Diagnostic Accuracy and Interobserver Agreement of PI-RADS Version 2 and Version 2.1 for the Detection of Transition Zone Prostate Cancers

Diagnostic Performance of Multiparametric MRI for the Detection of suspected Prostate Cancer in Biopsy-Naive Patients: A Systematic Review and Meta-analysis

RATIONALE AND OBJECTIVES

This meta-analysis aimed to assess the diagnostic accuracy of multiparametric MRI (mpMRI) in detecting suspected prostate cancer (PCa) in biopsy-naive men.

MATERIALS AND METHODS

PubMed, Scopus, and the Cochrane Library databases were systematically searched for studies published from January 2013 to April 2024. Sixteen studies comprising 4973 patients met the inclusion criteria. Data were extracted to construct 2×2 contingency tables for sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). A random-effects model was used for pooled estimation, and subgroup analyses were conducted. Summary receiver operating characteristic (SROC) curves were generated to summarize overall diagnostic performance.

RESULTS

The overall detection rate of PCa across studies was 57.3%. For detecting any PCa, mpMRI showed pooled sensitivity of 82% (95% CI, 80-83%) and specificity of 62% (95% CI, 60-64%), with positive likelihood ratio (LR) of 1.97 (95% CI, 1.71-2.26), negative LR of 0.28 (95% CI, 0.24-0.34), and diagnostic odds ratio (DOR) of 7.34 (95% CI, 5.60-9.63), and an area under the SROC curve of 0.81. For clinically significant PCa (csPCa), mpMRI had pooled sensitivity of 88% (95% CI, 87-90%) and specificity of 64% (95% CI, 63-66%), with positive LR of 2.49 (95% CI, 2.03-3.05), negative LR of 0.20 (95% CI, 0.16-0.25), DOR of 13.83 (95% CI, 9.14-20.9), and area under the curve of 0.90.

CONCLUSION

This meta-analysis suggests that mpMRI is effective in detecting PCa in biopsy-naive patients, particularly for csPCa. It can help reduce unnecessary biopsies and lower the risk of missing clinically significant cases, thereby guiding informed biopsy decisions.

Update on PI-RADS Version 2.1 Diagnostic Performance Benchmarks for Prostate MRI: Systematic Review and Meta-Analysis

Background Prostate MRI for the detection of clinically significant prostate cancer (csPCa) is standardized by the Prostate Imaging Reporting and Data System (PI-RADS), currently in version 2.1. A systematic review and meta-analysis infrastructure with a 12-month update cycle was established to evaluate the diagnostic performance of PI-RADS over time. Purpose To provide estimates of diagnostic accuracy and cancer detection rates (CDRs) of PI-RADS version 2.1 categories for prostate MRI, which is required for further evidence-based patient management. Materials and Methods A systematic search of PubMed, Embase, Cochrane Library, and multiple trial registers (English-language studies published from March 1, 2019, to August 30, 2022) was performed. Studies that reported data on diagnostic accuracy or CDRs of PI-RADS version 2.1 with csPCa as the primary outcome were included. For the meta-analysis, pooled estimates for sensitivity, specificity, and CDRs were derived from extracted data at the lesion level and patient level. Sensitivity and specificity for PI-RADS greater than or equal to 3 and PI-RADS greater than or equal to 4 considered as test positive were investigated. In addition to individual PI-RADS categories 1-5, subgroup analyses of subcategories (ie, 2+1, 3+0) were performed. Results A total of 70 studies (11 686 lesions, 13 330 patients) were included. At the patient level, with PI-RADS greater than or equal to 3 considered positive, meta-analysis found a 96% summary sensitivity (95% CI: 95, 98) and 43% specificity (95% CI: 33, 54), with an area under the summary receiver operating characteristic (SROC) curve of 0.86 (95% CI: 0.75, 0.93). For PI-RADS greater than or equal to 4, meta-analysis found an 89% sensitivity (95% CI: 85, 92) and 66% specificity (95% CI: 58, 74), with an area under the SROC curve of 0.89 (95% CI: 0.85, 0.92). CDRs were as follows: PI-RADS 1, 6%; PI-RADS 2, 5%; PI-RADS 3, 19%; PI-RADS 4, 54%; and PI-RADS 5, 84%. The CDR was 12% (95% CI: 7, 19) for transition zone 2+1 lesions and 19% (95% CI: 12, 29) for 3+0 lesions (P = .12). Conclusion Estimates of diagnostic accuracy and CDRs for PI-RADS version 2.1 categories are provided for quality benchmarking and to guide further evidence-based patient management. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Tammisetti and Jacobs in this issue.

Exploring the potential of ex-vivo 7-T magnetic resonance imaging on patients with clinically significant prostate cancer: visibility and size perspective

Background

Despite progress in multiparametric magnetic resonance imaging (MRI), issues of prostate cancer invisibility and underestimated tumor burden persist. This study investigates the potential of an ultra-high field MRI at 7-T in an ex-vivo setting to address these limitations.

Methods

This prospective study included 54 tumors from 20 treatment-naïve clinically significant prostate cancer patients, confirmed by biopsy, despite negative findings on preoperative 3-T MRI. Ex-vivo 7-T MRI of resected prostates was performed, with assessment on tumor visibility and size. Factors influencing visibility were analyzed using logistic regression analyses.

Results

Tumor visibility was confirmed in 80% of patients, and 48% of all tumors on ex-vivo imaging. Gleason pattern 4 percentage (odds ratio 1.09) and tumor size on pathology (odds ratio 1.36) were significantly associated with visibility (P < 0.05). Mean MRI-visible and invisible tumor sizes were 10.5 mm and 5.3 mm, respectively. The size discrepancy between MRI and pathology was 2.7 mm.

Conclusion

Tumor visibility on ex-vivo 7-T MRI was influenced by tumor grade and size. The notable tumor visibility initially overlooked on 3-T MRI, along with small size discrepancy with pathology, suggests potential improvements in resolution.

Effect of Bacillus Calmette-Guerin Exposure on Prostate Cancer Detection Using Magnetic Resonance Imaging: A Cohort Study

BACKGROUND

Granulomatous prostatitis is a medical condition that may mimic prostate cancer.

