Deep learning model for the detection of prostate cancer and classification of clinically significant disease using multiparametric MRI in comparison to PI-RADs score

BACKGROUND

The Prostate Imaging Reporting and Data System (PI-RADS) is an established reporting scheme for multiparametric magnetic resonance imaging (mpMRI) to distinguish clinically significant prostate cancer (csPCa). Deep learning (DL) holds great potential for automating csPCa classification on mpMRI.

METHOD

To compare the performance between a DL algorithm and PI-RADS categorization in PCa detection and csPCa classification, we included 1,729 consecutive patients who underwent radical prostatectomy or biopsy in Tongji hospital. We developed DL models by integrating individual mpMRI sequences and employing an ensemble approach for distinguishing between csPCa and CiSPCa (specifically defined as PCa with a Gleason group 1 or benign prostate disease, training cohort: 1,285 patients vs. external testing cohort: 315 patients).

RESULTS

DL-based models exhibited higher csPCa detection rates than PI-RADS categorization (area under the curve [AUC]: 0.902; sensitivity: 0.728; specificity: 0.906 vs. AUC: 0.759; sensitivity: 0.761; specificity: 0.756) (P < 0.001) Notably, DL networks exhibited significant strength in the prostate-specific antigen (PSA) arm < 10 ng/ml compared with PI-RADS assessment (AUC: 0.788; sensitivity: 0.588; specificity: 0.883 vs. AUC: 0.618; sensitivity: 0.379; specificity: 0.763) (P = 0.041).

CONCLUSIONS

We developed DL-based mpMRI ensemble models for csPCa classification with improved sensitivity, specificity, and accuracy compared with clinical PI-RADS assessment. In the PSA-stratified condition, the DL ensemble model performed better than PI-RADS in the detection of csPCa in both the high PSA group and the low PSA group.

Weakly Supervised MRI Slice-Level Deep Learning Classification of Prostate Cancer Approximates Full Voxel- and Slice-Level Annotation: Effect of Increasing Training Set Size

BACKGROUND

Weakly supervised learning promises reduced annotation effort while maintaining performance.

PURPOSE

To compare weakly supervised training with full slice-wise annotated training of a deep convolutional classification network (CNN) for prostate cancer (PC).

STUDY TYPE

Retrospective.

SUBJECTS

One thousand four hundred eighty-nine consecutive institutional prostate MRI examinations from men with suspicion for PC (65 ± 8 years) between January 2015 and November 2020 were split into training (N = 794, enriched with 204 PROSTATEx examinations) and test set (N = 695).

FIELD STRENGTH/SEQUENCE

1.5 and 3T, T2-weighted turbo-spin-echo and diffusion-weighted echo-planar imaging.

ASSESSMENT

Histopathological ground truth was provided by targeted and extended systematic biopsy. Reference training was performed using slice-level annotation (SLA) and compared to iterative training utilizing patient-level annotations (PLAs) with supervised feedback of CNN estimates into the next training iteration at three incremental training set sizes (N = 200, 500, 998). Model performance was assessed by comparing specificity at fixed sensitivity of 0.97 [254/262] emulating PI-RADS ≥ 3, and 0.88-0.90 [231-236/262] emulating PI-RADS ≥ 4 decisions.

STATISTICAL TESTS

Receiver operating characteristic (ROC) and area under the curve (AUC) was compared using DeLong and Obuchowski test. Sensitivity and specificity were compared using McNemar test. Statistical significance threshold was P = 0.05.

RESULTS

Test set (N = 695) ROC-AUC performance of SLA (trained with 200/500/998 exams) was 0.75/0.80/0.83, respectively. PLA achieved lower ROC-AUC of 0.64/0.72/0.78. Both increased performance significantly with increasing training set size. ROC-AUC for SLA at 500 exams was comparable to PLA at 998 exams (P = 0.28). ROC-AUC was significantly different between SLA and PLA at same training set sizes, however the ROC-AUC difference decreased significantly from 200 to 998 training exams. Emulating PI-RADS ≥ 3 decisions, difference between PLA specificity of 0.12 [51/433] and SLA specificity of 0.13 [55/433] became undetectable (P = 1.0) at 998 exams. Emulating PI-RADS ≥ 4 decisions, at 998 exams, SLA specificity of 0.51 [221/433] remained higher than PLA specificity at 0.39 [170/433]. However, PLA specificity at 998 exams became comparable to SLA specificity of 0.37 [159/433] at 200 exams (P = 0.70).

DATA CONCLUSION

Weakly supervised training of a classification CNN using patient-level-only annotation had lower performance compared to training with slice-wise annotations, but improved significantly faster with additional training data.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

The Value of 68Ga-PSMA PET/MRI for Classifying Patients with PI-RADS 3 Lesions on Multiparametric MRI: A Prospective Single-Center Study

Prostate Imaging Reporting and Data System (PI-RADS) category 3 lesions remain a diagnostic challenge for detecting clinically significant prostate cancer (csPCa). This article evaluates the added value of 68Ga-labeled prostate-specific membrane antigen-11 (68Ga-PSMA) PET/MRI in classifying PI-RADS 3 lesions to avoid unnecessary biopsies. Methods: Sixty biopsy-naïve men with PI-RADS 3 lesions on multiparametric MRI were prospectively enrolled between February 2020 and October 2022. In all, 56 participants underwent 68Ga-PSMA PET/MRI and prostate systematic biopsy. 68Ga-PSMA PET/MRI was independently evaluated and reported by the 5-level PRIMARY score developed within the PRIMARY trial. Receiver-operating-characteristic curve analysis was used to estimate the diagnostic performance. Results: csPCa was detected in 8 of 56 patients (14.3%). The proportion of patients with csPCa and a PRIMARY score of 1, 2, 3, 4, and 5 was 0% (0/12), 0% (0/13), 6.3% (1/16), 38.5% (5/13), and 100% (2/2), respectively. The estimated area under the curve of the PRIMARY score was 0.91 (95% CI, 0.817-0.999). For a PRIMARY score of 4-5 versus a PRIMARY score of 1-3, the sensitivity, specificity, positive predictive value, and negative predictive value were 87.5%, 83.3%, 46.7%, and 97.5%, respectively. With a PRIMARY score of at least 4 to make a biopsy decision in men with PI-RADS 3 lesions, 40 of 48 patients (83.3%) could avoid unnecessary biopsies, at the expense of missing 1 of 8 (12.5%) csPCa cases. Conclusion: 68Ga-PSMA PET/MRI has great potential to classify patients with PI-RADS 3 lesions and help avoid unnecessary biopsies.

Detection of the Highest-Grade Lesion in Multifocal Discordant Prostate Cancer by Multiparametric Magnetic Resonance Imaging

PURPOSE

Prostate cancer generally occurs multifocally. The lesions of the largest size and highest-grade are often concordant, and defined as an index tumor. However, these factors sometimes do not coincide within one lesion. In such discordant cases, not the largest size lesion but the highest-grade lesion is known to determine the prognosis. We focused on the multiparametric magnetic resonance imaging (mpMRI) detectability of the highest-grade tumors in discordant cases.

MATERIALS AND METHODS

We investigated the detectability of the highest-grade tumor using preoperative mpMRI in 50 discordant patients who underwent radical prostatectomy. The radiologist was informed of the tumor location on the pathological tumor map, and mpMRI interpretation for each tumor was performed.

RESULTS

Prostate Imaging-Reporting and Data System (PI-RADS) scores of 1, 2, 3, 4, and 5 on preoperative mpMRI were assigned to 13, 1, 9, 16, and 11 of the largest tumors, respectively. On the other hand, scores of 1, 2, 3, 4, and 5 were assigned to 23, 0, 7, 19, and 1 of the highest-grade tumors, respectively. The difference between them was statistically significant (p=0.007). We also found that the largest anterior tumor frequently hid the ipsilateral posterior highest-grade tumor; the detection rate of the highest-grade tumor in this pattern was 42.1% (8 of 19 cases) CONCLUSION: We found that mpMRI detectability of the highest-grade tumor in discordant cases was inferior to that of the largest tumor with low malignant potential. Our results suggest that the risk of high-grade tumors which determine patient prognosis being overlooked.

PSA-density, DRE, and PI-RADS 5: potential surrogates for omitting biopsy?

OBJECTIVE

In contrast to other malignancies, histologic confirmation prior treatment in patients with a high suspicion of clinically significant prostate cancer (csPCA) is common. To analyze the impact of extracapsular extension (ECE), cT-stage defined by digital rectal examination (DRE), and PSA-density (PSA-D) on detection of csPCA in patients with at least one PI-RADS 5 lesion (hereinafter, "PI-RADS 5 patients").

MATERIALS AND METHODS

PI-RADS 5 patients who underwent MRI/Ultrasound fusion biopsy (Bx) between 2016 and 2020 were identified in our institutional database. Uni- and multivariable logistic-regression models were used to identify predictors of csPCA-detection (GGG ≥ 2). Risk models were adjusted for ECE, PSA-D, and cT-stage. Corresponding Receiver Operating Characteristic (ROC) curves and areas under the curve (AUC) were calculated.

RESULTS

Among 493 consecutive PI-RADS 5 patients, the median age and PSA was 69 years (IQR 63-74) and 8.9 ng/ml (IQR 6.0-13.7), respectively. CsPCA (GGG ≥ 2) was detected in 405/493 (82%); 36/493 patients (7%) had no cancer. When tabulating for PSA-D of > 0.2 ng/ml/cc and > 0.5 ng/ml/cc, csPCA was found in 228/253 (90%, PI-RADS5 + PSA-D > 0.2 ng/ml/cc) and 54/54 (100%, PI-RADS5 + PSA-D > 0.5 ng/ml/cc). Finally, a model incorporating PSA-D and cT-stage achieved an AUC of 0.79 (CI 0.74-0.83).

CONCLUSION

In PI-RADS 5 patients, PSA-D and cT-stage emerged as strong predictors of csPCA at biopsy. Moreover, when adding the threshold of PSA-D > 0,5 ng/ml/cc, all PI-RADS 5 patients were diagnosed with csPCA. Therefore, straight treatment for PCA can be considered, especially if risk-factors for biopsy-related complications such as obligatory dual platelet inhibition are present.

