (ISUP grade ≥ 2) using UMFB. Both transrectal and transperineal approaches were included to enable comprehensive learning curve analysis. Data from 2017 to 2023 were reviewed, encompassing 157 UMFBs performed by U1 and 242 by U2, with a transrectal-to-perineal ratio of 7:3.

RESULTS

Both urologists achieved Ahmed-defined C-CDR targets from the outset. Over a median follow-up of 30 months, patients with initial PI-RADS 4 or 5 ratings and negative primary biopsies remained prostate cancer-free in 77% of cases for U1 and 91.2% for U2 (p = 0.152).

CONCLUSIONS

This study demonstrates that experienced urologists can achieve high diagnostic accuracy and maintain patient safety immediately upon implementing UMFB, meeting established benchmarks without requiring additional procedural learning.

Addressing intra- and inter-institution variability of a radiomic framework based on Apparent Diffusion Coefficient in prostate cancer

BACKGROUND

Prostate cancer (PCa) is a highly heterogeneous disease, making tailored treatment approaches challenging. Magnetic resonance imaging (MRI), notably diffusion-weighted imaging (DWI) and the derived Apparent Diffusion Coefficient (ADC) maps, plays a crucial role in PCa characterization. In this context, radiomics is a very promising approach able to disclose insights from MRI data. However, the sensitivity of radiomic features to MRI settings, encompassing DWI protocols and multicenter variations, requires the development of robust and generalizable models.

PURPOSE

To develop a comprehensive radiomics framework for noninvasive PCa characterization using ADC maps, focusing on identifying reliable imaging biomarkers against intra- and inter-institution variations.

MATERIALS AND METHODS

Two patient cohorts, including an internal cohort (118 PCa patients) used for both training (75%) and hold-out testing (25%), and an external cohort (50 PCa patients) for independent testing, were employed in the study. DWI images were acquired with three different DWI protocols on two different MRI scanners: two DWI protocols acquired on a 1.5-T scanner for the internal cohort, and one DWI protocol acquired on a 3-T scanner for the external cohort. One hundred and seven radiomics features (i.e., shape, first order, texture) were extracted from ADC maps of the whole prostate gland. To address variations in DWI protocols and multicenter variability, a dedicated pipeline, including two-way ANOVA, sequential-feature-selection (SFS), and ComBat features harmonization was implemented. Mann-Whitney U-tests (α = 0.05) were performed to find statistically significant features dividing patients with different tumor characteristics in terms of Gleason score (GS) and T-stage. Support-Vector-Machine models were then developed to predict GS and T-stage, and the performance was assessed through the area under the curve (AUC) of receiver-operating-characteristic curves.

RESULTS

Downstream of ANOVA, two subsets of 38 and 41 features stable against DWI protocol were identified for GS and T-stage, respectively. Among these, SFS revealed the most predictive features, yielding an AUC of 0.75 (GS) and 0.70 (T-stage) in the hold-out test. Employing ComBat harmonization improved the external-test performance of the GS model, raising AUC from 0.72 to 0.78.

CONCLUSION

By incorporating stable features with a harmonization procedure and validating the model on an external dataset, model robustness, and generalizability were assessed, highlighting the potential of ADC and radiomics for PCa characterization.

Comparison of diagnostic performance between diffusion models parameters and mono-exponential apparent diffusion coefficient in patients with prostate cancer: A systematic review and meta-analysis

Background

The importance of diffusion in prostate cancer (PCa) diagnosis has been widely proven. Several studies investigated diffusion models in PCa diagnosis.

Materials and Methods

This systematic review and meta-analysis study was performed to evaluate the ability of three diffusion models to diagnose PCa from the scientific electronic databases Embase, PubMed, Scopus, and Web of Science (ISI) for the period up to March 2022 to identify all relevant articles.

Results

Eighteen studies were included in the systematic review section (7 diffusion kurtosis imaging [DKI] studies, 4 diffusion tensor imaging [DTI] studies, 4 intravoxel incoherent motion [IVIM] studies, and 3 IVIM-DKI studies). Pooled sensitivity, specificity, accuracy, and summary area under each diffusion model's curve (AUC) and 95% confidence intervals (CIs) were calculated. The pooled accuracy and 95% CI on detection (differentiation of tumor from normal tissue and benign prostatic hyperplasia/prostatitis) were obtained for apparent diffusion coefficient (ADC) at 87.97% (84.56%-91.38%) for DKI parameters (Gaussian diffusion [DK] 87.94% [78.71%-97.16%] and deviation from Gaussian diffusion [K] 86.84% [81.83%-91.85%]) and IVIM parameters (true molecular diffusion [DIVIM] 81.73% [72.54%-90.91%], perfusion-related diffusion [D*] 65% [48.47%-81.53%] and perfusion fraction [f] 80.36% [64.23%-96.48%]). The AUC values and 95% CI in the detection of PCa were obtained for ADC at 0.95 (0.92-0.97), for DKI parameters (DK 0.94 [0.89-0.99] and K 0.93 [0.90-0.96]) and for IVIM parameters (DIVIM 0.85 [0.80-0.91], D* 0.60 [0.43-0.77] and f 0.73 [0.63-0.84]). Two studies showed that the DTI accuracy values were 97.34% and 85%. For IVIM-kurtosis model in PCa detection, two studies stated that the DIVIM-K and KIVIM-K accuracy values were 85% and 84.44% (the pooled accuracy; 84.64% with 95% CI 75.78%-93.50%), and 72.50% and 71.11% (the pooled accuracy, 72.10% with 95% CI 64.73%-79.48%), respectively.