PURPOSE

Granulomatous prostatitis resulting from BCG-exposure can confound the diagnosis of prostate cancer based on prostate imaging and data system (PI-RADS) classification observed on multiparametric prostate magnetic resonance imaging (mpMRI).

STUDY TYPE, POPULATION, ASSESSMENT AND STATISTICAL TESTS

A cohort study was conducted, enrolling consecutive males at risk for prostate cancer who underwent an mpMRI-targeted prostate biopsy between February 2016 and August 2023. The focus of the study was on prior BCG-exposure as adjuvant treatment for non-muscle-invasive urothelial carcinoma within the 3 years prior the magnetic resonance imaging (MRI). Exclusion criteria were a prior androgen deprivation therapy, prostate surgery or radiation, and BCG-exposure occurring more than 3 years and less than 3 months before the MRI. Chi-square, logistic-regression, statistical association, and homogeneity tests were used.

RESULTS

Total 712 patients, 899 biopsied lesions (218 PI-RADS 3, 521 PI-RADS 4 and 160 PI-RADS 5) and 20 patients with 30 lesions within the BCG-exposed cohort. Chi-square and logistic-regression tests showed an association between PI-RADS with malignancy and significant tumor (ST), considering PI-RADS3 as the reference (OR: 4.9 [95% CI, 3.4-7.1] for PI-RADS4 and OR: 21.7 [95% CI, 12.4-37.8] for PI-RADS5 for malignancy, and OR: 5.3 [95% CI, 3.2-8.7] for PI-RADS4 and OR: 16.5 [95% CI, 9.4-28.9] for PI-RADS5 regarding ST). A statistically significant negative association was demonstrated between malignancy and ST with respect to BCG-exposure (OR: 0.15 [95% CI, 0.06-0.39] and OR: 0.39 [95% CI, 0.15-1.0], respectively). Statistically significant risk-difference for malignancy in patients nonexposed to BCG regarding those exposed was 45% (61.6% vs. 16.7%) for PI-RADS4, and 68.5% (90.7% vs. 22.2%) and 42.7% (64.9% vs. 22.2%) concerning malignancy and ST for PI-RADS5, respectively.

DATA CONCLUSIONS

Granulomatous prostate reaction caused by BCG-exposure acts as confounding factor for prostate MRI interpretation. The risk of malignancy and significant tumor on targeted biopsy to PI-RADS 3, 4 and 5 is notably lower in exposed patients.

An artificial intelligence model based on transrectal ultrasound images of biopsy needle tract tissues to differentiate prostate cancer

PURPOSE

We aimed to develop an artificial intelligence (AI) model based on transrectal ultrasonography (TRUS) images of biopsy needle tract (BNT) tissues for predicting prostate cancer (PCa) and to compare the PCa diagnostic performance of the radiologist model and clinical model.

METHODS

A total of 1696 2D prostate TRUS images were involved from 142 patients between July 2021 and May 2022. The ResNet50 network model was utilized to train classification models with different input methods: original image (Whole model), BNT (Needle model), and combined image [Feature Pyramid Networks (FPN) model]. The training set, validation set, and test set were randomly assigned, then randomized 5-fold cross-validation between the training set and validation set was performed. The diagnostic effectiveness of AI models and image combination was accessed by an independent testing set. Then, the optimal AI model and image combination were selected to compare the diagnostic efficacy with that of senior radiologists and the clinical model.

RESULTS

In the test set, the area under the curve, specificity, and sensitivity of the FPN model were 0.934, 0.966, and 0.829, respectively; the diagnostic efficacy was improved compared with the Whole and Needle models, with statistically significant differences (P < 0.05), and was better than that of senior radiologists (area under the curve: 0.667). The FPN model detected more PCa compared with senior physicians (82.9% vs. 55.8%), with a 61.3% decrease in the false-positive rate and a 23.2% increase in overall accuracy (0.887 vs. 0.655).

CONCLUSION

The proposed FPN model can offer a new method for prostate tissue classification, improve the diagnostic performance, and may be a helpful tool to guide prostate biopsy.

Performance of a Region of Interest-based Algorithm in Diagnosing International Society of Urological Pathology Grade Group ≥2 Prostate Cancer on the MRI-FIRST Database-CAD-FIRST Study

BACKGROUND AND OBJECTIVE

Prostate multiparametric magnetic resonance imaging (MRI) shows high sensitivity for International Society of Urological Pathology grade group (GG) ≥2 cancers. Many artificial intelligence algorithms have shown promising results in diagnosing clinically significant prostate cancer on MRI. To assess a region-of-interest-based machine-learning algorithm aimed at characterising GG ≥2 prostate cancer on multiparametric MRI.

METHODS

The lesions targeted at biopsy in the MRI-FIRST dataset were retrospectively delineated and assessed using a previously developed algorithm. The Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) score assigned prospectively before biopsy and the algorithm score calculated retrospectively in the regions of interest were compared for diagnosing GG ≥2 cancer, using the areas under the curve (AUCs), and sensitivities and specificities calculated with predefined thresholds (PIRADSv2 scores ≥3 and ≥4; algorithm scores yielding 90% sensitivity in the training database). Ten predefined biopsy strategies were assessed retrospectively.

KEY FINDINGS AND LIMITATIONS

After excluding 19 patients, we analysed 232 patients imaged on 16 different scanners; 85 had GG ≥2 cancer at biopsy. At patient level, AUCs of the algorithm and PI-RADSv2 were 77% (95% confidence interval [CI]: 70-82) and 80% (CI: 74-85; p = 0.36), respectively. The algorithm's sensitivity and specificity were 86% (CI: 76-93) and 65% (CI: 54-73), respectively. PI-RADSv2 sensitivities and specificities were 95% (CI: 89-100) and 38% (CI: 26-47), and 89% (CI: 79-96) and 47% (CI: 35-57) for thresholds of ≥3 and ≥4, respectively. Using the PI-RADSv2 score to trigger a biopsy would have avoided 26-34% of biopsies while missing 5-11% of GG ≥2 cancers. Combining prostate-specific antigen density, the PI-RADSv2 and algorithm's scores would have avoided 44-47% of biopsies while missing 6-9% of GG ≥2 cancers. Limitations include the retrospective nature of the study and a lack of PI-RADS version 2.1 assessment.