PI-RADS v2.1 and PSAD for the prediction of clinically significant prostate cancer among patients with PSA levels of 4-10 ng/ml

This study intended to evaluate the diagnostic accuracy of the prostate imaging reporting and data system (PI-RADS) and prostate-specific antigen density (PSAD) for clinically significant prostate cancer (csPCa) with PSA levels of 4-10 ng/ml. Between July 2018 and June 2022, a total of 453 patients with PSA levels of 4-10 ng/ml were retrospectively included, which were randomly assigned to the training group (323 patients) and validation group (130 patients). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with their 95% CI were calculated. The overall diagnostic performance was determined with area under the receiver operating characteristic curve (AUC), and an integrated nomogram combining PI-RADS score and PSAD was constructed and tested in a validation cohort. In the training group, the AUC for PI-RADS 2.1 and PSAD alone were 0.875 (95% CI 0.834-0.916) and 0.712 (95% CI 0.648-0.775). At the cutoff PI-RADS score ≥ 4, the sensitivity and specificity were 86.2% (95% CI 77.4-1.9%) and 84.7% (95% CI 79.6-88.8%), respectively. For PSAD, the sensitivity and specificity were 73.3% (95% CI 63.0-82.4%) and 62.1% (95% CI 55.8-68.5%) at the cutoff 0.162 ng/ml/ml. While combining PI-RADS with PSAD, the diagnostic performance was improved significantly, with AUC of 0.893 (95% CI 0.853-0.933). In the validation group, the nomogram yielded a AUC of 0.871 (95% CI 0.807-0.934), which is significantly higher than PI-RADS alone (0.829, 95% CI 0.759-0.899, P = 0.02). For patients with PSA levels of 4-10 ng/ml, PSAD demonstrated moderate diagnostic accuracy whereas PI-RADS showed high performance. By combination of PSAD and PI-RADS together, the diagnostic performance could be improved significantly.

Cancer Detection Rate and Abnormal Interpretation Rate of Prostate MRI in Patients With Low-Grade Cancer

PURPOSE

The aim of this study was to evaluate the utility of cancer detection rate (CDR) and abnormal interpretation rate (AIR) in prostate MRI for patients with low-grade prostate cancer (PCa).

METHODS

This three-center retrospective study included patients who underwent prostate MRI from 2017 to 2021 with known low-grade PCa (Gleason score 6) without prior treatment. Patient-level highest Prostate Imaging Reporting & Data System (PI-RADS®) score and pathologic diagnosis within 1 year after MRI were used to evaluate the diagnostic performance of prostate MRI in detecting clinically significant PCa (csPCa; Gleason score ≥ 7). The metrics AIR, CDR, and CDR adjusted for pathologic confirmation rate were calculated. Radiologist-level AIR-CDR plots were shown. Simulation AIR-CDR lines were created to assess the effects of different diagnostic performances of prostate MRI and the prevalence of csPCa.

RESULTS

A total of 3,207 examinations were interpreted by 33 radiologists. Overall AIR, CDR, and CDR adjusted for pathologic confirmation rate at PI-RADS 3 to 5 (PI-RADS 4 and 5) were 51.7% (36.5%), 22.1% (18.8%), and 30.7% (24.6%), respectively. Radiologist-level AIR and CDR at PI-RADS 3 to 5 (PI-RADS 4 and 5) were in the 36.8% to 75.6% (21.9%-57.5%) range and the 16.3%-28.7% (10.9%-26.5%) range, respectively. In the simulation, changing parameters of diagnostic performance or csPCa prevalence shifted the AIR-CDR line.

CONCLUSIONS

The authors propose CDR and AIR as performance metrics in prostate MRI and report reference performance values in patients with known low-grade PCa. There was variability in radiologist-level AIR and CDR. Combined use of AIR and CDR could provide meaningful feedback for radiologists to improve their performance by showing relative performance to other radiologists.

Based on PI-RADS v2.1 combining PHI and ADC values to guide prostate biopsy in patients with PSA 4-20 ng/mL

PURPOSE

The study aimed to investigate the diagnostic accuracy of prostate health index (PHI) and apparent diffusion coefficient (ADC) values in predicting prostate cancer (PCa) and construct a nomogram for the prediction of PCa and clinically significant PCa (CSPCa) in Prostate Imaging-Reporting and Data System (PI-RADS) three lesions cohort.

METHODS

This study prospectively enrolled 301 patients who underwent multiparametric magnetic resonance (mpMRI) and were scheduled for prostate biopsy. The receiver operating characteristic curve (ROC) was performed to estimate the diagnostic accuracy of each predictor. Univariable and multivariable logistic regression analysis was conducted to ascertain hidden risk factors and constructed nomograms in PI-RADS three lesions cohort.

RESULTS

In the whole cohort, the area under the ROC curve (AUC) of PHI is relatively high, which is 0.779. As radiographic parameters, the AUC of PI-RADS and ADC values was 0.702 and 0.756, respectively. The utilization of PHI and ADC values either individually or in combination significantly improved the diagnostic accuracy of the basic model. In PI-RADS three lesions cohort, the AUC for PCa was 0.817 in the training cohort and 0.904 in the validation cohort. The AUC for CSPCa was 0.856 in the training cohort and 0.871 in the validation cohort. When applying the nomogram for predicting PCa, 50.0% of biopsies could be saved, supplemented by 6.9% of CSPCa being missed.

CONCLUSION

PHI and ADC values can be used as predictors of CSPCa. The nomogram included PHI, ADC values and other clinical predictors demonstrated an enhanced capability in detecting PCa and CSPCa within PI-RADS three lesions cohort.

Cancer Detection Rate and Abnormal Interpretation Rate of Prostate MRI Performed for Clinical Suspicion of Prostate Cancer

PURPOSE

To report cancer detection rate (CDR) and abnormal interpretation rate (AIR) in prostate MRI performed for clinical suspicion of prostate cancer (PCa).

MATERIALS AND METHODS

This retrospective single-institution, three-center study included patients who underwent MRI for clinical suspicion of PCa between 2017 and 2021. Patients with known PCa were excluded. Patient-level Prostate Imaging-Reporting and Data System (PI-RADS) score was extracted from the radiology report. AIR was defined as number of abnormal MRI (PI-RADS score 3-5) / total number of MRIs. CDR was defined as number of clinically significant PCa (csPCa: Gleason score ≥7) detected at abnormal MRI / total number of MRI. AIR, CDR, and CDR adjusted for pathology confirmation rate were calculated for each of three centers and pre-MRI biopsy status (biopsy-naive and previous negative biopsy).

RESULTS

A total of 9,686 examinations (8,643 unique patients) were included. AIR, CDR, and CDR adjusted for pathology confirmation rate were 45.4%, 23.8%, and 27.6% for center I; 47.2%, 20.0%, and 22.8% for center II; and 42.3%, 27.2%, and 30.1% for center III, respectively. Pathology confirmation rate ranged from 81.6% to 88.0% across three centers. AIR and CDR for biopsy-naive patients were 45.5% to 52.6% and 24.2% to 33.5% across three centers, respectively, and those for previous negative biopsy were 27.2% to 39.8% and 11.7% to 14.2% across three centers, respectively.

CONCLUSION

We reported CDR and AIR in prostate MRI for clinical suspicion of PCa. CDR needs to be adjusted for pathology confirmation rate and pre-MRI biopsy status for interfacility comparison.

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.

PI-RADS upgrading as the strongest predictor for the presence of clinically significant prostate cancer in patients with initial PI-RADS-3 lesions

PURPOSE

Unclear lesions on multiparametric magnetic resonance tomography (mpMRI) are challenging for the indication of biopsy in patients with clinical suspicion of prostate cancer (PCa). The aim of this study is the validation of the detection rate of clinically significant PCa (csPCa) in patients with PI-RADS 3 findings and to determine the appropriate follow-up strategy.

METHODS

In this retrospective single-center study, patients with maximum PI-RADS 3 lesions underwent targeted MRI/ultrasound-fusion biopsy (tPbx) combined with systematic 12-core biopsy (sPbx) and follow-up mpMRI with further control biopsy. We assessed the evolution of MRI findings (PI-RADS, volume of the lesion), clinical parameters and histopathology in follow-up MRI and biopsies. The primary objective is the detection rate of csPCa, defined as ISUP ≥ 2 findings.

RESULTS

A total of 126 patients (median PSA 6.65 ng/ml; median PSA-density (PSAD) 0.13 ng/ml2) were included. The initial biopsy identified low-risk PCa in 24 cases (19%). During follow-up biopsy, 22.2% of patients showed PI-RADS upgrading (PI-RADS > 3), and 29 patients (23%) exhibited a tumor upgrading. Patients with PI-RADS upgrading had a higher risk of csPCa compared to those without PI-RADS upgrading (42.9% vs. 9.18%, p < 0.05). PI-RADS upgrading was identified as an independent predictor for csPCa in follow-up biopsy (OR 16.20; 95% CI 1.17-224.60; p = 0.038).

CONCLUSION

Patients with stable PI-RADS 3 findings may not require a follow-up biopsy. Instead, it is advisable to schedule an MRI, considering that PI-RADS upgrading serves as an independent predictor for csPCa.

Effects of the lesion size on clinically significant prostate cancer detection rates in PI-RADS category 3-5 lesions

INTRODUCTION

Prostate cancer (PCa) ranks second among prevalent cancers in men, necessitating effective screening tools such as multiparametric magnetic resonance imaging (mpMRI) with the prostate imaging reporting and data system (PI-RADS) classification. This study explores the impact of lesion volume on clinically significant prostate cancer (csPCa) detection rates in PI-RADS 3-5 lesions, aiming to contribute insights into the underexplored relationship between lesion size and csPCa detection.

MATERIALS AND METHODS

A retrospective analysis was conducted on data from 754 patients undergoing mpMRI-guided transrectal ultrasound (TRUS) prostate biopsy between January 2016 and 2023. Patients with PI-RADS 3, 4, and 5 lesions were included. Lesion size and PI-RADS categories were assessed through mpMRI, followed by MR fusion biopsy.

RESULTS

Of the patients, 33.7%, 52.3%, and 14.1% had PI-RADS 3, 4, and 5 lesions, respectively. Lesion sizes correlated significantly with csPCa detection in PI-RADS 4 and 5 categories. For PI-RADS 3 lesions, no significant differences in csPCa rates were observed based on lesion size. However, in PI-RADS 4 and 5 groups, larger lesions showed higher csPCa rates.

CONCLUSION

This study suggests that subgroup categorizations based on lesion volume could predict clinically significant PCa with high accuracy, potentially reducing unnecessary biopsies and associated overtreatment. Future research should further explore the relationship between lesion size and csPCa, clarifying discussions regarding the inclusion of systematic biopsies in diagnostic protocols.