Conclusion

Our findings showed that among the DKI, IVIM, and ADC parameters, it seems that ADC, Dk, DIVIM, and K are the most important, which can be used as an indicator to distinguish PCa from normal tissue. The DKI model probably has a higher ability to detect PCa from normal tissue than the IVIM model. DKI probably has the same diagnostic performance in PCa detection and grading compared to diffusion-weighted imaging and ADC.

Shearlet Transform Applied to a Prostate Cancer Radiomics Analysis on MR Images

For decades, wavelet theory has attracted interest in several fields in dealing with signals. Nowadays, it is acknowledged that it is not very suitable to face aspects of multidimensional data like singularities and this has led to the development of other mathematical tools. A recent application of wavelet theory is in radiomics, an emerging field aiming to improve diagnostic, prognostic and predictive analysis of various cancer types through the analysis of features extracted from medical images. In this paper, for a radiomics study of prostate cancer with magnetic resonance (MR) images, we apply a similar but more sophisticated tool, namely the shearlet transform which, in contrast to the wavelet transform, allows us to examine variations along more orientations. In particular, we conduct a parallel radiomics analysis based on the two different transformations and highlight a better performance (evaluated in terms of statistical measures) in the use of the shearlet transform (in absolute value). The results achieved suggest taking the shearlet transform into consideration for radiomics studies in other contexts.

Prostate MRI for the detection of clinically significant prostate cancer: Update and future directions

PURPOSE OF REVIEW

In recent decades, there has been an increasing role for magnetic resonance imaging (MRI) in the detection of clinically significant prostate cancer (csPC). The purpose of this review is to provide an update and outline future directions for the role of MRI in the detection of csPC.

RECENT FINDINGS

In diagnosing clinically significant prostate cancer pre-biopsy, advances include our understanding of MRI-targeted biopsy, the role of biparametric MRI (non-contrast) and changing indications, for example the role of MRI in screening for prostate cancer. Furthermore, the role of MRI in identifying csPC is maturing, with emphasis on standardization of MRI reporting in active surveillance (PRECISE), clinical staging (EPE grading, MET-RADS-P) and recurrent disease (PI-RR, PI-FAB). Future directions of prostate MRI in detecting csPC include quality improvement, artificial intelligence and radiomics, positron emission tomography (PET)/MRI and MRI-directed therapy.

SUMMARY

The utility of MRI in detecting csPC has been demonstrated in many clinical scenarios, initially from simply diagnosing csPC pre-biopsy, now to screening, active surveillance, clinical staging, and detection of recurrent disease. Continued efforts should be undertaken not only to emphasize the reporting of prostate MRI quality, but to standardize reporting according to the appropriate clinical setting.

Dynamic Contrast-Enhanced Study in the mpMRI of the Prostate-Unnecessary or Underutilised? A Narrative Review

The aim of this review is to summarise recent scientific literature regarding the clinical use of DCE-MRI as a component of multiparametric resonance imaging of the prostate. This review presents the principles of DCE-MRI acquisition and analysis, the current role of DCE-MRI in clinical practice with special regard to its role in presently available categorisation systems, and an overview of the advantages and disadvantages of DCE-MRI described in the current literature. DCE-MRI is an important functional sequence that requires intravenous administration of a gadolinium-based contrast agent and gives information regarding the vascularity and capillary permeability of the lesion. Although numerous studies have confirmed that DCE-MRI has great potential in the diagnosis and monitoring of prostate cancer, its role is still inadequate in the PI-RADS categorisation. Moreover, there have been numerous scientific discussions about abandoning the intravenous application of gadolinium-based contrast as a routine part of MRI examination of the prostate. In this review, we summarised the recent literature on the advantages and disadvantages of DCE-MRI, focusing on an overview of currently available data on bpMRI and mpMRI, as well as on studies providing information on the potential better usability of DCE-MRI in improving the sensitivity and specificity of mpMRI examinations of the prostate.