CONCLUSIONS AND CLINICAL IMPLICATIONS

The algorithm provided robust results in the multicentre multiscanner MRI-FIRST database and could help select patients for biopsy.

PATIENT SUMMARY

An artificial intelligence-based algorithm aimed at diagnosing aggressive cancers on prostate magnetic resonance imaging showed results similar to expert human assessment in a prospectively acquired multicentre test database.

Radiomics from multisite MRI and clinical data to predict clinically significant prostate cancer

BACKGROUND

Magnetic resonance imaging (MRI) is useful in the diagnosis of clinically significant prostate cancer (csPCa). MRI-derived radiomics may support the diagnosis of csPCa.

PURPOSE

To investigate whether adding radiomics from biparametric MRI to predictive models based on clinical and MRI parameters improves the prediction of csPCa in a multisite-multivendor setting.

MATERIAL AND METHODS

Clinical information (PSA, PSA density, prostate volume, and age), MRI reviews (PI-RADS 2.1), and radiomics (histogram and texture features) were retrieved from prospectively included patients examined at different radiology departments and with different MRI systems, followed by MRI-ultrasound fusion guided biopsies of lesions PI-RADS 3-5. Predictive logistic regression models of csPCa (Gleason score ≥7) for the peripheral (PZ) and transition zone (TZ), including clinical data and PI-RADS only, and combined with radiomics, were built and compared using receiver operating characteristic (ROC) curves.

RESULTS

In total, 456 lesions in 350 patients were analyzed. In PZ and TZ, PI-RADS 4-5 and PSA density, and age in PZ, were independent predictors of csPCa in models without radiomics. In models including radiomics, PI-RADS 4-5, PSA density, age, and ADC energy were independent predictors in PZ, and PI-RADS 5, PSA density and ADC mean in TZ. Comparison of areas under the ROC curve (AUC) for the models without radiomics (PZ: AUC = 0.82, TZ: AUC = 0.80) versus with radiomics (PZ: AUC = 0.82, TZ: AUC = 0.82) showed no significant differences (PZ: P = 0.366; TZ: P = 0.171).

CONCLUSION

PSA density and PI-RADS are potent predictors of csPCa. Radiomics do not add significant information to our multisite-multivendor dataset.

Comparison of the Utility of PI-RADS 2.1, ADC Values, and Combined Use of Both, for the Diagnosis of Transition Zone Prostate Cancers

OBJECTIVE

To assess the performance of apparent diffusion coefficient (ADC; values or category) alone, Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) scoring alone, and the two in combination, to diagnose transition zone prostate cancers (PCas).

METHODS

This retrospective study included 222 patients who underwent multiparametric magnetic resonance imaging of the prostate between May 2020 and December 2022 and who had pathologically confirmed PCa or benign prostatic hyperplasia (BPH). Prostate Imaging Reporting and Data System version 2.1 and ADC (values or category) were used in the assessment of suspicious findings identified in the transition zone. The interobserver agreements for region-of-interest measurements were calculated by intraclass correlation coefficients. Logistic regression analyses were used to determine the performance of PI-RADS v2.1 alone and in combination with ADC (values or category) to diagnose PCa. Receiver operating characteristic curve and DeLong test were used to evaluate the diagnostic performance of the quantitative parameters.

RESULTS

A total of 152 patients had BPH, and 70 patients had PCa. For BPH versus PCa, the ADC values of PCa (0.64 × 10 -3 ± 0.16 × 10 -3 mm 2 /s) were significantly lower than BPH (1.06 ± 0.18 × 10 -3 mm 2 /s; P < 0.001). The PI-RADS scores for PCa (5 [interquartile range, 5-5]) were significantly higher than BPH (2 [interquartile range, 2-3]; P < 0.001). For all patients who had PI-RADS 1-5, the combined use of ADC (values or category) together with PI-RADS v2.1 did not perform significantly better than the use of PI-RADS v2.1 alone. The receiver operating characteristic of ADC category in combination with PI-RADS v2.1 score, 0.756 (95% confidence interval, 0.646-0.846), was significantly higher than that for PI-RADS 2.1 alone, 0.631 (95% confidence interval, 0.514-0.738), in PI-RADS 3-4 lesions ( P = 0.047).

CONCLUSION

The ADC category can help to improve the diagnostic performance of PI-RADS v2.1 category 3-4 lesions in diagnosing PCa.

Are T2WI PI-RADS sub-scores of transition zone prostate lesions biased by DWI information? A multi-reader, single-center study

PURPOSE

According to PI-RADS v2.1, T2-weighted imaging (T2WI) is the dominant sequence for transition zone (TZ) lesions. This study aimed to assess, whether diffusion-weighted imaging (DWI) information influences the assignment of T2WI scores.

METHOD

Out of 283 prostate MRI examinations with correlated biopsy results, fourty-four patients were selected retrospectively: first, 22 patients with a TZ lesion with T2WI and DWI scores ≥ 4, to represent lesions with unequivocal suspicion on T2WI and DWI. Second, 22 additional patients with TZ lesions of similar T2WI appearance but with corresponding DWI score ≤ 3 were added as control. Four residents and one board-certified radiologist each performed two assessments of the included patients: First, only T2WI was available (T2-only read); second, both T2WI and DWI sequences were available (biparametric read). Lesion scores were assessed using Wilcoxon signed-rank test, inter-reader agreement using weighted kappa and Kendall's W statistics, and sensitivity/specificity using McNemar test.

RESULTS

The T2WI scores were significantly different between the T2-only and biparametric read for 3 out of 4 residents (p ≤ 0.049) but not for the radiologist. The overall PI-RADS scores derived from the two reading sessions differed considerably for 35/220 cases (all readers pooled). Inter-reader agreement was fair for the T2WI and overall PI-RADS scores (mean kappa 0.27-0.30) and moderate for the DWI scores (mean kappa 0.43).

CONCLUSIONS

For inexperienced readers, assessment of T2WI is variable and potentially biased by availability of DWI information, which can lead to changes of overall PI-RADS score and consequently clinical management. Assessment of T2WI should be performed before reviewing DWI to ensure non-biased interpretation of TZ lesions in the dominant sequence.