Utility of Prostate Health Index Density for Biopsy Strategy in Biopsy-Naïve Patients With PI-RADS v2.1 Category 3 Lesions

BACKGROUND

Category 3 lesions in PI-RADSv2.1 pose diagnostic challenges, complicating biopsy decisions. Recent biomarkers like prostate health index (PHI) have shown higher specificity in detecting clinically significant prostate cancer (csPCa) than prostate-specific antigen (PSA). Yet their integration with MRI remains understudied.

PURPOSE

To evaluate the utility of PSA and PHI with its derivatives for detecting csPCa in biopsy-naïve patients with category 3 lesion on initial prostate MRI scan.

STUDY TYPE

Retrospective.

POPULATION

One hundred ninety-three biopsy-naïve patients who underwent MRI, PSA, and PHI testing, followed by both targeted and systematic biopsies.

FIELD STRENGTH/SEQUENCE

Turbo spin-echo T2-weighted imaging, diffusion-weighted single-shot echo-planar imaging, and dynamic contrast-enhanced T1-weighted fast field echo sequence imaging in 3 T.

ASSESSMENT

PHI density (PHID) and PSA density (PSAD) derived by dividing serum PHI and PSA with prostate volume (MRI based methodology suggested by PI-RADSv2.1). Risk-stratified models to evaluate the utility of markers in triaging patients for biopsy, including low-, intermediate-, and high-risk groups.

STATISTICAL TESTS

Independent t-test, Mann-Whitney U test, Mantel-Haenszel test, generalized estimating equation, and receiver operating characteristic (ROC) curve analysis were used. Statistical significance defined as P < 0.05.

RESULTS

CsPCa was found in 16.6% (32/193) of patients. PHID had the highest area under the ROC curve (AUROC) of 0.793, followed by PHI of 0.752, PSAD of 0.750, and PSA of 0.654. PHID with two cut-off points (0.88/mL and 1.82/mL) showed the highest potential biopsy avoidance of 47.7% (92/193) with 5% missing csPCa, and the lowest intermediate-risk group (borderline decision group) at 38.9% (75/193), compared to PSA and PHI.

DATA CONCLUSION

PHID demonstrated better potential in triaging patients with category 3 lesions, possibly aiding more selective and confident biopsy decisions for csPCa detection, than traditional markers.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 5.

Prostagram magnetic resonance imaging in a screening population: Prostate Imaging-Reporting and Data System or Likert?

OBJECTIVE

To compare biopsy recommendation rates and accuracy of the Prostate Imaging-Reporting and Data System, version 2 (PI-RADSv2) with the Likert scale for detection of clinically significant and insignificant prostate cancer in men screened within the Imperial Prostate 1 Prostate Cancer Screening Trial Using Imaging (IP1-PROSTAGRAM).

PATIENTS AND METHODS

Men aged 50-69 years were screened with Prostagram MRI. Scans were prospectively reported using both PI-RADSv2 (excluding dynamic contrast-enhanced sequence score) and 5-point Likert scores by expert uro-radiologists. Systematic and targeted transperineal biopsy was recommended if the scan was scored ≥ 3, based on either reporting system. The proportion of patients recommended for biopsy and detection rates for Grade Groups (GGs) 1 and ≥ 2 were compared. Receiver operating characteristic (ROC) analysis was performed to compare performance.

RESULTS

A total of 406 men underwent Prostagram MRI. The median (interquartile range) age and prostate-specific antigen level were 57 (53-61) years and 0.91 (0.56-1.74) ng/mL, respectively. At MRI score ≥ 3, more patients were recommended for biopsy based on Likert criteria (94/406; 23%, 95% confidence interval [CI] 19.2%-27.6%) compared to PI-RADSv2 (72/406; 18%, 95% CI 14.2%-21.9%; P = 0.03). For MRI scores ≥ 4, PI-RADSv2 and Likert scales led to 43/406 (11%, 95% CI 7.9%-14.1%) and 35/406 (9%, 95% CI 6.2%-11.9%) men recommended for biopsy (P = 0.40). For GG ≥ 2 detection, PIRADSv2 and Likert detected 22% (95% CI 11.4%-30.8%, 14/72) and 16% (95% CI 9.5%-25.3%, 15/94), respectively (P = 0.56). For GG1 cancers detection these were 11% (95% CI 4.3%-19.6%, seven of 72) vs 11% (95% CI 4.7%-17.8%, nine of 94; P = 1.00). The accuracy of PI-RADSv2 and Likert scale was similar (area under the ROC curve 0.64 vs 0.65, P = 0.95).

CONCLUSIONS

In reporting non-contrast-enhanced Prostagram MRI in a screening population, the PI-RADSv2 and Likert scoring systems were equally accurate; however, Likert scale use led to more men undergoing biopsy without a subsequent increase in significant cancer detection rates. To improve reporting of Prostagram MRI, either the PI-RADSv2 or a modified Likert scale or a standalone scoring system should be developed.

PI-RADS Version 2.0 Versus Version 2.1: Comparison of Prostate Cancer Gleason Grade Upgrade and Downgrade Rates From MRI-Targeted Biopsy to Radical Prostatectomy

BACKGROUND. Precise risk stratification through MRI/ultrasound (US) fusion-guided targeted biopsy (TBx) can guide optimal prostate cancer (PCa) management. OBJECTIVE. The purpose of this study was to compare PI-RADS version 2.0 (v2.0) and PI-RADS version 2.1 (v2.1) in terms of the rates of International Society of Urological Pathology (ISUP) grade group (GG) upgrade and downgrade from TBx to radical prostatectomy (RP). METHODS. This study entailed a retrospective post hoc analysis of patients who underwent 3-T prostate MRI at a single institution from May 2015 to March 2023 as part of three prospective clinical trials. Trial participants who underwent MRI followed by MRI/US fusion-guided TBx and RP within a 1-year interval were identified. A single genitourinary radiologist performed clinical interpretations of the MRI examinations using PI-RADS v2.0 from May 2015 to March 2019 and PI-RADS v2.1 from April 2019 to March 2023. Upgrade and downgrade rates from TBx to RP were compared using chi-square tests. Clinically significant cancer was defined as ISUP GG2 or greater. RESULTS. The final analysis included 308 patients (median age, 65 years; median PSA density, 0.16 ng/mL2). The v2.0 group (n = 177) and v2.1 group (n = 131) showed no significant difference in terms of upgrade rate (29% vs 22%, respectively; p = .15), downgrade rate (19% vs 21%, p = .76), clinically significant upgrade rate (14% vs 10%, p = .27), or clinically significant downgrade rate (1% vs 1%, p > .99). The upgrade rate and downgrade rate were also not significantly different between the v2.0 and v2.1 groups when stratifying by index lesion PI-RADS category or index lesion zone, as well as when assessed only in patients without a prior PCa diagnosis (all p > .01). Among patients with GG2 or GG3 at RP (n = 121 for v2.0; n = 103 for v2.1), the concordance rate between TBx and RP was not significantly different between the v2.0 and v2.1 groups (53% vs 57%, p = .51). CONCLUSION. Upgrade and downgrade rates from TBx to RP were not significantly different between patients whose MRI examinations were clinically interpreted using v2.0 or v2.1. CLINICAL IMPACT. Implementation of the most recent PI-RADS update did not improve the incongruence in PCa grade assessment between TBx and surgery.

Risk factors for prostate cancer in men with false-negative mpMRI: A retrospective single center cohort study of image quality scores and clinical parameters

PURPOSE

To identify predictors of prostate cancer (PCa) and clinically significant prostate cancer (csPCa) in men with prior false-negative multiparametric MRI (mpMRI), focusing on image quality scoring systems and clinical parameters.

METHODS

In this IRB-approved retrospective single-center study, patients with a negative mpMRI (PI-RADS score ≤2) and subsequent prostate biopsies were included. Histopathological results served as reference standard. Welch's t-Test was conducted to identify significant differences in image quality scores (PI-QUAL and PSHS) between patients with and without PCa/csPCA. In addition, clinical parameters (age, BMI, PSA density) and image quality scores (PI-QUAL and PSHS) were examined as potential predictors of PCa/csPCa detection after a false-negative mpMRI in uni- and multivariate analyses.

RESULTS

Among 96 patients with negative mpMRI results, 44.8 % had PCa and 16.7 % had csPCa upon biopsy with histopathological confirmation. PI-QUAL scores were significantly lower in patients with PCa (p = 0.03) and csPCa (p = 0.005). PSHS scores were lower in patients with csPCa, but the difference was not statistically significant (p = 0.1). Higher age (p = 0.035) and a lower PI-QUAL score (p < 0.004) were predictors of subsequent csPCa detection upon biopsy, however, a lower PI-QUAL score was the only independent predictor of missed csPCa in false-negative mpMRIs.

CONCLUSIONS

Lower image quality scores were associated with missed PCa/csPCa in patients with false-negative mpMRIs, with PI-QUAL being an independent predictor of failed csPCa detection. This highlights the importance of image quality for prostate MRI and advocats the inclusion of its measurement into the standardized report.

Interpretation of Prostate Magnetic Resonance Imaging Using Prostate Imaging and Data Reporting System Version 2.1: A Primer

Prostate magnetic resonance imaging (MRI) is increasingly being used to diagnose and stage prostate cancer. The Prostate Imaging and Data Reporting System (PI-RADS) version 2.1 is a consensus-based reporting system that provides a standardized and reproducible method for interpreting prostate MRI. This primer provides an overview of the PI-RADS system, focusing on its current role in clinical interpretation. It discusses the appropriate use of PI-RADS and how it should be applied by radiologists in clinical practice to assign and report PI-RADS assessments. We also discuss the changes from prior versions and published validation studies on PI-RADS accuracy and reproducibility.

Comparison of Targeted Biopsy and Combined Biopsy to Avoid Unnecessary Systematic Biopsy in Patients with PI-RADS 5 Lesions

PURPOSE

To evaluate the detection rates of prostate cancer (PCa) and clinically significant prostate cancer (CSPCa) detection via target biopsy (TB), systematic biopsy (SB), and combined biopsy (CB) in patients with PI-RADS 5 lesions.

METHODS

Patients with at least one PI-RADS 5 lesion were retrospectively enrolled in a prospectively collected database. The patients underwent multiparametric magnetic resonance imaging (mpMRI) followed by transrectal TB of PI-RADS 5 lesions and SB. The PCa and CSPCa detection rates and cores of TB and SB were compared with those of CB.