The Impact of Prostate Volume on the Prostate Imaging and Reporting Data System (PI-RADS) in a Real-World Setting

Multiparametric magnetic resonance imaging (mpMRI) has emerged as a new cornerstone in the diagnostic pathway of prostate cancer. However, mpMRI is not devoid of factors influencing its detection rate of clinically significant prostate cancer (csPCa). Amongst others, prostate volume has been demonstrated to influence the detection rates of csPCa. Particularly, increasing volume has been linked to a reduced cancer detection rate. However, information about the linkage between PI-RADS, prostate volume and detection rate is relatively sparse. Therefore, the current study aims to assess the association between prostate volume, PI-RADS score and detection rate of csP-Ca, representing daily practice and contemporary mpMRI expertise. Thus, 1039 consecutive patients with 1151 PI-RADS targets, who underwent mpMRI-guided prostate biopsy at our tertiary referral center, were included. Prior mpMRI had been assessed by a plethora of 111 radiology offices, including academic centers and private practices. mpMRI was not secondarily reviewed in house before biopsy. mpMRI-targeted biopsy was performed by a small group of a total of ten urologists, who had performed at least 100 previous biopsies. Using ROC analysis, we defined cut-off values of prostate volume for each PI-RADS score, where the detection rate drops significantly. For PI-RADS 4 lesions, we found a volume > 61.5 ccm significantly reduced the cancer detection rate (OR 0.24; 95% CI 0.16-0.38; p < 0.001). For PI-RADS 5 lesions, we found a volume > 51.5 ccm to significantly reduce the cancer detection rate (OR 0.39; 95% CI 0.25-0.62; p < 0.001). For PI-RADS 3 lesions, none of the evaluated clinical parameters had a significant impact on the detection rate of csPCa. In conclusion, we show that enlarged prostate volume represents a major limitation in the daily practice of mpMRI-targeted biopsy. This study is the first to define exact cut-off values of prostate volume to significantly impair the validity of PI-RADS assessed in a real-world setting. Therefore, the results of mpMRI-targeted biopsy should be interpreted carefully, especially in patients with prostate volumes above our defined thresholds.

Improved diffusion-weighted imaging of the prostate: Comparison of readout-segmented and zoomed single-shot imaging

OBJECTIVES

Diffusion weighted imaging (DWI) is the most important sequence for detection and grading prostate cancer (PCa), but it is considerably prone to artifacts. New approaches like zoomed single-shot imaging (z-EPI) with advanced image processing or multi-shot readout segmentation (rs-EPI) try to improve DWI quality. This study evaluates objective and subjective image quality (IQ) of rs-EPI and z-EPI with and without advanced processing.

MATERIALS AND METHODS

Fifty-six consecutive patients (67 ± 8 years; median PSA 8.3 ng/ml) with mp-MRI performed at 3 Tesla between February and October 2019 and subsequently verified PCa by targeted plus systematic MRI/US-fusion biopsy were included in this retrospective single center cohort study. Rs-EPI and z-EPI were prospectively acquired in every patient. Signal intensities (SI) of PCa and benign tissue in ADC, b1000, and calculated high b-value images were analyzed. Endpoints were signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), PCa contrast intensity (CI), and subjective IQ on a 5-point scale evaluated by three blinded readers. Wilcoxon signed rank test, Friedman test and Cohen's kappa coefficient was calculated.

RESULTS

SNR, CNR, and PCa CI of z-EPI with and without advanced processing was superior to rs-EPI (p < 0.01), whereas no significant differences were observed between z-EPI with and without advanced processing. Subjective IQ was significantly higher for z-EPI with advanced processing compared rs-EPI for ADC, b1000, and calculated high b-values (p < 0.01). Compared to z-EPI without advanced processing, z-EPI with advanced processing was superior for ADC and calculated high b-values (p < 0.01), but no significant differences were shown for b1000 images.

CONCLUSIONS

Z-EPI with and without advanced processing was superior to rs-EPI regarding objective imaging parameters and z-EPI with advanced processing was superior to rs-EPI regarding subjective imaging parameters for the detection of PCa.

Tumor Area Highlighting Using T2WI, ADC Map, and DWI Sequence Fusion on bpMRI Images for Better Prostate Cancer Diagnosis

Prostate cancer is the second most common cancer in men worldwide. The results obtained in magnetic resonance imaging examinations are used to decide the indication, type, and location of a prostate biopsy and contribute information about the characterization or aggressiveness of detected cancers, including tumor progression over time. This study proposes a method to highlight prostate lesions with a high and very high risk of being malignant by overlaying a T2-weighted image, apparent diffusion coefficient map, and diffusion-weighted image sequences using 204 pairs of slices from 80 examined patients. It was reviewed by two radiologists who segmented suspicious lesions and labeled them according to the prostate imaging-reporting and data system (PI-RADS) score. Both radiologists found the algorithm to be useful as a “first opinion”, and they gave an average score on the quality of the highlight of 9.2 and 9.3, with an agreement of 0.96.

Impact of dynamic contrast-enhanced MRI in 1.5 T versus 3 T MRI for clinically significant prostate cancer detection

PURPOSE

This study analyzes the value of dynamic contrast-enhanced MRI (DCE) of the prostate on 1.5 T and 3 T examinations in patients within PI-RADS category 4.

METHODS

In this retrospective, bi-centric, cohort study all consecutive patients classified as PI-RADS 4 in mpMRI with 100 verified prostate cancers (PCa) in subsequent MRI/US-guided fusion biopsy were included for 1.5 T and 3 T, each. PCa detection in index lesions (IL) upgraded to PI-RADS 4 based on positive DCE findings was compared between 1.5 T and 3 T. Secondary objectives are subgroup analysis of PZ lesions and comparison of ISUP grade group distribution between 1.5 T and 3 T.