Prevalence and grade of malignancy differences with respect to the area of involvement in multiparametric resonance imaging of the prostate in the diagnosis of prostate cancer using the PI-RADS version 2 classification

PURPOSE

The peripheral zone is histologically different from the transitional zone. The aim of this study is to analyze the differences between the prevalence and grade of malignancy of mpMRI-targeted biopsies that involve the TZ with respect to the PZ.

METHODS

A cross-sectional study of 597 men evaluated for PC screening between February 2016 and October 2022 was conducted. Exclusion criteria were prior BPH surgery, radiotherapy, 5-alpha-reductase inhibitors treatment, UTI, mixed involvement of PZ-TZ or doubts, and central-zone involvement. Hypothesis contrast test was used to study differences proportions of malignancy (ISUP > 0) and significant (ISUP > 1) and high-grade tumor (ISUP > 3) in PI-RADSv2 > 2-targeted biopsies in PZ with respect to TZ, and logistic regression and hypothesis contrast tests were used to study the influence of the area of exposure as an effect-modifying factor in the diagnosis of malignancy with respect to the PI-RADSv2 classification.

RESULTS

473 patients were selected and 573 lesions biopsied (127 PI-RADS3, 346 PI-RADS4 and 100 PI-RADS5). A significant increase was described in the proportion of malignancy and significant and high-grade tumor in PZ compared to TZ (22.6%, 21.3%, and 8.7%, respectively). Significant increase in proportions and malignancy were described in cores targeted to PZ with respect to TZ, highlight the differences between PZ and TZ for ST (37.3%vs23.7% for PI-RADS4, 69.2%vs27.3% for PI-RADS5, respectively). Statistically significant linear trend was described increasing for malignancy, significant and high-grade tumors with respect to the PI-RADSv2 scores (change > 10%).

CONCLUSION

Although the prevalence and grade of malignancy in the TZ is lower than in the PZ, PI-RADS4 and 5-targeted biopsies should not be omitted in this location, but PI-RADS3 could be.

Characterization of high-grade prostate cancer at multiparametric MRI: assessment of PI-RADS version 2.1 and version 2 descriptors across 21 readers with varying experience (MULTI study)

OBJECTIVE

To assess PI-RADSv2.1 and PI-RADSv2 descriptors across readers with varying experience.

METHODS

Twenty-one radiologists (7 experienced (≥ 5 years) seniors, 7 less experienced seniors and 7 juniors) assessed 240 'predefined' lesions from 159 pre-biopsy multiparametric prostate MRIs. They specified their location (peripheral, transition or central zone) and size, and scored them using PI-RADSv2.1 and PI-RADSv2 descriptors. They also described and scored 'additional' lesions if needed. Per-lesion analysis assessed the 'predefined' lesions, using targeted biopsy as reference; per-lobe analysis included 'predefined' and 'additional' lesions, using combined systematic and targeted biopsy as reference. Areas under the curve (AUCs) quantified the performance in diagnosing clinically significant cancer (csPCa; ISUP ≥ 2 cancer). Kappa coefficients (κ) or concordance correlation coefficients (CCC) assessed inter-reader agreement.

RESULTS

At per-lesion analysis, inter-reader agreement on location and size was moderate-to-good (κ = 0.60-0.73) and excellent (CCC ≥ 0.80), respectively. Agreement on PI-RADSv2.1 scoring was moderate (κ = 0.43-0.47) for seniors and fair (κ = 0.39) for juniors. Using PI-RADSv2.1, juniors obtained a significantly lower AUC (0.74; 95% confidence interval [95%CI]: 0.70-0.79) than experienced seniors (0.80; 95%CI 0.76-0.84; p = 0.008) but not than less experienced seniors (0.74; 95%CI 0.70-0.78; p = 0.75). As compared to PI-RADSv2, PI-RADSv2.1 downgraded 17 lesions/reader (interquartile range [IQR]: 6-29), of which 2 (IQR: 1-3) were csPCa; it upgraded 4 lesions/reader (IQR: 2-7), of which 1 (IQR: 0-2) was csPCa. Per-lobe analysis, which included 60 (IQR: 25-73) 'additional' lesions/reader, yielded similar results.

CONCLUSIONS

Experience significantly impacted lesion characterization using PI-RADSv2.1 descriptors. As compared to PI-RADSv2, PI-RADSv2.1 tended to downgrade non-csPCa lesions, but this effect was small and variable across readers.

Are Urologists Ready for Interpretation of Multiparametric MRI Findings? A Prospective Multicentric Evaluation

Aim: To assess urologists’ proficiency in the interpretation of multiparametric magnetic resonance imaging (mpMRI). Materials and Methods: Twelve mpMRIs were shown to 73 urologists from seven Italian institutions. Responders were asked to identify the site of the suspicious nodule (SN) but not to assign a PIRADS score. We set an a priori cut-off of 75% correct identification of SN as a threshold for proficiency in mpMRI reading. Data were analyzed according to urologists’ hierarchy (UH; resident vs. consultant) and previous experience in fusion prostate biopsies (E-fPB, defined as <125 vs. ≥125). Additionally, we tested for differences between non-proficient vs. proficient mpMRI readers. Multivariable logistic regression analyses (MVLRA) tested potential predictors of proficiency in mpMRI reading. Results: The median (IQR) number of correct identifications was 8 (6−8). Anterior nodules (number 3, 4 and 6) represented the most likely prone to misinterpretation. Overall, 34 (47%) participants achieved the 75% cut-off. When comparing consultants vs. residents, we found no differences in terms of E-fPB (p = 0.9) or in correct identification rates (p = 0.6). We recorded higher identification rates in urologists with E-fBP vs. their no E-fBP counterparts (75% vs. 67%, p = 0.004). At MVLRA, only E- fPB reached the status of independent predictor of proficiency in mpMRI reading (OR: 3.4, 95% CI 1.2−9.9, p = 0.02) after adjusting for UH and type of institution. Conclusions: Despite urologists becoming more familiar with interpretation of mpMRI, their results are still far from proficient. E-fPB enhances the proficiency in mpMRI interpretation.

Inter-reader agreement of the prostate imaging reporting and data system version v2.1 for detection of prostate cancer: A systematic review and meta-analysis

Objectives

We aimed to systematically assess the inter-reader agreement of the Prostate Imaging Reporting and Data System Version (PI-RADS) v2.1 for the detection of prostate cancer (PCa).