RESULTS

In 585 patients, prostate biopsy revealed PCa in 560 cases (95.73%) and CSPCa in 549 cases (93.85%). PCa was detected in T2 patients (93.13%, 217/233) and in T3/4 patients (97.44%, 343/352). CSPCa was detected in T2 patients (89.27%, 208/233) and in T3/4 patients (96.87%, 341/352). The positive rates of TB for T2/3/4, T3/4, and T2 were 94.02%, 96.21%, and 90.56%, respectively. SB added 1.71% (10/585) PCa and 1.37% (8/585) CSPCa detection to TB. There was no difference between TB and SB in detecting different stages of cancer (p > 0.05). In the biopsy core analysis, TB had fewer biopsy cores and a higher detection rate than SB (all p < 0.05).

CONCLUSIONS

In patients with PI-RADS score 5 lesions, TB can achieve the same detection rate as, with fewer biopsy cores than, CB. SB adds minimal clinical value and can be omitted for these patients.

Development and validation of a nomogram for predicting prostate cancer based on combining contrast-enhanced transrectal ultrasound and biparametric MRI imaging

Objectives

This study was to explore the feasibility of combining contrast-enhanced transrectal ultrasound (CE-TRUS) with biparametric MRI (CEUS-BpMRI) score for diagnosing prostate cancer (PCa).

Methods

A total of 183 patients with suspected PCa who underwent multiparametric MRI (Mp-MRI) and CE-TRUS were included. CEUS-BpMRI score was developed based on the results of Mp-MRI and CE-TRUS. The diagnostic performance was evaluated by the area under the curve (AUC). The diagnostic efficacy of the CEUS-BpMRI score, BpMRI score, and PI-RADS v2.1 score were compared. Total patients were randomly assigned to a training cohort (70%) or validation cohort (30%). A nomogram was constructed based on univariate and multivariate logistic regression. The model was evaluated by AUC and calibration curve.

Results

The diagnostic performance of CEUS-BpMRI score (AUC 0.857) was comparable to that of PI-RADS v2.1 (AUC 0.862) (P = 0.499), and both were superior to Bp-MRI score (AUC 0.831, P < 0.05). In peripheral zone lesions with Bp-MRI score of 3, there was no statistically significant difference between PI-RADS v2.1 score (AUC 0.728) and CEUS-BpMRI score (AUC 0.668) (P = 0.479). Multivariate analysis showed that age, total prostate specific antigen/free prostate specific antigen (F/T), time to peak (TTP), and CEUS-BpMRI score were independent factors. The AUC of the nomogram was 0.909 in the training cohort and 0.914 in the validation cohort.

Conclusions

CEUS-BpMRI score has high diagnostic efficacy for diagnosing PCa. A nomogram model established by combining age, F/T, TTP, and CEUS-BpMRI score can achieve the best predictive accuracy for PCa.

Exploration of the diagnostic capacity of PSAMR combined with PI-RADS scoring for clinically significant prostate cancer and establishment and validation of the Nomogram prediction model

PURPOSE

The objective of this investigation was to explore the diagnostic capability of Prostate Specific Antigen Mass Ratio (PSAMR) combined with Prostate Imaging Reporting and Data System (PI-RADS) scoring for clinically significant prostate cancer (CSPC), develop and validate a Nomogram prediction model for the probability of prostate cancer occurrence in patients who have not undergone prostate biopsy.

METHODS

Initially, we retrospectively collected clinical and pathological data of patients who underwent trans-perineal prostate puncture at Yijishan Hospital of Wanan Medical College from July 2021 to January 2023. Through logistic univariate and multivariate regression analysis, independent risk factors for CSPC were determined. Receiver Operating Characteristic (ROC) curves were generated to compare the ability of different factors for diagnosis of CSPC. Then, we split the dataset into a training set and validation set, compared their heterogeneity, and developed a Nomogram prediction model based on the training set. Finally, we validated the Nomogram prediction model in terms of discrimination, calibration, and clinical usefulness.

RESULTS

Logistic multivariate regression analysis illustrated that age [64-69 (OR = 2.736, P = 0.029); 69-75 (OR = 4.728, P = 0.001); > 75 (OR = 11.344, P < 0.001)], PSAMR [0.44-0.73 (OR = 4.144, P = 0.028); 0.73-1.64(OR = 13.022, P < 0.001); > 1.64(OR = 50.541, P < 0.001)], and PI-RADS score [4 points (OR = 7.780, P < 0.001); 5 points (OR = 24.533, P < 0.001)] were independent risk factors for CSPC. The Area Under the Curve (AUC) of the ROC curves of PSA, PSAMR, PI-RADS score, and PSAMR combined with PI-RADS score were respectively 0.797, 0.874, 0.889, and 0.928. The performance of PSAMR and PI-RADS score for diagnosis of CSPC was superior to PSA, but inferior to PSAMR combined with PI-RADS. Age, PSAMR, and PI-RADS were included in the Nomogram prediction model. The AUCs of the training set ROC curve and the validation set ROC curve were 0.943 (95% CI 0.917-0.970) and 0.878 (95% CI 0.816-0.940), respectively, in the discrimination validation. The calibration curve showed good consistency, and the decision analysis curve suggested the model had good clinical efficacy.

CONCLUSIONS

We found that PSAMR combined with PI-RADS scoring had a strong diagnostic capability for CSPC, and provided a Nomogram prediction model to predict the probability of prostate cancer occurrence combined with clinical data.

Development and validation of nomogram to improve the specificity of multiparametric MRI for clinically significant prostate cancer

OBJECTIVE

To develop and validate a nomogram to improve the specificity of prostate imaging reporting and data system (PI-RADS) on multiparametric magnetic resonance imaging (MRI) for clinically significant prostate cancer on targeted fusion biopsy.

METHODS

A retrospective review of patients who underwent fusion biopsy for PI-RADS 3-5 lesions using UroNav and Artemis systems between 2016 and 2022 was performed. Patients were divided into those with CS disease on fusion biopsy (Gleason grade group ≥2) versus those without. Multivariable analysis was used to identify variables associated with CS disease. A 100-point nomogram was constructed, and ROC curve was generated.

RESULTS

1485 lesions (1032 patients) were identified, 510 (34%) were PI-RADS 3, 586 (40%) were PI-RADS 4, and 389 (26%) were PI-RADS 5. Of these, 11% of PI-RADS 3, 39% of PI-RADS 4, and 61% of PI-RADS 5 showed CS disease. CS disease was associated with older age (OR 1.04, 95% CI 1.02-1.06, p < 0.01), previous negative biopsy (OR 0.52, 95% CI 0.36-0.74, p < 0.01), presence of multiple PI-RADS 3-5 lesions (OR 0.61, 95% CI 0.45-0.83, p < 0.01), peripheral zone location (OR 1.88, 95% CI 1.30-2.70, p < 0.01), PSA density (OR 1.48 per 0.1 unit, 95% CI 1.33-1.64, p < 0.01), PI-RADS score 4 (OR 3.28, 95% CI 2.21-4.87, p < 0.01), and PI-RADS score 5 (OR 7.65, 95% CI 4.93-11.85, p < 0.01). Area under ROC curve was 82% for nomogram compared to 75% for PI-RADS score alone.

CONCLUSION

We report a nomogram that combines PI-RADS score with other clinical parameters. The nomogram outperforms PI-RADS score for the detection of CS prostate cancer.

Prostate cancer screening-stepping forward with MRI

OBJECTIVE

To comprehensively review the literature on the integration of MRI as a diagnostic tool in prostate cancer screening and offer practical recommendations for optimising its use.

METHODS

Existing research studies, clinical guidelines and expert opinions were reviewed to support the optimisation standards for MRI use in screening. Consolidated screening principles were used to make appropriate recommendations regarding the integration of MRI into the diagnostic pathway.

RESULTS

To strike a balance between the potential benefits of early detection on mortality and minimising the harm of over-diagnosing indolent cancers, it is necessary to have a clear understanding of the context of MRI use. The key to optimisation is patient selections and MRI-targeted biopsies. For men at higher-than-average risk, it is essential to use screening-specific MRI protocols and establish accuracy levels and interpretation criteria. Optimisation of readings by the automation of data acquisition, image quality monitoring, post-processing, radiologist certification and deep-learning computer-aided software is needed. The optimal utilisation of MRI involves its integration into a multistep diagnostic pathway, supported by a quality-assured and cost-effective infrastructure that ensures community-wide access to imaging.

CONCLUSION

MRI in the prostate cancer screening pathway can bring substantial diagnostic benefits. By carefully considering its advantages, limitations and safety concerns and integrating it into a multistep diagnostic pathway, clinicians can improve outcomes while minimising harm to screening participants.

CLINICAL RELEVANCE STATEMENT

The manuscript discusses the role of MRI in prostate cancer screening, highlighting its potential to improve accuracy and reduce overdiagnosis. It emphasises the importance of optimising protocols and integrating MRI into a multistep diagnostic pathway for successfully delivering screening benefits.

KEY POINTS

• Population screening for prostate cancer is a new indication for prostate MRI that allows the detection of high-risk cancers while reducing the need for biopsies and associated harm. • To optimise prostate cancer screening using MRI, it is essential to redefine MRI protocols; establish accuracy levels, reliability and interpretation criteria; and optimise reading (including post-processing, image quality, radiologist certification, and deep-learning computer-aided software). • The optimal utilisation of MRI for prostate cancer screening would involve its integration into a multistep diagnostic pathway, supported by a quality-assured and cost-effective infrastructure that ensures community-wide access to imaging.

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.

Understanding tumor localization in multiparametric MRI of the prostate-effectiveness of 3D printed models

Introduction

Understanding tumor localization in multiparametric MRI (mpMRI) of the prostate is challenging for urologists but of great importance in mpMRI-fused prostate biopsy or radical prostatectomy. The aim was to evaluate the effectiveness of 3D printed models of the prostate to help urologists to locate tumors.

Methods and Participants

20 urologists from University Medical Center Mainz (Germany) were asked to plot the location of a cancer suspicious lesion (PI-RADS ≥ 4) on a total of 30 mpMRI on a prostate sector diagram. The following 3 groups (as matched triplets) were divided into: mpMRI only, mpMRI with radiological report and mpMRI with 3D printed model (scaled 1:1). Statistical analysis was performed using one-way and two-way ANOVA (with bonferroni post-test).

Results

Overall, localization of the suspicious lesion was superior with the radiological report (median of max. 10 [IQR]: MRI 2 [IQR 1;5], MRI + report: 8 [6.3;9], MRI + 3D model 3 [1.3;5.8]; p < 0.001). Residents with <1 year of experience had a significantly higher detection rate using a 3D printed model [5 (5;5.8)] compared to mpMRI alone [1.5 (1;3.5)] (p < 0.05). Regarding the estimation of index lesion extension, the 3D model showed a significant benefit (mean percentage difference [95% CI]: MRI alone 234% [17.1;451.5], MRI + report 114% [78.5;149.6], MRI + 3D model 17% [-7.4;41.3] (p < 0.01).

