Prostate Cancer: Interobserver Agreement and Accuracy with the Revised Prostate Imaging Reporting and Data System at Multiparametric MR Imaging

A pilot study of AI-assisted reading of prostate MRI in Organized Prostate Cancer Testing

OBJECTIVES

To evaluate the feasibility of AI-assisted reading of prostate magnetic resonance imaging (MRI) in Organized Prostate cancer Testing (OPT).

METHODS

Retrospective cohort study including 57 men with elevated prostate-specific antigen (PSA) levels ≥3 µg/L that performed bi-parametric MRI in OPT. The results of a CE-marked deep learning (DL) algorithm for prostate MRI lesion detection were compared with assessments performed by on-site radiologists and reference radiologists. Per patient PI-RADS (Prostate Imaging-Reporting and Data System)/Likert scores were cross-tabulated and compared with biopsy outcomes, if performed. Positive MRI was defined as PI-RADS/Likert ≥4. Reader variability was assessed with weighted kappa scores.

RESULTS

The number of positive MRIs was 13 (23%), 8 (14%), and 29 (51%) for the local radiologists, expert consensus, and DL, respectively. Kappa scores were moderate for local radiologists versus expert consensus 0.55 (95% confidence interval [CI]: 0.37-0.74), slight for local radiologists versus DL 0.12 (95% CI: -0.07 to 0.32), and slight for expert consensus versus DL 0.17 (95% CI: -0.01 to 0.35). Out of 10 cases with biopsy proven prostate cancer with Gleason ≥3+4 the DL scored 7 as Likert ≥4.

INTERPRETATION

The Dl-algorithm showed low agreement with both local and expert radiologists. Training and validation of DL-algorithms in specific screening cohorts is essential before introduction in organized testing.

Perspectives on technology: Prostate Imaging-Reporting and Data System (PI-RADS) interobserver variability

OBJECTIVES

To explore the topic of Prostate Imaging-Reporting and Data System (PI-RADS) interobserver variability, including a discussion of major sources, mitigation approaches, and future directions.

METHODS

A narrative review of PI-RADS interobserver variability.

RESULTS

PI-RADS was developed in 2012 to set technical standards for prostate magnetic resonance imaging (MRI), reduce interobserver variability at interpretation, and improve diagnostic accuracy in the MRI-directed diagnostic pathway for detection of clinically significant prostate cancer. While PI-RADS has been validated in selected research cohorts with prostate cancer imaging experts, subsequent prospective studies in routine clinical practice demonstrate wide variability in diagnostic performance. Radiologist and biopsy operator experience are the most important contributing drivers of high-quality care among multiple interrelated factors including variability in MRI hardware and technique, image quality, and population and patient-specific factors such as prostate cancer disease prevalence. Iterative improvements in PI-RADS have helped flatten the curve for novice readers and reduce variability. Innovations in image quality reporting, administrative and organisational workflows, and artificial intelligence hold promise in improving variability even further.

CONCLUSION

Continued research into PI-RADS is needed to facilitate benchmark creation, reader certification, and independent accreditation, which are systems-level interventions needed to uphold and maintain high-quality prostate MRI across entire populations.

Prior Negative Biopsy, PSA Density, and Anatomic Location Impact Cancer Detection Rate of MRI-Targeted PI-RADS Index Lesions

BACKGROUND

MRI fusion prostate biopsy has improved the detection of clinically significant prostate cancer (CSC). Continued refinements in predicting the pre-biopsy probability of CSC are essential for optimal patient counseling. We investigated potential factors related to improved cancer detection rates (CDR) of CSC in patients with PI-RADS ≥ 3 lesions.

METHODS

The pathology of 980 index lesions in 980 patients sampled by transrectal mpMRI-targeted prostate biopsy across four medical centers between 2017-2020 was reviewed. PI-RADS lesion distribution included 291 PI-RADS-5, 374 PI-RADS-4, and 315 PI-RADS-3. We compared CDR of index PI-RADS ≥ 3 lesions based on location (TZ) vs. (PZ), PSA density (PSAD), and history of prior negative conventional transrectal ultrasound-guided biopsy (TRUS).