Methods

We included studies reporting inter-reader agreement of different radiologists that applied PI-RADS v2.1 for the detection of PCa. Quality assessment of the included studies was performed with the Guidelines for Reporting Reliability and Agreement Studies. The summary estimates of the inter-reader agreement were pooled with the random-effect model and categorized (from slight to almost perfect) according to the kappa (κ) value. Multiple subgroup analyses and meta-regression were performed to explore various clinical settings.

Results

A total of 12 studies comprising 2475 patients were included. The pooled inter-reader agreement for whole gland was κ=0.65 (95% CI 0.56-0.73), and for transitional zone (TZ) lesions was κ=0.62 (95% CI 0.51-0.72). There was substantial heterogeneity presented throughout the studies (I 2= 95.6%), and meta-regression analyses revealed that only readers' experience (<5 years vs. ≥5 years) was the significant factor associated with heterogeneity (P<0.01). In studies providing head-to-head comparison, there was no significant difference in inter-reader agreement between PI-RADS v2.1 and v2.0 for both the whole gland (0.64 vs. 0.57, p=0.37), and TZ (0.61 vs. 0.59, p=0.81).

Conclusions

PI-RADS v2.1 demonstrated substantial inter-reader agreement among radiologists for whole gland and TZ lesions. However, the difference in agreement between PI-RADS v2.0 and v2.1 was not significant for the whole gland or the TZ.

Comparison of Computed Diffusion-Weighted Imaging b2000 and Acquired Diffusion-Weighted Imaging b2000 for Detection of Prostate Cancer

Purpose

To compare the sensitivity of tumor detection and inter-observer agreement between acquired diffusion-weighted imaging (aDWI) b2000 and computed DWI (cDWI) b2000 in patients with prostate cancer (PCa).

Materials and Methods

Eighty-eight patients diagnosed with PCa by radical prostatectomy and having undergone pre-operative 3 Tesla-MRI, including DWI (b, 0, 100, 1000, 2000 s/mm2), were included in the study. cDWI b2000 was obtained from aDWI b0, b100, and b1000. Two independent reviewers performed a review of the aDWI b2000 and cDWI b2000 images in random order at 4-week intervals. A region of interest was drawn for the largest tumor on each dataset, and a Prostate Imaging-Reporting and Data System (PI-RADS) score based on PI-RADS v2.1 was recorded. Histologic topographic maps served as the reference standard.

Results

The study population's Gleason scores were 6 (n = 16), 7 (n = 53), 8 (n = 9), and 9 (n = 10). According to the reviewers, the sensitivities of cDWI b2000 and aDWI b2000 showed no significant differences (for reviewer 1, both 94% [83/88]; for reviewer 2, both 90% [79/88]; p = 1.000, respectively). The kappa values of cDWI b2000 and aDWI b2000 for the PI-RADS score were 0.422 (95% confidence interval [CI], 0.240-0.603) and 0.495 (95% CI, 0.308-0.683), respectively.

Conclusion

cDWI b2000 showed comparable sensitivity with aDWI b2000, in addition to sustained moderate inter-observer agreement, in the detection of PCa.

The role of MRI in prostate cancer: current and future directions

There has been an increasing role of magnetic resonance imaging (MRI) in the management of prostate cancer. MRI already plays an essential role in the detection and staging, with the introduction of functional MRI sequences. Recent advancements in radiomics and artificial intelligence are being tested to potentially improve detection, assessment of aggressiveness, and provide usefulness as a prognostic marker. MRI can improve pretreatment risk stratification and therefore selection of and follow-up of patients for active surveillance. MRI can also assist in guiding targeted biopsy, treatment planning and follow-up after treatment to assess local recurrence. MRI has gained importance in the evaluation of metastatic disease with emerging technology including whole-body MRI and integrated positron emission tomography/MRI, allowing for not only better detection but also quantification. The main goal of this article is to review the most recent advances on MRI in prostate cancer and provide insights into its potential clinical roles from the radiologist's perspective. In each of the sections, specific roles of MRI tailored to each clinical setting are discussed along with its strengths and weakness including already established material related to MRI and the introduction of recent advancements on MRI.

PI-RADS: multiparametric MRI in prostate cancer

Multiparametric MRI of the prostate gland has become the initial evaluation of biopsy naïve men with a clinical suspicion for prostate cancer. PI-RADS 2.1 is a joint initiative and framework for prostate MRI acquisition and reporting, which aims to standardize technique and interpretation across centers. Building upon experience accrued following the introduction of PI-RADS 2.0, version 2.1 provides key updates and important clarifications, although it is intended to be an active document, which continues to be updated. Continued advances in our understanding of prostate cancer and progress in imaging technology will undoubtedly shape future iterations of the reporting system.

Comparison of Prostate Imaging and Reporting Data System V2.0 and V2.1 for Evaluation of Transition Zone Lesions: A 5-Reader 202-Patient Analysis

OBJECTIVE

The aim of the study was to compare the distribution of Prostate Imaging and Reporting Data System (PI-RADS) scores, interreader agreement, and diagnostic performance of PI-RADS v2.0 and v2.1 for transition zone (TZ) lesions.

METHODS

The study included 202 lesions in 202 patients who underwent 3T prostate magnetic resonance imaging showing a TZ lesion that was later biopsied with magnetic resonance imaging/ultrasound fusion. Five abdominal imaging faculty reviewed T2-weighted imaging and high b value/apparent diffusion coefficient images in 2 sessions. Cases were randomized using a crossover design whereby half in the first session were reviewed using v2.0 and the other half using v2.1, and vice versa for the 2nd session. Readers provided T2-weighted imaging and DWI scores, from which PI-RADS scores were derived.

RESULTS

Interreader agreement for all PI-RADS scores had κ of 0.37 (v2.0) and 0.26 (v2.1). For 4 readers, the percentage of lesions retrospectively scored PI-RADS 1 increased greater than 5% and PI-RADS 2 score decreased greater than 5% from v2.0 to v2.1. For 2 readers, the percentage scored PI-RADS 3 decreased greater than 5% and, for 2 readers, increased greater than 5%. The percentage of PI-RADS 4 and 5 lesions changed less than 5% for all readers. For the 4 readers with increased frequency of PI-RADS 1 using v2.1, 4% to 16% were Gleason score ≥3 + 4 tumor. Frequency of Gleason score ≥3 + 4 in PI-RADS 3 lesions increased for 2 readers and decreased for 1 reader. Sensitivity of PI-RADS of 3 or greater for Gleason score ≥3 + 4 ranged 76% to 90% (v2.0) and 69% to 96% (v2.1). Specificity ranged 32% to 64% (v2.0) and 25% to 72% (v2.1). Positive predictive value ranged 43% to 55% (v2.0) and 41% to 58% (v2.1). Negative predictive value ranged 82% to 87% (v2.0) and 81% to 91% (v2.1).