RESULTS

In total, 293 patients within PI-RADS category 4, including 152 (mean 66 ± 8y; median PSA 6.4 ng/ml;116 PZ IL) in the 1.5 T group and 141 (mean 65 ± 8y; median PSA 7.2 ng/ml;100 PZ IL) in the 3 T group were included. Overall amount of PCa (66 % vs 71 %; p = 0.346) and portion of upgraded IL (28 % vs 21 %; p = 0.126) did not differ significantly. At 1.5 T PCa detection was higher in upgraded PZ lesions compared to 3 T (23 % vs 14 %; p = 0.048). The amount of upgraded PZ lesions with ISUP grade group 2-5 PCa was significantly higher at 1.5 T versus 3 T (13.8 % vs 4.0 %; p = 0.007). 33 % (11/33; 1.5 T) and 32 % (10/31; 3 T) of the ISUP grade group 1 PCa of the PZ lesions were detected in upgraded lesions (10% of all PZ index lesions, respectively).

CONCLUSION

DCE enabled the detection of a substantial amount of additional clinically significant PCa in prostate mpMRI at 1.5 T. The effect was smaller at 3 T and was accompanied in relation to 1.5 T by higher risk of overdiagnosis due to detection of additional low-risk PCa.

Variability in contrast and apparent diffusion coefficient of kiwifruit used as prostate MRI phantom: 1-week validation

Kiwifruit has been proposed as a phantom for prostate magnetic resonance imaging (MRI) to adjust multiparametric MRI factors. This study evaluated the variability in contrasts and apparent diffusion coefficients (ADCs) via repeated scans of kiwifruits for 1 week. All scans were performed using a 3T MRI system. Six kiwifruits were prepared as phantoms. Each kiwifruit was scanned consecutively for 1 week, and T2-weighted and diffusion-weighted images were acquired. Contrasts between the peripheral placenta (PP) and central placenta (CP), and between the outer pericarp (OP) and CP were calculated. The ADCs of the PP, OP, and CP were also determined. The Friedman test with Scheffé's post hoc comparison was used to assess contrast and ADC variability over 1 week; no significant differences between the contrasts or ADCs were found. Thus, each kiwifruit phantom exhibited low variability when used as a prostate MRI phantom.

Synthetic correlated diffusion imaging hyperintensity delineates clinically significant prostate cancer

Prostate cancer (PCa) is the second most common cancer in men worldwide and the most frequently diagnosed cancer among men in more developed countries. The prognosis of PCa is excellent if detected at an early stage, making early screening crucial for detection and treatment. In recent years, a new form of diffusion magnetic resonance imaging called correlated diffusion imaging (CDI) was introduced, and preliminary results show promise as a screening tool for PCa. In the largest study of its kind, we investigate the relationship between PCa presence and a new variant of CDI we term synthetic correlated diffusion imaging (CDI\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^s$$\end{document}s), as well as its performance for PCa delineation compared to current standard MRI techniques [T2-weighted (T2w) imaging, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging] across a cohort of 200 patient cases. Statistical analyses reveal that hyperintensity in CDI\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^s$$\end{document}s is a strong indicator of PCa presence and achieves strong delineation of clinically significant cancerous tissue compared to T2w, DWI, and DCE. These results suggest that CDI\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^s$$\end{document}s hyperintensity may be a powerful biomarker for the presence of PCa, and may have a clinical impact as a diagnostic aid for improving PCa screening.

Additional Value of PET Radiomic Features for the Initial Staging of Prostate Cancer: A Systematic Review from the Literature

Simple Summary

Prostate cancer (PCa) is one of the most frequent malignancies diagnosed in men and its prognosis depends on the stage at diagnosis. Molecular imaging, namely PET/CT or PET/MRI using prostate-specific radiotracers, has gained increasing application in accurately evaluating PCa at staging, especially in cases of high-risk disease, and it is now also recommended by international guidelines. Radiomic analysis is an emerging research field with a high potential to offer non-invasive and longitudinal biomarkers for personalized medicine, and several applications have been described in oncology patients. In this review, we discuss the available evidence on the role of radiomic analysis in PCa imaging at staging, exploring two different hybrid imaging modalities, such as PET/CT and PET/MRI, and the whole spectrum of radiotracers involved.