Conclusion

Urologists still need the written radiological report for a sufficient understanding of tumor localization. The effectiveness of the 3D printed model regarding tumor localization is particularly evident in young residents (<1 year) and leads to a better overall assessment of the tumor extension.

Association of quantifiable prostate MRI parameters with any and large cribriform pattern in prostate cancer patients undergoing radical prostatectomy

PURPOSE

Cribriform pattern has recently been recognized as an important independent risk factor for prostate cancer (PCa) outcome. This study aimed to identify the association of quantifiable prostate magnetic resonance imaging (MRI) parameters with any and large cribriform pattern at radical prostatectomy (RP) specimens.

METHODS

Preoperative prostate MRI's from 188 men undergoing RP between 2010 and 2018 were retrospectively acquired. RP specimens of the patients were revised for Gleason score (GS), and presence of any and large cribriform pattern. MRI parameters such as MRI visibility, PI-RADS score, lowest apparent diffusion coefficient (ADC) value, lesion size, and radiologic extra-prostatic extension (EPE) were reviewed. The association of prostate MRI parameters for presence of any and large cribriform pattern at RP was analysed using logistic regression.

RESULTS

116/188 (61.7%) PCa patients had any cribriform and 36/188 (19.1%) large cribriform pattern at RP. 171/188 (91.0%) men had MRI-visible lesions; 111/116 (95.7%) tumours with any and 36/36 (100%) with large cribriform pattern were visible at MRI. PCa with any and large cribriform pattern both had lower ADC values than those without (p < 0.001). In adjusted analysis, lowest ADC value was as an independent predictor for any cribriform (Odds Ratio (OR) 0.2, 95% Confidence Interval (CI) 0.1-0.8; p = 0.01) and large cribriform pattern (OR 0.2, 95% CI 0.1-0.7; p = 0.01), while other parameters were not.

CONCLUSIONS

The majority of PCa with cribriform pattern at RP were visible at MRI, and lowest ADC value was an independent predictor for both any and large cribriform pattern.

Effectiveness and Accuracy of MRI-Ultrasound Fusion Targeted Biopsy Based on PI-RADS v2.1 Category in Transition/Peripheral Zone of the Prostate

BACKGROUND

MRI-ultrasound fusion targeted biopsy (MRI-TBx) improves the clinically significant prostate cancer (csPCa) detection with fewer cores. However, whether systematic biopsy-guided by transrectal ultrasound (TRUS-SBx) can be omitted when undergoing MRI-TBx in transition zone (TZ) and peripheral zone (PZ) remains unclear.

PURPOSE

To assess the performance and effectiveness of MRI-TBx based on PI-RADS v2.1 for csPCa diagnosis in TZ and PZ, respectively.

STUDY TYPE

Retrospective.

SUBJECTS

A total of 309 selected cases (median age 70 years) with 356 lesions who underwent both MRI-TBx and TRUS-SBx were enrolled.

FIELD STRENGTH/SEQUENCE

A 3.0 T, multiparametric MRI (mp-MRI) including T2-weighted turbo-spin echo imaging (T2WI), diffusion-weighted spin-echo echo planar imaging (DWI), dynamic contrast-enhanced MRI with time-resolved T1-weighted imaging (DCE).

ASSESSMENT

Mp-MRI was assessed by two radiologists using PI-RADS v2.1. The csPCa detection rates provided by MRI-TBx, TRUS-SBx and combined biopsy in TZ and PZ were calculated, respectively.

STATISTICAL TESTS

McNemar test was used to compare the csPCa detection rates in TZ and PZ, respectively. The frequencies and distribution of all detected prostate cancers by different biopsy methods were also compared. P < 0.05 was considered statistically significant.

RESULTS

Among 356 lesions in 309 patients, 208 (68 in TZ, 140 in PZ) were pathologically confirmed as csPCa. In TZ, there were significant differences for csPCa detection with PI-RADS 3 between combined biopsy and TRUS-SBx (23.5% vs. 15.3%), MRI-TBx (23.5% vs. 16.3%), respectively. MRI-TBx detected 23% (19/83) cases missed by TRUS-SBx in which 68% (13/19) were csPCa. In PZ, there were no statistical differences between MRI-TBx and combined biopsy with PI-RADS 3-5 (P = 0.21, 0.25, 0.07, respectively). In 9% (14/152) cases only detected by MRI-TBx, 86% (12/14) were clinically significant. Five percent (7/152) of cases only detected by TRUS-SBx were completely nonclinically significant.

DATA CONCLUSION

MRI-TBx played a positive role on csPCa diagnosis in TZ, but combined biopsy might be the best choice especially in the subgroup PI-RADS 3. In PZ, MRI-TBx had an advantage over TRUS-SBx for csPCa detection.

EVIDENCE LEVEL

2.

TECHNICAL EFFICACY

Stage 2.

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.

Early Detection of Prostate Cancer: AUA/SUO Guideline Part II: Considerations for a Prostate Biopsy

PURPOSE

The summary presented herein covers recommendations on the early detection of prostate cancer and provides a framework to facilitate clinical decision-making in the implementation of prostate cancer screening, biopsy, and follow-up. This is Part II of a two-part series focusing on initial and repeat biopsies, and biopsy technique. Please refer to Part I for discussion of initial prostate cancer screening recommendations.

MATERIALS AND METHODS

The systematic review utilized to inform this guideline was conducted by an independent methodological consultant. The systematic review was based on searches in Ovid MEDLINE and Embase and Cochrane Database of Systematic Reviews (January 1, 2000-November 21, 2022). Searches were supplemented by reviewing reference lists of relevant articles.

RESULTS

The Early Detection of Prostate Cancer Panel developed evidence- and consensus-based guideline statements to provide guidance in prostate cancer screening, initial and repeat biopsies, and biopsy technique.

CONCLUSIONS

The evaluation of prostate cancer risk should be focused on the detection of clinically significant prostate cancer (Grade Group 2 or higher [GG2+]). The use of laboratory biomarkers, prostate MRI, and biopsy techniques described herein may improve detection and safety when a prostate biopsy is deemed necessary following prostate cancer screening.

Quantification of cross-vendor variation in ADC measurements in vendor-specific prostate MRI-protocols

PURPOSE

The purpose of this study was to quantify the variability of Apparent Diffusion Coefficient (ADC) and test if there were statistically significant differences in ADC between MRI systems and sequences.

METHOD

With a two-chamber cylindrical ADC phantom with fixed ADC values (1,000 and 1,600x10^-6 mm²/s) a single-shot (ss) Echo Planar Imaging (EPI), a multi-shot EPI, a reduced field of view DWI (zoom) and a Turbo Spin Echo DWI sequence were tested in six MRI systems from three vendors at 1.5 T and 3 T. Technical parameters were according to Prostate Imaging Reporting and Data System Version 2.1. ADC maps were calculated by vendor specific algorithms. Absolute and relative differences in ADC from the phantom-ADC were calculated and differences between sequences were tested.

RESULTS

At 3 T absolute differences from phantom given ADC (∼1,000 and ∼ 1,600x10^-6 mm²/s) were -83 - 42x10^-6 mm²/s (-8.3%-4.2%) and -48 - 15x10^-6 mm²/s (-3%-0.9%), respectively and at 1.5 T absolute differences were -81 - 26x10^-6 mm²/s (-2.6%-8.1%) and -74 - 67x10^-6 mm²/s (-4.6%-4.2%), respectively. Significant statistical differences in ADC measurements were identified between vendors in all sequences except for ssEPI and zoom at 3 T in the 1,600x10^-6 mm²/s phantom chamber. Significant differences were also identified between ADC measurements at 1.5 T and 3 T in some of the sequences and vendors, but not all.

CONCLUSION

The variation of ADC between different MRI systems and prostate specific DWI sequences is limited in this phantom study and without apparent clinical relevance. However, prospective multicenter studies of prostate cancer patients are needed for further investigation.

Study of iron metabolism based on T2* mapping sequences in PI-RADS 3 prostate lesions

Introduction

Prostate cancer is one of the most common malignant tumors in Chinese men, which is rich in iron metabolic activity and is closely related to all stages of prostate cancer progression. Since the current diagnostic methods are insufficient, we aimed to evaluate the value of quantitative T2 star values from the T2* mapping sequences in multiparametric magnetic resonance imaging (mpMRI) in the diagnosis and grading of PI-RADS 3 prostate cancer (PCa).

Methods

We prospectively enrolled patients with PCa or benign prostatic hyperplasia (BPH) admitted to our hospital from January 2021 to November 2022. Imaging indicators, including the T2* value and apparent diffusion coefficient (ADC) value, were collected, and enzyme-linked immunosorbent assays (ELISAs) were used to measure the levels of proteins involved in iron metabolism in the patients. ROC curves were drawn to explore whether the T2* value could be used for the diagnosis and grading of PCa.

Results

We found that three iron metabolism indexes, ferritin, hepcidin, and the ferric ion (Fe), and the T2* value were significantly different between the PCa group and BPH group and between the low International Society of Urology Pathology (ISUP) group (ISUP ≤ 2) and the high ISUP group (ISUP>2). Additionally, there was a significant correlation between the levels of these three indicators and the T2* value. Further ROC analysis showed that the levels of iron metabolism-related indexes and T2* values performed well in diagnosing and grading PCa.

Discussion

The T2* value has good value in detecting and predicting the grade of prostate cancer and can reflect the iron metabolism of the tumor, which could provide a foundation for the diagnosis and grading of PCa in the future.

Detection Rate of Prostate Cancer in Repeat Biopsy after an Initial Negative Magnetic Resonance Imaging/Ultrasound-Guided Biopsy

A negative multiparametric magnetic resonance imaging (mpMRI)-guided prostate biopsy in patients with suspected prostate cancer (PC) results in clinical uncertainty, as the biopsy can be false negative. The clinical challenge is to determine the optimal follow-up and to select patients who will benefit from repeat biopsy. In this study, we evaluated the rate of significant PC (sPC, Gleason score ≥7) and PC detection in patients who received a follow-up mpMRI/ultrasound-guided biopsy for persistent PC suspicion after a negative mpMRI/ultrasound-guided biopsy. We identified 58 patients at our institution that underwent repeat targeted biopsy in case of PI-RADS lesions and systematic saturation biopsy between 2014 and 2022. At the initial biopsy, the median age was 59 years, and the median prostate specific antigen level was 6.7 ng/mL. Repeat biopsy after a median of 18 months detected sPC in 3/58 (5%) patients and Gleason score 6 PC in 11/58 (19%). Among 19 patients with a downgraded PI-RADS score at the follow-up mpMRI, none had sPC. In conclusion, men with an initial negative mpMRI/ultrasound-guided biopsy had a high likelihood of not harboring sPC at repeat biopsy (95%). Due to the small size of the study, further research is recommended.