CONCLUSIONS

Poor interreader agreement and lack of improvement in diagnostic performance indicate an ongoing need to refine evaluation of TZ lesions.

Detection of High-Grade Prostate Cancer With a Super High B-value (4000 s/mm2) in Diffusion-Weighted Imaging Sequences by Magnetic Resonance Imaging

Introduction: High-grade adenocarcinoma of the prostate tends to have denser glandular structures and a prominent desmoplastic reaction, which could be detected by magnetic resonance imaging (MRI) with a super-high b-value in diffusion-weighted imaging (DWI) sequence, to differentiate it from low-grade carcinomas.

Objective: To evaluate the diagnostic validity of the diffusion sequence with values ​​of b4000 s/mm2 for the diagnosis of high-grade prostate cancer (Gleason score ≥ 7).

Materials and methods: It is a retrospective analytical study of male patients who have undergone a prostate biopsy and count with a prostate MRI with a DWI sequence of a super-high b-value (4000 s/mm2).

Results: The sensitivity of the diffusion sequence with b4000 s/mm2 values ​​to classify as positive for prostate cancer was 57.14% as compared to biopsy. The specificity of the diffusion sequence with b4000 s/mm2 values ​​classifying patients with prostate carcinoma as negative was 84.62%. The probability that the diffusion sequence with b4000 s/mm2 values ​​classifies patients with prostate cancer was 80%. The probability that the diffusion sequence with b4000 s/mm2 values ​​does not classify patients with prostate cancer was 64.71%. The proportion of patients adequately classified with prostate cancer using the diffusion sequence with b4000 s/mm2 values ​​was 70.37%.

Conclusions: The study shows that using the diffusion sequence with values of b4000 s/mm2 is an optimal value that serves as a tool to be able to decant those high-risk carcinomas with those of low risk; however, it is not a definitive method of diagnosis that could replace the performance of a biopsy. Since the study sample was limited, these results cannot be interpreted as reliable for diagnosing high-grade prostate cancer and should encourage future studies on a larger scale population to obtain significant evidence for a non-invasive diagnostic tool with a better cost-benefit for the patient.

Interobserver Agreement and Accuracy in Interpreting mpMRI of the Prostate: a Systematic Review

PURPOSE OF REVIEW

To present the latest evidence related to interobserver agreement and accuracy; evaluate the strengths, weaknesses, and implications of use; and outline opportunities for improvement and future development of the Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) for detection of prostate cancer (PCa) on multiparametric magnetic resonance imaging (mpMRI).

RECENT FINDINGS

Our review of currently available evidence suggests that recent improvements to the PI-RADS system with PI-RADS v2.1 slightly improved interobserver agreement, with generally high sensitivity and moderate specificity for the detection of clinically significant PCa. Recent evidence additionally demonstrates substantial improvement in diagnostic specificity with PI-RADS v2.1 compared with PI-RADS v2. However, results of studies examining the comparative performance of v2.1 are limited by small sample sizes and retrospective cohorts, potentially introducing selection bias. Some studies suggest a substantial improvement between v2.1 and v2, while others report no statistically significant difference. Additionally, in PI-RADS v2.1, the interpretation and reporting of certain findings remain subjective, particularly for category 2 lesions, and reader experience continues to vary significantly. These factors further contribute to a remaining degree of interobserver variability and findings of improved performance among more experienced readers. PI-RADS v2.1 appears to show at least minimal improvement in interobserver agreement, diagnostic performance, and both sensitivity and specificity, with greater improvements seen among more experienced readers. However, given the decrescent nature of these improvements and the limited power of all studies examined, the clinical impact of this progress may be marginal. Despite improvements in PI-RADS v2.1, practitioner experience in interpreting mpMRI of the prostate remains the most important factor in prostate cancer detection.

The Role of PSA Density among PI-RADS v2.1 Categories to Avoid an Unnecessary Transition Zone Biopsy in Patients with PSA 4-20 ng/mL

Objective

To evaluate the role of prostate-specific antigen density (PSAD) in different Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) categories to avoid an unnecessary biopsy in transition zone (TZ) patients with PSA ranging from 4 to 20 ng/mL.

Materials and Methods

In this retrospective and single-center study, 333 biopsy-naïve patients with TZ lesions who underwent biparametric magnetic resonance imaging (bp-MRI) were analyzed from January 2016 to March 2020. Multivariate logistic regression analyses were performed to determine independent predictors of clinically significant prostate cancer (cs-PCa). The receiver operating characteristic (ROC) curve was used to compare diagnostic performance.

Results

PI-RADS v2.1 and PSAD were the independent predictors for TZ cs-PCa in patients with PSA 4-20 ng/mL. 0.9% (2/213), 10.0% (7/70), and 48.0% (24/50) of PI-RADS v2.1 score 1-2, 3, and 4-5 had TZ cs-PCa. However, for patients with PI-RADS v2.1 score 1-2, there were no obvious changes in the detection of TZ cs-PCa (0.8% (1/129), 1.3% (1/75), and 0.0% (0/9)) combining with different PSAD stratification (PSAD < 0.15, 0.15-0.29, and ≥0.30 ng/mL/mL). For patients with PI-RADS v2.1 score ≥ 3, the TZ cs-PCa detection rate significantly varied according to different PSAD stratification. A PI-RADS v2.1 score 3 and PSAD < 0.15 and 0.15-0.29 ng/mL/mL had 8.6% (3/35) and 3.7% (1/27) of TZ cs-PCa, while a PI-RADS v2.1 score 3 and PSAD ≥ 0.30 ng/mL/mL had a higher TZ cs-PCa detection rate (37.5% (3/8)). A PI-RADS v2.1 score 4-5 and PSAD <0.15 ng/mL/mL had no cs-PCa (0.0% (0/9)). In contrast, a PI-RADS v2.1 score 4-5 and PSAD 0.15-0.29 and ≥0.30 ng/mL/mL had the highest cs-PCa detection rate (50.0% (10/20), 66.7% (14/21)). It showed the highest AUC in the combination of PI-RADS v2.1 and PSAD (0.910), which was significantly higher than PI-RADS v2.1 (0.889, P = 0.039) or PSAD (0.803, P < 0.001).