Abstract

We performed a systematic review of the literature to provide an overview of the application of PET radiomics for the prediction of the initial staging of prostate cancer (PCa), and to discuss the additional value of radiomic features over clinical data. The most relevant databases and web sources were interrogated by using the query “prostate AND radiomic* AND PET”. English-language original articles published before July 2021 were considered. A total of 28 studies were screened for eligibility and 6 of them met the inclusion criteria and were, therefore, included for further analysis. All studies were based on human patients. The average number of patients included in the studies was 72 (range 52–101), and the average number of high-order features calculated per study was 167 (range 50–480). The radiotracers used were [68Ga]Ga-PSMA-11 (in four out of six studies), [18F]DCFPyL (one out of six studies), and [11C]Choline (one out of six studies). Considering the imaging modality, three out of six studies used a PET/CT scanner and the other half a PET/MRI tomograph. Heterogeneous results were reported regarding radiomic methods (e.g., segmentation modality) and considered features. The studies reported several predictive markers including first-, second-, and high-order features, such as “kurtosis”, “grey-level uniformity”, and “HLL wavelet mean”, respectively, as well as PET-based metabolic parameters. The strengths and weaknesses of PET radiomics in this setting of disease will be largely discussed and a critical analysis of the available data will be reported. In our review, radiomic analysis proved to add useful information for lesion detection and the prediction of tumor grading of prostatic lesions, even when they were missed at visual qualitative assessment due to their small size; furthermore, PET radiomics could play a synergistic role with the mpMRI radiomic features in lesion evaluation. The most common limitations of the studies were the small sample size, retrospective design, lack of validation on external datasets, and unavailability of univocal cut-off values for the selected radiomic features.

The significance of a high preoperative PSA level for the detection of incidental prostate cancer in LUTS patients with large prostates

PURPOSE

To evaluate the diagnostic value of a high preoperative PSA level for the detection of incidental prostate cancer (iPCa) in LUTS patients with very large prostates (> 100 cc).

METHODS

We conducted a retrospective analysis of 1125 men treated for LUTS with holmium laser enucleation of the prostate (HoLEP). Patients were stratified according to a preoperative PSA level higher (high PSA; n = 365) or lower than 10 ng/ml (low PSA; n = 760). Preoperative and histopathological parameters were compared between both cohorts. Logistic regression models were used to identify independent predictors of iPCa.

RESULTS

Demographic parameters were similar between both cohorts. The median PSA levels were 14.2 ng/ml (11.5-19.9) and 4 ng/ml (2.4-6.0). The prostate volume was significantly higher in the high PSA group (105 cc vs. 75 cc; p < 0.001). Correspondingly, the PSA density was significantly increased in the high PSA cohort compared to the low PSA cohort (0.14 vs. 0.05; p < 0.001). The overall detection rate of iPCa showed no difference between groups (9.5% vs. 9.9%). More preoperative prostate biopsies were performed in the high PSA group compared to the low PSA group (46.8% vs. 17.6%; p < 0.001). However, the rate of false negative results was comparable between groups (12.7% vs. 11.1%; p = 0.726). In logistic regression models all PSA-related parameters failed to predict iPCa.

CONCLUSIONS

PSA-guided approaches to predict iPCa in LUTS patients with very large prostates are not accurate. This finding is useful in clinical practice for counselling our patients and to prevent unwarranted diagnostic procedures.

Radiomics and Prostate MRI: Current Role and Future Applications

Multiparametric prostate magnetic resonance imaging (mpMRI) is widely used as a triage test for men at a risk of prostate cancer. However, the traditional role of mpMRI was confined to prostate cancer staging. Radiomics is the quantitative extraction and analysis of minable data from medical images; it is emerging as a promising tool to detect and categorize prostate lesions. In this paper we review the role of radiomics applied to prostate mpMRI in detection and localization of prostate cancer, prediction of Gleason score and PI-RADS classification, prediction of extracapsular extension and of biochemical recurrence. We also provide a future perspective of artificial intelligence (machine learning and deep learning) applied to the field of prostate cancer.

Differentiation of prostate cancer and benign prostatic hyperplasia: comparisons of the histogram analysis of intravoxel incoherent motion and monoexponential model with in-bore MR-guided biopsy as pathological reference

PURPOSE

To evaluate the diagnostic performance of histogram analysis of intravoxel incoherent motion (IVIM) parameters for differentiating prostate cancer (PCa) from benign prostatic hyperplasia (BPH), and compare with the monoexponential model, with in-bore MR-guided biopsy as pathological reference.

METHODS

Thirty patients were included in this study. DWI images were processed with Matlab R2015b software by IVIM and monoexponential model for quantitation of diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f), and apparent diffusion coefficient (ADC). The multiparametric data were compared between PCa and BPH group. Correlations between parameters and Gleason scores of PCa were assessed with Spearman rank test. ROC analysis was used to evaluate and compare the diagnostic ability of each parameter for discriminating PCa from BPH. Logistic regression model was used to evaluate the diagnostic performance of combination of different histogram parameters.

RESULTS

Sixteen PCa lesions and 20 BPH nodules were analyzed in this study. For IVIM-derived D, the histogram mean, 75th, 90th, and max of PCa were significantly lower than BPH. PCa had significantly lower min and 10th D* than BPH. For f, histogram mean, min, 10th, 25th, 50th, 75th, 90th, max and skew showed significant differences between PCa and BPH. For ADC, PCa were significantly lower than BPH in terms of histogram mean, min, 10th, 25th, 50th, 75th, 90th, max and kurtosis. Histogram mean D and min, 25th D* show significantly negative correlation with Gleason score (r = - 0.582, - 0.534, - 0.554, respectively). Histogram max D and mean f and min ADC showed higher diagnostic performance than other parameters (AUC = 0.925, 0.881, 0.969, respectively). The IVIM model (combined with max D, min D* and mean f) (AUC = 0.950 [0.821, 0.995]) didn't show significant difference from the monoexponential model (AUC = 0.969 [0.849, 0.999], p = 0.23). Besides, combination of the IVIM and monoexponential model didn't improve diagnostic performance compared with the single model (p = 0.362 and 0.763, respectively).