Bridging the experience gap in prostate multiparametric magnetic resonance imaging using artificial intelligence: A prospective multi-reader comparison study on inter-reader agreement in PI-RADS v2.1, image quality and reporting time between novice and expert readers

PURPOSE

The aim of the study was to determine the impact of using a semi-automatic commercially available AI-assisted software (Quantib® Prostate) on inter-reader agreement in PI-RADS scoring at different PI-QUAL ratings and grades of reader confidence and on reporting times among novice readers in multiparametric prostate MRI.

METHODS

A prospective observational study, with a final cohort of 200 patients undergoing mpMRI scans, was performed at our institution. An expert fellowship-trained urogenital radiologist interpreted all 200 scans based on PI-RADS v2.1. The scans were divided into four equal batches of 50 patients. Four independent readers evaluated each batch with and without the use of AI-assisted software, blinded to expert and individual reports. Dedicated training sessions were held before and after each batch. Image quality rated according to PI-QUAL and reporting times were recorded. Readers' confidence was also evaluated. A final evaluation of the first batch was conducted at the end of the study to assess for any changes in performance.

RESULTS

The overall kappa coefficient differences in PI-RADS scoring agreement without and with Quantib® were 0.673 to 0.736 for Reader 1, 0.628 to 0.483 for Reader 2, 0.603 to 0.292 for Reader 3 and 0.586 to 0.613 for Reader 4. Using PI-RADS ≥ 4 as cut-off for biopsy, the AUCs with AI ranged from 0.799 (95 % CI: 0.743, 0.856) to 0.820 (95 % CI: 0.765, 0.874). Inter-reader agreements at different PI-QUAL scores were higher with the use of Quantib, particularly for readers 1 and 4, with Kappa coefficient values showing moderate to slight agreement.

CONCLUSION

Quantib® Prostate could potentially be useful in improving inter-reader agreement among less experienced to completely novice readers if used as a supplement to PACS.

The use of 68 Ga-PSMA PET/CT to stratify patients with PI-RADS 3 lesions according to clinically significant prostate cancer risk

BACKGROUND

Prostate imaging reporting and data system (PI-RADS) category 3 lesions represent a "gray zone," having an equivocal risk of presenting as clinically significant prostate cancer (csPCa). ⁶⁸ Ga-labelled prostate-specific membrane antigen (⁶⁸ Ga-PSMA) positron emission tomography/computed tomography (PET/CT) has been identified as a diagnostic tool that can help to predict cases of primary PCa. We aimed to explore diagnostic value of ⁶⁸ Ga-PSMA PET/CT for csPCa in PI-RADS 3 lesions to aid in decision-making and avoid unnecessary biopsies.

METHODS

A total of 78 men with PI-RADS 3 lesions who underwent both ⁶⁸ Ga-PSMA PET/CT and transrectal ultrasound/magnetic resonance imaging (MRI) fusion-guided biopsy were enrolled. Images were analyzed by respective physicians who were blinded to the pathological results. Receiver operating characteristic (ROC) curve analysis and decision curve analysis were used to evaluate the diagnostic performance of univariate and multivariate analyses.

RESULTS

A total of 26/78 men had pathologically confirmed csPCa. A lower ADCT/ADCCLP (0.65 vs. 0.71, p = 0.018), smaller prostate volume (25.27 vs. 42.79 ml, p < 0.001), lower free prostate-specific antigen/total prostate-specific antigen (0.11 vs. 0.16, p < 0.001), higher PSA level (13.45 vs. 7.90 ng/ml, p = 0.001), higher PSA density (0.40 vs. 0.16 ng/ml² , p < 0.001), higher SUVmax (9.80 vs. 4.40, p < 0.001) and SUVT/BGp (2.41 vs. 1.00, p < 0.001) were associated with csPCa. ROC analysis illustrated the improvement in SUVmax and SUVT/BGp compared with all independent and combined clinical features as well as multiparametric magnetic resonance imaging (mpMRI) features for csPCa detection. The net benefits of SUVmax and SUVT/BGp were superior to those of other features, respectively. With cutoff values of 5.0 for SUVmax and 1.4 for SUVT/BGp, the diagnostic sensitivity and specificity for csPCa were 96.2%, 100% and 80.8%, 84.6%, respectively.

CONCLUSION

⁶⁸ Ga-PSMA PET/CT is potentially capable of stratifying men with PI-RADS 3 lesions according to the presence of csPCa and has better performance than the model established based on clinical and mpMRI features.

Intraobserver and Interobserver Agreement between Six Radiologists Describing mpMRI Features of Prostate Cancer Using a PI-RADS 2.1 Structured Reporting Scheme

Clinical practice has revealed ambiguities in PI-RADS v2.1 scoring, but a limited number of studies are available that validate the interreader and intrareader reproducibility of the mpMRI PI-RADS lexicon. We decomposed the PI-RADS rules into a set of common data elements to evaluate the inter- and intraobserver agreement in assessing the individual features included in the PI-RADS lexicon. Six radiologists (three highly experienced, three less experienced) in two sessions independently read thirty-two lesions in the peripheral and transition zone using the structured reporting tool, blinded to clinical MRI indication. The highest agreement between radiologists was observed for the abnormality detection, the evaluation of the type of signal intensity, and the characteristic of benign prostatic hyperplasia. Moderate agreement was reported for dynamic contrast-enhanced images. This resulted in a decrease in abnormality detection (PA = 76.5%) and enhancement indication (PA = 77.3%). The lowest agreement was observed for highly subjective features: shape, signal intensity level, and type of lesion margins. The results indicate the limitations of the PI-RADS v2.1 lexicon in relation to interreader and intrareader reproducibility. We have demonstrated that it is possible to develop structured reporting systems standardized according to the PI-RADS lexicon.

Sub-differentiation of PI-RADS 3 lesions in TZ by advanced diffusion-weighted imaging to aid the biopsy decision process

Background

Performing biopsy for intermediate lesions with PI-RADS 3 has always been controversial. Moreover, it is difficult to differentiate prostate cancer (PCa) and benign prostatic hyperplasia (BPH) nodules in PI-RADS 3 lesions by conventional scans, especially for transition zone (TZ) lesions. The purpose of this study is sub-differentiation of transition zone (TZ) PI-RADS 3 lesions using intravoxel incoherent motion (IVIM), stretched exponential model, and diffusion kurtosis imaging (DKI) to aid the biopsy decision process.

Methods

A total of 198 TZ PI-RADS 3 lesions were included. 149 lesions were BPH, while 49 lesions were PCa, including 37 non-clinical significant PCa (non-csPCa) lesions and 12 clinical significant PCa (csPCa) lesions. Binary logistic regression analysis was used to examine which parameters could predict PCa in TZ PI-RADS 3 lesions. The ROC curve was used to test diagnostic efficiency in distinguishing PCa from TZ PI-RADS 3 lesions, while one-way ANOVA analysis was used to examine which parameters were statistically significant among BPH, non-csPCa and csPCa.

Results

The logistic model was statistically significant (χ2 = 181.410, p<0.001) and could correctly classify 89.39% of the subjects. Parameters of fractional anisotropy (FA) (p=0.004), mean diffusion (MD) (p=0.005), mean kurtosis (MK) (p=0.015), diffusion coefficient (D) (p=0.001), and distribute diffusion coefficient (DDC) (p=0.038) were statistically significant in the model. ROC analysis showed that AUC was 0.9197 (CI 95%: 0.8736-0.9659). Sensitivity, specificity, positive predictive value and negative predictive value were 92.1%, 80.4%, 93.9% and 75.5%, respectively. FA and MK of csPCa were higher than those of non-csPCa (all p<0.05), while MD, ADC, D, and DDC of csPCa were lower than those of non-csPCa (all p<0.05).

Conclusion

FA, MD, MK, D, and DDC can predict PCa in TZ PI-RADS 3 lesions and inform the decision-making process of whether or not to perform a biopsy. Moreover, FA, MD, MK, D, DDC, and ADC may have ability to identify csPCa and non-csPCa in TZ PI-RADS 3 lesions.

Avoiding Unnecessary Systematic Biopsy in Clinically Significant Prostate Cancer: Comparison Between MRI-Based Radiomics Model and PI-RADS Category

BACKGROUND

MRI-targeted biopsy (MRTB) improves the clinically significant prostate cancer (csPCa) detection rate with fewer biopsy cores in men with suspected PCa. However, whether concurrent systematic biopsy (SB) can be avoided in patients undergoing MRTB remains unclear.

PURPOSE

To evaluate the potential value of MRI-based radiomics models in avoiding unnecessary SB in biopsy-naïve patients.

STUDY TYPE

Retrospective.

POPULATION

A total of 226 patients (mean age 66.6 ± 9.02 years) with suspicion of PCa (PI-RADS score ≥ 3) and received combined cognitive MRTB with SB were retrospectively recruited and randomly divided into training (N = 180) and test (N = 46) cohorts at an 8:2 ratio.

FIELD STRENGTH/SEQUENCE

A 3.0 T, biparametric MRI (bpMRI) including T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) map.

ASSESSMENT

The whole prostate gland (PG) and the index lesion (IL) were delineated. Three radiomics models of bpMRI_PG , bpMRI_IL , and bpMRI_PG+IL were constructed, respectively, and the performance of each radiomics model was compared with that of PI-RADS assessment.

STATISTICAL TESTS

The least absolute shrinkage and selection operator (LASSO) regression method was used to select texture features. The area under the curve (AUC) and decision curve analysis were used to estimate the models.

RESULTS

The bpMRI_PG+IL radiomics model exhibited good discrimination, calibration, and net benefits, which would reduce the SB biopsy in 71.2% and 71.4% of men with PI-RADS ≥ 5 lesions in the training and test cohorts, respectively.

DATA CONCLUSION

A bpMRI_PG+IL radiomics model may outperform PI-RADS category in help reducing unnecessary SB in biopsy-naïve patients.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 6.

The diagnostic performance in clinically significant prostate cancer with PI-RADS version 2.1: simplified bpMRI versus standard mpMRI

OBJECTIVES

To compare the diagnostic performance for the detection of clinically significant prostate cancer (csPCa) between bpMRI with only axial T2WI (simplified bpMRI) and standard-multiparametric MRI (mpMRI).