Conclusions

For TZ patients with PSA 4-20 ng/mL, PI-RADS v2.1 score ≤ 2 can avoid an unnecessary biopsy regardless of PSAD. PI-RADS v2.1 score ≥ 3 may avoid an unnecessary biopsy after combining with PSAD. PI-RADS v2.1 combined with PSAD could significantly improve diagnostic performance.

A Combined Radiomics and Machine Learning Approach to Distinguish Clinically Significant Prostate Lesions on a Publicly Available MRI Dataset

Although prostate cancer is one of the most common causes of mortality and morbidity in advancing-age males, early diagnosis improves prognosis and modifies the therapy of choice. The aim of this study was the evaluation of a combined radiomics and machine learning approach on a publicly available dataset in order to distinguish a clinically significant from a clinically non-significant prostate lesion. A total of 299 prostate lesions were included in the analysis. A univariate statistical analysis was performed to prove the goodness of the 60 extracted radiomic features in distinguishing prostate lesions. Then, a 10-fold cross-validation was used to train and test some models and the evaluation metrics were calculated; finally, a hold-out was performed and a wrapper feature selection was applied. The employed algorithms were Naïve bayes, K nearest neighbour and some tree-based ones. The tree-based algorithms achieved the highest evaluation metrics, with accuracies over 80%, and area-under-the-curve receiver-operating characteristics below 0.80. Combined machine learning algorithms and radiomics based on clinical, routine, multiparametric, magnetic-resonance imaging were demonstrated to be a useful tool in prostate cancer stratification.

The Micro-ultrasound Guided Prostate Biopsy in Detection of Prostate Cancer: A Systematic Review and Meta-Analysis

BACKGROUND

To compare the detection rate of micro-ultrasound with multiparametric magnetic resonance imaging targeted biopsy (mpMRI-TB) for prostate cancer diagnosis.

METHODS

The studies on micro-ultrasound prostate biopsy for prostate cancer diagnosis were searched in PubMed, Cochrane library and EMBASE databases from inception to April.2021. we performed a systematic review and cumulative meta-analysis based on search results using Software Rev-Man 5.3.

RESULTS

A total of 11 studies involving 1081 patients were included. The Meta-analysis showed that no significant difference was found between micro-ultrasound and mpMRI-TB in the total detection of prostate cancer(OR:1.01, 95%CI:0.85~1.21, p=0.89), of Grading Groups(GG)=1(OR: 0.92, 95%CI:0.68~1.25, p=0.59),of GG≥2(OR:1.01, 95%CI:0.83~1.22, p=0.92), and of GG≥3(OR: 1.31, 95%CI:0.95~1.81, p=0.10).

CONCLUSIONS

Micro-ultrasound guided prostate biopsy provides comparable detection rates for prostate cancer diagnosis with the mpMRI-TB, which is expected to challenge mpMRI-TB in the diagnosis of prostate cancer.

Fully automated detection of prostate transition zone tumors on T2-weighted and apparent diffusion coefficient (ADC) map MR images using U-Net ensemble

PURPOSE

Accurate detection of transition zone (TZ) prostate cancer (PCa) on magnetic resonance imaging (MRI) remains challenging using clinical subjective assessment due to overlap between PCa and benign prostatic hyperplasia (BPH). The objective of this paper is to describe a deep-learning-based framework for fully automated detection of PCa in the TZ using T2-weighted (T2W) and apparent diffusion coefficient (ADC) map MR images.

METHOD

This was a single-center IRB-approved cross-sectional study of men undergoing 3T MRI on two systems. The dataset consisted of 196 patients (103 with and 93 without clinically significant [Grade Group 2 or higher] TZ PCa) to train and test our proposed methodology, with an additional 168 patients with peripheral zone PCa used only for training. We proposed an ensemble of classifiers in which multiple U-Net-based models are designed for prediction of TZ PCa location on ADC map MR images, with initial automated segmentation of the prostate to guide detection. We compared accuracy of ADC alone to T2W and combined ADC+T2W MRI for input images, and investigated improvements using ensembles over their constituent models with different methods of diversity in individual models by hyperparameter configuration, loss function and model architecture.

RESULTS

Our developed algorithm reported sensitivity and precision of 0.829 and 0.617 in 56 test cases containing 31 instances of TZ PCa and in 25 patients without clinically significant TZ tumors. Patient-wise classification accuracy had an area under receiver operator characteristic curve (AUROC) of 0.974. Single U-Net models using ADC alone (sensitivity 0.829, precision 0.534) outperformed assessment using T2W (sensitivity 0.086, precision 0.081) and assessment using combined ADC+T2W (sensitivity 0.687, precision 0.489). While the ensemble of U-Nets with varying hyperparameters demonstrated the highest performance, all ensembles improved PCa detection compared to individual models, with sensitivities and precisions close to the collective best of constituent models.

CONCLUSION

We describe a deep-learning-based method for fully automated TZ PCa detection using ADC map MR images that outperformed assessment by T2W and ADC+T2W.

Role of MRI for the detection of prostate cancer

The use of multiparametric MRI has been hastened under expanding, novel indications for its use in the diagnostic and management pathway of men with prostate cancer. This has helped drive a large body of the literature describing its evolving role over the last decade. Despite this, prostate cancer remains the only solid organ malignancy routinely diagnosed with random sampling. Herein, we summarize the components of multiparametric MRI and interpretation, and present a critical review of the current literature supporting is use in prostate cancer detection, risk stratification, and management.

Correlation between Intraprostatic PSMA Uptake and MRI PI-RADS of [68Ga]Ga-PSMA-11 PET/MRI in Patients with Prostate Cancer: Comparison of PI-RADS Version 2.0 and PI-RADS Version 2.1

PURPOSE

We aimed to evaluate the correlation between PSMA uptake and magnetic resonance imaging (MRI) PI-RADS of simultaneous [68Ga]Ga-PSMA-11 PET/MRI regarding PI-RADS version 2.0 and 2.1 respectively and compared the difference between these two versions.