CONCLUSIONS

Histogram analyses of IVIM and monoexponential model were both useful methods for discriminating PCa from BPH. The diagnostic performance of IVIM model seemed to be not superior to that of monoexponential model. Combination of IVIM and monoexponential model did not add significant information to the single model alone.

Diffusion processes modeling in magnetic resonance imaging

Background

The paper covers modern approaches to the evaluation of neoplastic processes with diffusion-weighted imaging (DWI) and proposes a physical model for monitoring the primary quantitative parameters of DWI and quality assurance. Models of hindered and restricted diffusion are studied.

Material and method

To simulate hindered diffusion, we used aqueous solutions of polyvinylpyrrolidone with concentrations of 0 to 70%. We created siloxane-based water-in-oil emulsions that simulate restricted diffusion in the intracellular space. To obtain a high signal on DWI in the broadest range of b values, we used silicon oil with high T₂: cyclomethicone and caprylyl methicone. For quantitative assessment of our phantom, we performed DWI on 1.5T magnetic resonance scanner with various fat suppression techniques. We assessed water-in-oil emulsion as an extracorporeal source signal by simultaneously scanning a patient in whole-body DWI sequence.

Results

We developed phantom with control substances for apparent diffusion coefficient (ADC) measurements ranging from normal tissue to benign and malignant lesions: from 2.29 to 0.28 mm²/s. The ADC values of polymer solutions are well relevant to the mono-exponential equation with the mean relative difference of 0.91%.

Conclusion

The phantom can be used to assess the accuracy of the ADC measurements, as well as the effectiveness of fat suppression. The control substances (emulsions) can be used as a body marker for quality assurance in whole-body DWI with a wide range of b values.

Comparing Prostate Imaging-Reporting and Data System Version 2 (PI-RADSv2) Category 1 and 2 Groups: Clinical Implication of Negative Multiparametric Magnetic Resonance Imaging

Objectives

To evaluate the clinicopathological differences between Prostate Imaging-Reporting and Data System (PI-RADS) version 2 (v2) category 1 and 2 groups. Materials and Methods. We retrospectively reviewed our two institutional clinical databases: (1) transrectal ultrasound (TRUS)/magnetic resonance imaging (MRI) fusion biopsy cohort (n = 706) and (2) radical prostatectomy (RP) cohort (n = 1403). Subsequently, we performed comparative analyses between PI-RADSv2 category 1 and 2 groups. Clinically significant prostate cancer (csPCa) was defined as the presence of Gleason score (GS) ≥ 3 + 4 in a single biopsy core, and adverse pathology (AP) was defined as high-grade (primary Gleason pattern 4 or any pattern 5) and/or non-organ-confined disease (pT3/N1). We also performed multivariate logistic regression analyses for AP.

Results

In the TRUS/MRI fusion biopsy cohort, no significant differences in detection rates of all cancer (18.2% vs. 29.0%, respectively, P = 0.730) or csPCa (9.1% vs. 9.9%, respectively, P = 0.692) were observed between PI-RADSv2 category 1 and 2 groups. There were no significant differences in pathologic outcomes including Gleason score (≥4 + 3, 21.2% vs. 29.9%, respectively, P = 0.420) or detection rate of AP (27.3% vs. 33.8%, respectively, P = 0.561) between the two groups in the RP cohort either. PI-RADSv2 category 1 or 2 had no significant association with AP, even in univariate analysis (P = 0.299).

Conclusions

PI-RADSv2 categories 1 and 2 had similar performance to predict clinicopathological outcomes. Consequently, these two categories may be unified into a single category. Negative mpMRI does not guarantee the absence of AP, as with csPCa.

Prognostic value of seminal vesicle invasion on preoperative multi-parametric magnetic resonance imaging in pathological stage T3b prostate cancer

We aimed to evaluate the prognostic value of seminal vesicle invasion (SVI) on preoperative multiparametric MRI (mpMRI) in pathological T3b prostate cancer (PCa). We retrospectively reviewed the clinical data of patients who underwent preoperative mpMRI and subsequent radical prostatectomy (RP). A total of 159 patients with pathologic T3b PCa were stratified into two groups based on mpMRI findings (negative vs. positive SVI). A positive SVI was defined as the presence of mpMRI evidence of SVI. In addition, 290 patients with pathologic T3a were also included in this study for further comparative analysis. Fifty-two patients (32.7%) had a positive SVI on preoperative mpMRI. Biochemical recurrence (BCR) occurred in a total of 45 (28.3%) patients, with 25 (23.4%) cases in the negative SVI group and 20 (38.5%) cases in the positive SVI group. Kaplan-Meier survival analysis of the two groups revealed significantly decreased BCR-free survival in the positive SVI group (median, 21 vs. 9 months, log-rank test, P < 0.001). On multivariate Cox regression analysis, pre-biopsy PSA (P = 0.035) and positive SVI on preoperative mpMRI (P = 0.049) were identified as significant predictors of BCR. Upon further comparative analysis with the pathologic T3a group, we also found significant differences among the groups throughout the Kaplan-Meier curve (P < 0.001). Conclusively, the unpredicted (negative) SVI group had a favorable BCR-free survival compared to the positive SVI group. In addition, significant differences were observed in the prognosis of pathologic T3a and these two groups. This suggests that pathologic T3b can be stratified into two categories.