RESULTS

Mean age, PSA, prostate volume, and level of prior negative TRUS biopsy were 66 years (43-90), 7.82 ng/dL (5.6-11.2), 54 cm3 (12-173), and 456/980 (46.5%), respectively. Higher PSAD, no prior history of negative TRUS biopsy, and PZ lesions were associated with higher CDR. Stratified CDR highlighted significant variance across subgroups. CDR for a PI-RADS-5 score, PZ lesion with PSAD ≥ 0.15, and prior negative biopsy was 77%. Conversely, the CDR rate for a PI-RADS-4 score, TZ lesion with PSAD < 0.15, and prior negative biopsy was significantly lower at 14%.

CONCLUSIONS

For index PI-RADS ≥ 3 lesions, CDR varied significantly based on location, prior history of negative TRUS biopsy, and PSAD. Such considerations are critical when counseling on the merits and potential yield of prostate needle biopsy.

A novel model incorporating quantitative contrast-enhanced ultrasound into PI-RADSv2-based nomogram detecting clinically significant prostate cancer

The diagnostic accuracy of clinically significant prostate cancer (csPCa) of Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) is limited by subjectivity in result interpretation and the false positive results from certain similar anatomic structures. We aimed to establish a new model combining quantitative contrast-enhanced ultrasound, PI-RADSv2, clinical parameters to optimize the PI-RADSv2-based model. The analysis was conducted based on a data set of 151 patients from 2019 to 2022, multiple regression analysis showed that prostate specific antigen density, age, PI-RADSv2, quantitative parameters (rush time, wash-out area under the curve) were independent predictors. Based on these predictors, we established a new predictive model, the AUCs of the model were 0.910 and 0.879 in training and validation cohort, which were higher than those of PI-RADSv2-based model (0.865 and 0.821 in training and validation cohort). Net Reclassification Index analysis indicated that the new predictive model improved the classification of patients. Decision curve analysis showed that in most risk probabilities, the new predictive model improved the clinical utility of PI-RADSv2-based model. Generally, this new predictive model showed that quantitative parameters from contrast enhanced ultrasound could help to improve the diagnostic performance of PI-RADSv2 based model in detecting csPCa.

Histogram analysis of MR quantitative parameters: are they correlated with prognostic factors in prostate cancer?

PURPOSE

To investigate the correlation between quantitative MR parameters and prognostic factors in prostate cancer (PCa).

METHOD

A total of 186 patients with pathologically confirmed PCa who underwent preoperative multiparametric MRI (mpMRI), including synthetic MRI (SyMRI), were enrolled from two medical centers. The histogram metrics of SyMRI [T1, T2, proton density (PD)] and apparent diffusion coefficient (ADC) values were extracted. The Mann‒Whitney U test or Student's t test was employed to determine the association between these histogram features and the prognostically relevant factors. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the differentiation performance. Spearman's rank correlation coefficients were calculated to determine the correlations between histogram parameters and the International Society of Urological Pathology (ISUP) grade group as well as pathological T stage.

RESULTS

Significant correlations were found between the histogram parameters and the ISUP grade as well as pathological T stage of PCa. Among these histogram parameters, ADC_minimum had the strongest correlation with the ISUP grade (r = - 0.481, p < 0.001), and ADC_Median showed the strongest association with pathological T stage (r = - 0.285, p = 0.008). The ADC_10th percentile exhibited the highest performance in identifying clinically significant prostate cancer (csPCa) (AUC 0.833; 95% CI 0.771-0.883). When discriminating between the status of different prognostically relevant factors, a significant difference was observed between extraprostatic extension-positive and -negative cancers with regard to histogram parameters of the ADC map (10th percentile, 90th percentile, mean, median, minimum) and T1 map (minimum) (p = 0.002-0.032). Moreover, histogram parameters of the ADC map (90th percentile, maximum, mean, median), T2 map (10th percentile, median), and PD map (10th percentile, median) were significantly lower in PCa with perineural invasion (p = 0.009-0.049). The T2 values were significantly lower in patients with seminal vesicle invasion (minimum, p = 0.036) and positive surgical margin (10th percentile, 90th percentile, mean, median, and minimum, p = 0.015-0.025).