MATERIALS AND METHODS

We retrospectively analyzed a total of forty-six patients with biopsy-proven prostate cancer who underwent simultaneous [68Ga]Ga-PSMA-11 PET/MRI. We classified the lesions regarding PI-RADS version 2.0 and 2.1, peripheral zone (PZ), and transitional zone (TZ), respectively. Based on regions of interest (ROI), standardized uptake values maximum (SUVmax), and corresponding lesion-to-background ratios (LBR) of SUVmax of each category, PI-RADS score 1 to 5, were measured. A comparison between PI-RADS version 2.0 and PI-RADS version 2.1 was performed.

RESULTS

A total of 215 focal prostate lesions were analyzed, including two subgroups, 125 TZ and 90 PZ. Data are reported as median and interquartile range (IQR). Regarding PI-RADS version 2.1, TZ SUVmax of each category were 1.5 (0.5, 1.9), 1.9 (0.8, 2.3), 3.3 (2.1, 4.6), 4.2 (3.1, 5.7), 7.3 (5.2, 9.7). PZ SUVmax of each category were 1.0 (0.8, 1.6), 2.5 (1.5, 3.2), 3.3 (1.9, 4.5), 4.3 (3.0, 5.4), 7.4 (5.0, 9.3). Regarding the inter-reader agreement of the overall PI-RADS assessment category, the kappa value was 0.723 for version 2.0 and 0.853 for version 2.1.

CONCLUSION

Revisions of PI-RADS version 2.1 results in variations in lesions classification. Lesions with the PI-RADS category of 3, 4, and 5 present relatively higher intraprostatic PSMA uptake, while lesions with the PI-RADS category of 1 and 2 present relatively lower and similar uptake. Version 2.1 has higher inter-reader reproducibility than version 2.0.

PI-RADSv2.1: Current status

Multiparametric magnetic resonance imaging (mpMRI) has played an increasing role in the detection and local staging of prostate cancer over the last 15 years. Prostate mpMRI, due to various factors, is prone to high inter-reader variability necessitating standardized reporting guidelines that provide accurate and actionable information to the ordering clinician. The Prostate Imaging-Reporting and Data System version 2.1 (PI-RADSv2.1) was released in March 2019 as an update to PI-RADSv2.0 with the hope of further standardizing the reporting process of prostate mpMRI, improving the detection of clinically significant cancer, reducing the biopsy rate of indolent tumors, and decreasing inter-reader variability. Early data show an improved performance of PI-RADSv2.1 over PI-RADSv2.0. Updates included in PI-RADSv2.1 and its current experience in clinic will be reviewed in this review.

Comparison of the PI-RADS 2.1 scoring system to PI-RADS 2.0: Impact on diagnostic accuracy and inter-reader agreement

Purpose

To assess the value of the PI-RADS 2.1 scoring system in the detection of prostate cancer on multiparametric MRI in comparison to the standard PI-RADS 2.0 system and to assess its inter-reader variability.

Materials and methods

This IRB-approved study included 229 patients undergoing multiparametric prostate MRI prior to MRI-guided TRUS-based biopsy, which were retrospectively recruited from our prospectively maintained institutional database. Two readers with high (reader 1, 6 years) and low (reader 2, 2 years) level of expertise identified the lesion with the highest PI-RADS score for both version 2.0 and 2.1 for each patient. Inter-reader agreement was estimated, and diagnostic accuracy analysis was performed.

Results

Inter-reader agreement on PI-RADS scores was fair for both version 2.0 (kappa: 0.57) and 2.1 (kappa: 0.51). Detection rates for prostate cancer (PCa) and clinically significant prostate cancer (csPCa) were almost identical for both PI-RADS versions and higher for the more experienced reader (AUC, Reader 1: PCa, 0.881–0.887, csPCa, 0.874–0.879; Reader 2: PCa, 0.765, csPCa, 0.746–0.747; both p > 0.05), both when using a PI-RADS score of ≥ 4 and ≥3 as indicators for positivity for cancer.

Conclusions

The new PI-RADS 2.1 scoring system showed comparable diagnostic performance and inter-reader variability compared to version 2.0. The introduced changes in the version 2.1 seem only to take effect in a very small number of patients.

Computer-Aided Diagnosis in Multiparametric MRI of the Prostate: An Open-Access Online Tool for Lesion Classification with High Accuracy

Computer-aided diagnosis (CADx) approaches could help to objectify reporting on prostate mpMRI, but their use in many cases is hampered due to common-built algorithms that are not publicly available. The aim of this study was to develop an open-access CADx algorithm with high accuracy for classification of suspicious lesions in mpMRI of the prostate. This retrospective study was approved by the local ethics commission, with waiver of informed consent. A total of 124 patients with 195 reported lesions were included. All patients received mpMRI of the prostate between 2014 and 2017, and transrectal ultrasound (TRUS)-guided and targeted biopsy within a time period of 30 days. Histopathology of the biopsy cores served as a standard of reference. Acquired imaging parameters included the size of the lesion, signal intensity (T2w images), diffusion restriction, prostate volume, and several dynamic parameters along with the clinical parameters patient age and serum PSA level. Inter-reader agreement of the imaging parameters was assessed by calculating intraclass correlation coefficients. The dataset was stratified into a train set and test set (156 and 39 lesions in 100 and 24 patients, respectively). Using the above parameters, a CADx based on an Extreme Gradient Boosting algorithm was developed on the train set, and tested on the test set. Performance optimization was focused on maximizing the area under the Receiver Operating Characteristic curve (ROC_AUC). The algorithm was made publicly available on the internet. The CADx reached an ROC_AUC of 0.908 during training, and 0.913 during testing (p = 0.93). Additionally, established rule-in and rule-out criteria allowed classifying 35.8% of the malignant and 49.4% of the benign lesions with error rates of <2%. All imaging parameters featured excellent inter-reader agreement. This study presents an open-access CADx for classification of suspicious lesions in mpMRI of the prostate with high accuracy. Applying the provided rule-in and rule-out criteria might facilitate to further stratify the management of patients at risk.