Validation of the PI-RADS language: predictive values of PI-RADS lexicon descriptors for detection of prostate cancer

OBJECTIVES

To assess the discriminatory power of lexicon terms used in PI-RADS version 2 to describe MRI features of prostate lesions.

METHODS

Four hundred fifty-four patients were included in this retrospective, institutional review board-approved study. Patients received multiparametric (mp) MRI and subsequent prostate biopsy including MRI/transrectal ultrasound fusion biopsy and 10-core systematic biopsy. PI-RADS lexicon terms describing lesion characteristics on mpMRI were assigned to lesions by experienced readers. Positive and negative predictive values (PPV, NPV) of each lexicon term were assessed using biopsy results as a reference standard.

RESULTS

From a total of 501 lesions, clinically significant prostate cancer (csPCa) was present in 175 lesions (34.9%). Terms related to findings of restricted diffusion showed PPVs of up to 52.0%/43.9% and NPV of up to 91.8%/89.7% (peripheral zone or PZ/transition zone or TZ). T2-weighted imaging (T2W)-related terms showed a wide range of predictive values. For PZ lesions, high PPVs were found for "markedly hypointense," "lenticular," "lobulated," and "spiculated" (PPVs between 67.2 and 56.7%). For TZ lesions, high PPVs were found for "water-drop-shaped" and "erased charcoal sign" (78.6% and 61.0%). The terms "encapsulated," "organized chaos," and "linear" showed to be good predictors for benignity with distinctively low PPVs between 5.4 and 6.9%. Most T2WI-related terms showed improved predictive values for TZ lesions when combined with DWI-related findings.

CONCLUSIONS

Lexicon terms with high discriminatory power were identified (e.g., "markedly hypointense," "water-drop-shaped," "organized chaos"). DWI-related terms can be useful for excluding TZ cancer. Combining T2WI- with DWI findings in TZ lesions markedly improved predictive values.

KEY POINTS

• Lexicon terms describing morphological and functional features of prostate lesions on MRI show a wide range of predictive values for prostate cancer. • Some T2-related terms have favorable PPVs, e.g., "water-drop-shaped" and "organized chaos" while others show less distinctive predictive values. DWI-related terms have noticeable negative predictive values in TZ lesions making DWI feature a useful tool for exclusion of TZ cancer. • Combining DWI- and T2-related lexicon terms for assessment of TZ lesions markedly improves PPVs. Most T2-related lexicon terms showed a significant decrease in PPV when combined with negative findings for "DW hyperintensity."

The effect of 5 alpha-reductase inhibitor therapy on prostate cancer detection in the era of multi-parametric magnetic resonance imaging

We aimed to evaluate the effect of 5 alpha-reductase inhibitor (5-ARI) treatment on prostate cancer (PCa) and clinically significant PCa (csPCa) detection in patients undergoing transrectal ultrasound (TRUS)/magnetic resonance imaging (MRI) fusion biopsy in the current era of multi-parametric MRI (mpMRI). We retrospectively reviewed our TRUS/MRI fusion biopsy database (n = 706). Eighty (11.3%) patients who had used 5-ARI for more than one year at the time of biopsy were stratified as 5-ARI group. Subsequently, we performed comparative analyses of 5-ARI and non-5-ARI groups. csPCa was defined by a Gleason score ≥3 + 4 in a single biopsy core. Chi-squared test was used to evaluate the performance of mpMRI in predicting PCa/csPCa between the two groups. Multivariate logistic regression analyses were performed to evaluate the significant variables associated with PCa detection. There were no significant differences in PCa/csPCa detection rates between 5-ARI and non-5-ARI groups (all, P > 0.05). In multivariate logistic regression analyses for the evaluation of variables associated with csPCa detection, age (odds ratio [OR], 1.062; 95% confidence interval [CI], 1.035–1.090; P < 0.001), pre-biopsy PSA (OR, 1.062; 95% CI, 1.034–1.090; P < 0.001), prostate volume on TRUS (OR, 0.956; 95% CI, 0.943–0.970, P < 0.001), and PI-RADsV2 category (OR, 5.528; 95% CI, 3.017–10.131; P < 0.001) were found to be significant predictors. However, 5-ARI had no significant association with PCa detection (P = 0.384). Conclusively, 5-ARI therapy did not adversely affect PCa/csPCa detection after TRUS/MRI fusion biopsy, which suggests that exposure to 5-ARI may not impair the performance of mpMRI.

Accuracy of the preoperative PSA level for predicting clinically significant incidental transitional zone-prostate cancer before endoscopic enucleation of very large adenoma

OBJECTIVE

To analyse the accuracy of high preoperative PSA levels for predicting transitional zone incidental PCa (TZ-PCa) in men with very large prostates.