METHODS

A total of 569 patients who underwent mpMRI followed by biopsy or prostatectomy were enrolled in this retrospective study. According to PI-RADS v2.1, three radiologists (A, B, C) from three centers blinded to clinical variables were assigned scores on lesions with simplified bpMRI and then with mpMRI 2 weeks later. Diagnostic performance of simplified bpMRI was compared with mpMRI using histopathology as reference standard.

RESULTS

For all the three radiologists, the diagnostic sensitivity was significantly higher with mpMRI than with simplified bpMRI (P < 0.001 to P = 0.035); and although specificity was also higher with mpMRI than with simplified bpMRI for radiologist B and radiologist C, it was statistically significant only for radiologist B (P = 0.011, P = 0.359, respectively). On the contrary, for radiologist A, specificity was higher with simplified bpMRI than with mpMRI (P = 0.001). The area under the receiver operating characteristic curve (AUC) was significantly higher for mpMRI than for simplified bpMRI except for radiologist A (radiologist A: 0.903 vs 0.913, P = 0.1542; radiologist B: 0.861 vs 0.834 P = 0.0013; and radiologist C: 0.884 vs 0.848, P = 0.0003). Interobserver reliability of PI-RADS v2.1 showed good agreement for both simplified bpMRI (kappa = 0.665) and mpMRI (kappa = 0.739).

CONCLUSION

Although the detection of csPCa with simplified bpMRI was comparatively lower than that with mpMRI, the diagnostic performance was still high in simplified bpMRI. Our data justify using mpMRI outperforms simplified bpMRI for prostate cancer screening and imply simplified bpMRI as a potential screening tool.

A Faster Prostate MRI: Comparing a Novel Denoised, Single-Average T2 Sequence to the Conventional Multiaverage T2 Sequence Regarding Lesion Detection and PI-RADS Score Assessment

BACKGROUND

The T₂ w sequence is a standard component of a prostate MRI examination; however, it is time-consuming, requiring multiple signal averages to achieve acceptable image quality.

PURPOSE/HYPOTHESIS

To determine whether a denoised, single-average T₂ sequence (T₂ -R) is noninferior to the standard multiaverage T₂ sequence (T₂ -S) in terms of lesion detection and PI-RADS score assessment.

STUDY TYPE

Retrospective.

POPULATION

A total of 45 males (age range 60-75 years) who underwent clinically indicated prostate MRI examinations, 21 of whom had pathologically proven prostate cancer.

FIELD STRENGTH/SEQUENCE

A 3 T; T₂ w FSE, DWI with ADC maps, and dynamic contrast-enhanced images with color-coded perfusion maps. T₂ -R images were created from the raw data utilizing a single "average" with iterative denoising.

ASSESSMENT

Nine readers randomly assessed complete exams including T₂ -R and T₂ -S images in separate sessions. PI-RADS version 2.1 was used. All readers then compared the T₂ -R and T₂ -S images side by side to evaluate subjective preference. An additional detailed image quality assessment was performed by three senior level readers.

STATISTICAL TESTS

Generalized linear mixed effects models for differences in lesion detection, image quality features, and overall preference between T₂ -R and T₂ -S sequences. Intraclass correlation coefficients (ICC) were used to assess reader agreement for all comparisons. A significance threshold of P = 0.05 was used for all statistical tests.

RESULTS

There was no significant difference between sequences regarding identification of lesions with PI-RADS ≥3 (P = 0.10) or PI-RADS score (P = 0.77). Reader agreement was excellent for lesion identification (ICC = 0.84). There was no significant overall preference between the two sequences regarding image quality (P = 0.07, 95% CI: [-0.23, 0.01]). Reader agreement was good regarding sequence preference (ICC = 0.62).

DATA CONCLUSION

Use of single-average, denoised T₂ -weighted images was noninferior in prostate lesion detection or PI-RADS scoring when compared to standard multiaverage T₂ -weighted images.

EVIDENCE LEVEL

3.

TECHNICAL EFFICACY

Stage 3.

Diagnostic performance of transperineal prostate targeted biopsy alone according to the PI-RADS score based on bi-parametric magnetic resonance imaging

Purpose

To compare the diagnostic performance of transperineal targeted biopsy (TB) or systematic biopsy (SB) alone based on combined TB+SB and radical prostatectomy (RP) specimen for detecting prostate cancer (PCa) according to the prostate imaging reporting and data system (PI-RADS) score.

Materials and methods

This study included 1077 men who underwent transperineal bi-parametric (bp) magnetic resonance imaging (MRI)-ultrasound (US) fusion TB+SB (bpMRI-US FTSB) between April 2019 and March 2022. To compare the performance of each modality (TB, SB, and combined TB+SB) with the RP specimen (as the standard) for detecting PCa and clinically significant PCa (csPCa), receiver operating characteristic (ROC) curves were plotted.

Results

PCa was detected in 581 of 1077 men (53.9%) using bpMRI-US FTSB. CsPCa was detected in 383 of 1077 men (35.6%), 17 of 285 (6.0%) with PI-RADS 0 to 2, 35 of 277 (12.6%) with PI-RADS 3, 134 of 274 (48.9%) with PI-RADS 4, and 197 of 241 (81.7%) with PI-RADS 5, respectively. The additional diagnostic value of TB vs. SB compared to combined TB+SB for diagnosing csPCa were 4.3% vs. 3.2% (p=0.844), 20.4% vs 5.1% (p<0.001), and 20.3% vs. 0.7% (p<0.001) with PI-RADS 3, 4, and 5, respectively. TB alone showed no significant difference in diagnostic performance for csPCa with combined TB+SB based on RP specimens in patients with PI-RADS 5 (p=0.732).

Conclusion

A need for addition of SB to TB in patients with PI-RADS 3 and 4 lesions, however, TB alone may be performed without affecting the management of patients with PI-RADS 5.

Predicting clinically significant prostate cancer with a deep learning approach: a multicentre retrospective study

PURPOSE

This study aimed to develop deep learning (DL) models based on multicentre biparametric magnetic resonance imaging (bpMRI) for the diagnosis of clinically significant prostate cancer (csPCa) and compare the performance of these models with that of the Prostate Imaging and Reporting and Data System (PI-RADS) assessment by expert radiologists based on multiparametric MRI (mpMRI).

METHODS

We included 1861 consecutive male patients who underwent radical prostatectomy or biopsy at seven hospitals with mpMRI. These patients were divided into the training (1216 patients in three hospitals) and external validation cohorts (645 patients in four hospitals). PI-RADS assessment was performed by expert radiologists. We developed DL models for the classification between benign and malignant lesions (DL-BM) and that between csPCa and non-csPCa (DL-CS). An integrated model combining PI-RADS and the DL-CS model, abbreviated as PIDL-CS, was developed. The performances of the DL models and PIDL-CS were compared with that of PI-RADS.

RESULTS

In each external validation cohort, the area under the receiver operating characteristic curve (AUC) values of the DL-BM and DL-CS models were not significantly different from that of PI-RADS (P > 0.05), whereas the AUC of PIDL-CS was superior to that of PI-RADS (P < 0.05), except for one external validation cohort (P > 0.05). The specificity of PIDL-CS for the detection of csPCa was much higher than that of PI-RADS (P < 0.05).

CONCLUSION

Our proposed DL models can be a potential non-invasive auxiliary tool for predicting csPCa. Furthermore, PIDL-CS greatly increased the specificity of csPCa detection compared with PI-RADS assessment by expert radiologists, greatly reducing unnecessary biopsies and helping radiologists achieve a precise diagnosis of csPCa.

DCE-MRI and DWI can differentiate benign from malignant prostate tumors when serum PSA is ≥10 ng/ml

Background

This study investigated the diagnostic utility of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) parameters for distinguishing between benign and malignant prostate tumors when serum prostate-specific antigen (PSA) level is ≥10 ng/ml.

Methods

Patients with prostate cancer (PCa) and benign prostatic hyperplasia (BPH) with serum PSA ≥10 ng/ml before treatment were recruited. Transrectal ultrasound-guided biopsy or surgery was performed for tumor classification and patients were stratified accordingly into PCa and BPH groups. Patients underwent DCE-MRI and DWI scanning and the transfer constant (K_trans), rate constant (K_ep), fractional volume of the extravascular extracellular space, plasma volume (V_p), and Prostate Imaging Reporting and Data System Version 2 (PI-RADS v2) score were determined. The apparent diffusion coefficient (ADC) was calculated from DWI. The diagnostic performance of these parameters was assessed by receiver operating characteristic (ROC) curve analysis, and those showing a significant difference between the PCa and BPH groups were combined into a multivariate logistic regression model for PCa diagnosis. Spearman's correlation was used to analyze the relationship between Gleason score and imaging parameters.

Results

The study enrolled 65 patients including 32 with PCa and 33 with BPH. Ktrans (P=0.006), Kep (P=0.001), and Vp (P=0.009) from DCE-MRI and ADC (P<0.001) from DWI could distinguish between the 2 groups when PSA was ≥10 ng/ml. PI-RADS score (area under the ROC curve [AUC]=0.705), Ktrans (AUC=0.700), Kep (AUC=0.737), Vp (AUC=0.688), and ADC (AUC=0.999) showed high diagnostic performance for discriminating PCa from BPH. A combined model based on PI-RADS score, Ktrans, Kep, Vp, and ADC had a higher AUC (1.000), with a sensitivity of 0.998 and specificity of 0.999. Imaging markers showed no significant correlation with Gleason score in PCa.

Conclusion

DCE-MRI and DWI parameters can distinguish between benign and malignant prostate tumors in patients with serum PSA ≥10 ng/ml.

Biparametric MRI-based radiomics classifiers for the detection of prostate cancer in patients with PSA serum levels of 4∼10 ng/mL

Purpose

To investigate the predictive performance of the combined model by integrating clinical variables and radiomic features for the accurate detection of prostate cancer (PCa) in patients with prostate-specific antigen (PSA) serum levels of 4-10 ng/mL.