MATERIALS AND METHODS

Perioperative data from 375 consecutive patients who underwent endoscopic enucleation of the prostate (EEP) for benign prostatic obstruction between July 2013 and December 2018 were retrospectively reviewed. Patients were stratified into three groups according to the preoperative PSA level: low-PSA (< 4 ng/mL), intermediate-PSA (4 ≤ PSA < 10 ng/mL) and high-PSA (≥ 10 ng/mL). Men in each group were propensity score matched by age, 5α-reductase inhibitor (5-ARI) use, prostate volume and mpMRI. The TZ-PCa incidence rate was retrospectively compared by preoperative PSA level in a propensity score model including all predetermined variables.

RESULTS

Age, prostate volume, 5-ARI use were similar between patient groups. The median PSA levels in the low-, intermediate- and high-PSA groups were 3 [2.3; 3.4], 6.6 [5.3; 8.1] and 12.7 [11; 16.7] ng/mL, respectively. The median prostate volume was > 100 grams in all groups (108, 105 and 120 cc, respectively). The T1a-Gleason 6 incidental TZ-PCa rate was statistically comparable between the three groups (3.4, 5.1 and 8.6% in the low-, intermediate- and high-PSA groups, respectively). The detection rate of clinically significant TZ-PCa was low for preoperative PSA levels > 4 ng/mL (1.7%); with no difference between the intermediate- and high-PSA groups.

CONCLUSION

In men with large glands, the clinically significant incidental TZ-PCa detection rate was similar regardless of the preoperative PSA level stratum. Such details may help with patient counselling during BPH surgical management.

Utility of Restriction Spectrum Imaging Among Men Undergoing First-Time Biopsy for Suspected Prostate Cancer

OBJECTIVE. The purpose of this article is to evaluate restriction spectrum imaging (RSI) in men undergoing MRI-ultrasound fusion biopsy for suspected prostate cancer (PCa) and to compare the performance of RSI with that of conventional DWI. MATERIALS AND METHODS. One hundred ninety-eight biopsy-naïve men enrolled in a concurrent prospective clinical trial evaluating MRI-targeted prostate biopsy underwent multiparametric MRI with RSI. Clinical and imaging features were compared between men with and without clinically significant (CS) PCa (MRI-ultrasound fusion biopsy Gleason score ≥ 3 + 4). RSI z score and apparent diffusion coefficient (ADC) were correlated, and their diagnostic performances were compared. RESULTS. CS PCa was detected in 109 of 198 men (55%). Using predefined thresholds of ADC less than or equal to 1000 μm²/s and RSI z score greater than or equal to 3, sensitivity and specificity for CS PCa were 86% and 38%, respectively, for ADC and 61% and 70%, respectively, for RSI. In the transition zone (n = 69), the sensitivity and specificity were 94% and 17%, respectively, for ADC and 59% and 69%, respectively, for RSI. Among lesions with CS PCa, RSI z score and ADC were significantly inversely correlated in the peripheral zone (ρ = -0.4852; p < 0.01) but not the transition zone (ρ = -0.2412; p = 0.17). Overall diagnostic accuracies of RSI and DWI were 0.70 and 0.68, respectively (p = 0.74). CONCLUSION. RSI and DWI achieved equivalent diagnostic performance for PCa detection in a large population of men undergoing first-time prostate biopsy for suspected PCa, but RSI had superior specificity for transition zone lesions.

Diagnostic Accuracy of a Rapid Biparametric MRI Protocol for Detection of Histologically Proven Prostate Cancer

OBJECTIVE

To evaluate the performance of a rapid, low cost, noncontrast MRI examination as a secondary screening tool in detection of clinically significant prostate cancer.

METHODS

In this prospective single institution study, 129 patients with elevated prostate-specific antigen levels or abnormal digital rectal examination findings underwent MRI with an abbreviated biparamatric MRI protocol consisting of high-resolution axial T2- and diffusion-weighted images. Index lesions were classified according to modified Prostate Imaging - Reporting and Data System (mPI-RADS) version 2.0. All patients underwent standard transrectal ultrasound-guided biopsy after MRI with the urologist being blinded to MRI results. Subsequently, all patients with suspicious lesions (mPI-RADS 3, 4, or 5) underwent cognitively guided targeted biopsy after discussion of MRI results with the urologist. Sensitivity and negative predictive value for identification of clinically significant prostate cancer (Gleason score 3+4 and above) were determined.

RESULTS

Rapid biparametric MRI discovered 176 lesions identified in 129 patients. Rapid MRI detected clinically significant cancers with a sensitivity of 95.1% with a negative predictive value of 95.1% and positive predictive value of 53.2%, leading to a change in management in 10.8% of the patients. False negative rate of biparametric (bp) MRI was 4.7%.

CONCLUSION

We found that a bp-MRI examination can detect clinically significant lesions and changed patient management in 10.8% of the patients. A rapid MRI protocol can be used as a useful secondary screening tool in men presenting with suspicion of prostate cancer.