Methods

A retrospective study of 136 males (mean age, 67.3 ± 8.4 years) with Prostate Imaging-Reporting and Data System (PI-RADS) v2.1 category ≤3 lesions and PSA serum levels of 4-10 ng/mL were performed. All patients underwent multiparametric MRI at 3.0T and transrectal ultrasound-guided systematic prostate biopsy in their clinical workup. Radiomic features were extracted from axial T2-weighted images (T2WI) and apparent diffusion coefficient (ADC) maps of each patient using PyRadiomics. Pearson correlation coefficient (PCC) and recursive feature elimination (RFE) were implemented to identify the most significant radiomic features. Independent clinic-radiological factors were identified via univariate and multivariate regression analyses. Seven machine-learning algorithms were compared to construct a single-layered radiomic score (ie, radscore) and multivariate regression analysis was applied to construct the fusion radscore. Finally, the radiomic nomogram was further developed by integrating useful clinic-radiological factors and fusion radscore using multivariate regression analysis. The discriminative power of the nomogram was evaluated by area under the curve (AUC), DeLong test, calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC).

Results

The transitional zone-specific antigen density was identified as the only independent clinic-radiological factor, which yielded an AUC of 0.592 (95% confidence interval [CI]: 0.527-0.657). The ADC radscore based on six features and Naive Bayes achieved an AUC of 0.779 (95%CI: 0.730-0.828); the T2WI radscore based on 13 features and Support Vector Machine yielded an AUC of 0.808 (95%CI: 0.761-0.855). The fusion radscore obtained an improved AUC of 0.844 (95%CI: 0.801-0.887), which was higher than the single-layered radscores (both P<0.05). The radiomic nomogram achieved the highest value among all models (all P<0.05), with an AUC of 0.872 (95%CI: 0.835-0.909). Calibration curve showed good agreement and DCA together with CIC confirmed the clinical benefits of the radiomic nomogram.

Conclusion

The radiomic nomogram holds the potential for accurate and noninvasive identification of PCa in patients with PI-RADS ≤3 lesions and PSA of 4-10 ng/mL, which could reduce unnecessary biopsy.

Diagnosis and Pathologic Reporting of Prostate Cancer in the Era of MRI-Targeted Prostate Biopsy

Historically, the detection of prostate cancer relied upon a systematic yet random sampling of the prostate by transrectal ultrasound guided biopsy. This approach was a nontargeted technique that led to the under detection of cancers at biopsy and the upgrading of cancers at radical prostatectomy. Multiparametric MRI-targeted prostate biopsy allows for an image-directed approach to the identification of prostate cancer. MRI-targeted biopsy of the prostate is superior for the detection of clinically significant prostate cancer. As this technique has become more prevalent among urologists, pathologists need to recognize how this development impacts cancer diagnosis and reporting.

Micro-ultrasound Versus Magnetic Resonance Imaging in Prostate Cancer Active Surveillance

Accurate assessment of tumor grade is critical for active surveillance (AS) in prostate cancer. We compared magnetic resonance imaging (MRI) and micro-ultrasound scoring (Prostate Imaging-Reporting and Data System [PI-RADS] v2.1 vs Prostate Risk Identification using Micro-ultrasound [PRI-MUS]) in 128 men on AS. The primary outcome was upgrading to Gleason grade group (GG) ≥2. There was no difference in GG ≥2 detection between the imaging techniques (PRI-MUS score ≥3: 33/34, 98%; PI-RADS score ≥3: 29/34, 85%; p = 0.22). The sensitivity, specificity, and positive and negative predictive values for GG ≥2 detection were 97%, 32%, 34%, and 97% with PRI-MUS ≥3, and 85%, 53%, 40%, and 91% with PI-RADS ≥3, respectively. Upgrading to GG ≥2 was more likely for PRI-MUS ≥3 than for PRI-MUS ≤2 scores (odds ratio 15.5, 95% confidence interval 2.0-118.5). A limitation is the lack of blinding to the MRI results. In conclusion, detection of upgrading to GG ≥2 during AS appears similar when using micro-ultrasound or MRI to inform prostate biopsy.

Patient summary

We looked at a novel imaging technology, micro-ultrasound, in patients undergoing biopsy during active surveillance for prostate cancer. We found that micro-ultrasound can detect prostate cancer that may require treatment at a similar rate to that with magnetic resonance imaging (MRI) scans.

Prostate Cancer Detection with mpMRI According to PI-RADS v2 Compared with Systematic MRI/TRUS-Fusion Biopsy: A Prospective Study

Background: mpMRI assesses prostate lesions through their PI-RADS score. The primary goal of this prospective study was to demonstrate the correlation of PI-RADS v2 score and the volume of a lesion with the presence and clinical significance of prostate cancer (PCa). The secondary goal was to determine the extent of additionally PCa in inconspicuous areas. Methods: All 157 patients underwent a perineal MRI/TRUS-fusion prostate biopsy. Targeted biopsies as well as a systematic biopsy were performed. The presence of PCa in the probes was specified by the ISUP grading system. Results: In total, 258 lesions were biopsied. Of the PI-RADS 3 lesions, 24% were neoplastic. This was also true for 36.9% of the PI-RADS 4 lesions and for 59.5% of the PI-RADS 5 lesions. Correlation between ISUP grades and lesion volume was significant (p < 0.01). In the non-suspicious mpMRI areas carcinoma was revealed in 19.7% of the patients. Conclusions: The study shows that the PI-RADS v2 score and the lesion volume correlate with the presence and clinical significance of PCa. However, there are two major points to consider: First, there is a high number of false positive findings. Second, inconspicuous mpMRI areas revealed PCa.

The Application of Biopsy Density in Transperineal Templated-Guided Biopsy Patients With PI-RADS<3

Background

In patients with multiparameter magnetic resonance imaging (mpMRI) low-possibility but highly clinical suspicion of prostate cancer, the biopsy core is unclear. Our study aims to introduce the biopsy density (BD; the ratio of biopsy cores to prostate volume) and investigates the BD-predictive value of prostate cancer and clinically significant prostate cancer (csPCa) in PI-RADS<3 patients.

Methods

Patients underwent transperineal template–guided prostate biopsy from 2012 to 2022. The inclusion criteria were PI-RADS<3 with a positive digital rectal examination or persistent PSA abnormalities. BD was defined as the ratio of the biopsy core to the prostate volume. Clinical data were collected, and we grouped the patients according to pathology results. Kruskal–Wallis test and chi-square test were used in measurement and enumeration data, respectively. Logistics regression was used to choose the factor associated with positive biospy and csPCa. The receiver operating characteristic (ROC) curve was used to evaluate the ability to predict csPCa.

Results

A total of 115 patients were included in our study. Biopsy was positive in 14 of 115 and the International Society of Urological Pathology grade groups 2–5 were in 7 of all the PCa patients. The BD was 0.38 (0.24-0.63) needles per milliliter. Binary logistics analysis suggested that PSAD and BD were correlated with positive biopsy. Meanwhile, BD and PSAD were associated with csPCa. The ROC curve illustrated that BD was a good parameter to predict csPCa (AUC=0.80, 95% CI: 0.69-0.91, p<0.05). The biopsy density combined with PSAD increased the prediction of csPCa (AUC=0.90, 95% CI: 0.85-0.97, p<0.05). The cut-off value of the BD was 0.42 according to the Youden index.

Conclusion

In PI-RADS<3 patients, BD and PSAD are related to csPCa. A biopsy density of more than 0.42 needles per millimeter can increase the csPCa detection rate, which should be considered as an alternative biopsy method when we perform prostate biopsy in patients with PI-RADS<3.

Impact of enema prep on the false-negative rate of a PI-RADS 1 MRI of the prostate for clinically significant prostate cancer

PURPOSE

To investigate whether use of an enema prep reduces the false-negative (FN) rate of PI-RADS 1 MRI of the prostate for clinically significant prostate cancer (csPCa).

MATERIALS AND METHODS

1108 consecutive patients with a PI-RADS 1 MRI performed 01/2016-09/2021 were retrospectively collected. Patient charts were examined for subsequent systematic prostate biopsy performed within 1 year if positive or anytime thereafter if negative. Patients without biopsy were excluded. Use of an enema prep 1-2 h before MRI, which was implemented in 03/2019, was recorded. FN rate of MRI for detection of csPCa, defined as Gleason score ≥ 7, using systematic biopsy was assessed per patient and compared between those with and without an enema prep. Χ² test and logistic regression were performed.

RESULTS

255 patients (median age 64, IQR 58-69) with median PSA 5.6 (IQR 4.2-8.1), PI-RADS 1 MRI, and subsequent biopsy were included in the analysis. 66 patients (26%) had an enema prep and 189 patients (74%) did not. 7 (11%) patients with and 21 (11%) patients without enema prep had a FN biopsy. There was no significant association between enema prep and FN biopsy (OR 0.95, 95% CI 0.38-2.35, p = 0.91).

CONCLUSIONS

Use of an enema prep prior to prostate MRI did not decrease the FN rate of PI-RADS 1 MRI of the prostate for clinically significant prostate cancer.

Multiparametric transrectal ultrasound for the diagnosis of peripheral zone prostate cancer and clinically significant prostate cancer: novel scoring systems

Background

To evaluate the diagnostic performance of multiparametric transrectal ultrasound (TRUS) and to design diagnostic scoring systems based on four modes of TRUS to predict peripheral zone prostate cancer (PCa) and clinically significant prostate cancer (csPCa).

Methods

A development cohort involved 124 nodules from 116 patients, and a validation cohort involved 72 nodules from 67 patients. Predictors for PCa and csPCa were extracted to construct PCa and csPCa models based on regression analysis of the development cohort. An external validation was performed to assess the performance of models using area under the curve (AUC). Then, PCa and csPCa diagnostic scoring systems were established to predict PCa and csPCa. The diagnostic accuracy was compared between PCa and csPCa scores and PI-RADS V2, using receiver operating characteristics (ROC) and decision curve analysis (DCA).

Results

Regression models were established as follows: PCa = − 8.284 + 4.674 × Margin + 1.707 × Adler grade + 3.072 × Enhancement patterns + 2.544 × SR; csPCa = − 7.201 + 2.680 × Margin + 2.583 × Enhancement patterns + 2.194 × SR. The PCa score ranged from 0 to 6 points, and the csPCa score ranged from 0 to 3 points. A PCa score of 5 or higher and a csPCa score of 3 had the greatest diagnostic performance. In the validation cohort, the AUC for the PCa score and PI-RADS V2 in diagnosing PCa were 0.879 (95% confidence interval [CI] 0.790–0.967) and 0.873 (95%CI 0.778–0.969). For the diagnosis of csPCa, the AUC for the csPCa score and PI-RADS V2 were 0.806 (95%CI 0.700–0.912) and 0.829 (95%CI 0.727–0.931).

Conclusions

The multiparametric TRUS diagnostic scoring systems permitted better identifications of peripheral zone PCa and csPCa, and their performances were comparable to that of PI-RADS V2.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12894-022-01013-8.