CONCLUSION

Quantitative histogram parameters derived from synthetic MRI and ADC maps may have great potential for predicting the prognostic features of PCa.

Utilization trend of prebiopsy multiparametric magnetic resonance imaging and its impact on prostate cancer detection: Real-world insights from a high-volume center in southwest China

BACKGROUND

Data on the utilization and effects of prebiopsy prostate multiparametric magnetic resonance imaging (mpMRI) to support its routine use in real-world setting are still scarce.

OBJECTIVE

To evaluate the change of clinical practice of prebiopsy mpMRI over time, and assess its diagnostic accuracy.

DESIGN, SETTING, AND PARTICIPANTS

We retrospectively analyzed data from 6168 patients who underwent primary prostate biopsy (PBx) between January 2011 and December 2021 and had prostate-specific antigen (PSA) values ranging from 3 to 100 ng/mL.

INTERVENTION

Prebiopsy MRI at the time of PBx.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

We performed general linear regression and to elucidate trends in the annual use of prebiopsy mpMRI and conducted multivariable logistic regression to evaluate the potential benefits of incorporating prebiopsy mpMRI for prostate cancer (PCa) detection.

RESULTS AND LIMITATIONS

The utilization of prebiopsy mpMRI significantly increased from 9.2% in 2011 to 75.0% in 2021 (p < 0.001). In addition, prebiopsy mpMRI significantly reduced negative PBx by 8.6% while improving the detection of clinically significant PCa (csPCa) by 7.0%. Regression analysis showed that the utilization of prebiopsy mpMRI was significantly associated with a 48% (95% confidence interval [CI]: 1.19-1.84) and 36% (95% CI: 1.12-1.66) increased PCa detection rate in the PSA 3-10 ng/mL and 10-20 ng/mL groups, respectively; and a 34% increased csPCa detection rate in the PSA 10-20 ng/mL group (95% CI: 1.09-1.64). The retrospective design and the single center cohort constituted the limitations of this study.

CONCLUSIONS

Our study demonstrated a notable rise in the utilization of prebiopsy mpMRI in the past decade. The adoption of this imaging technique was significantly associated with an increased probability of detecting prostate cancer.

PATIENT SUMMARY

From 2011 to 2021, we demonstrated a steady increase in the utilization of prebiopsy mpMRI among biopsy-naïve men. We also confirmed the positive impact of prebiopsy mpMRI utilization on the detection of prostate cancer.

Diagnostic Utility of Artificial Intelligence-assisted Transperineal Biopsy Planning in Prostate Cancer Suspected Men: A Prospective Cohort Study

BACKGROUND AND OBJECTIVE

Accurate magnetic resonance imaging (MRI) reporting is essential for transperineal prostate biopsy (TPB) planning. Although approved computer-aided diagnosis (CAD) tools may assist urologists in this task, evidence of improved clinically significant prostate cancer (csPCa) detection is lacking. Therefore, we aimed to document the diagnostic utility of using Prostate Imaging Reporting and Data System (PI-RADS) and CAD for biopsy planning compared with PI-RADS alone.

METHODS

A total of 262 consecutive men scheduled for TPB at our referral centre were analysed. Reported PI-RADS lesions and an US Food and Drug Administration-cleared CAD tool were used for TPB planning. PI-RADS and CAD lesions were targeted on TPB, while four (interquartile range: 2-5) systematic biopsies were taken. The outcomes were the (1) proportion of csPCa (grade group ≥2) and (2) number of targeted lesions and false-positive rate. Performance was tested using free-response receiver operating characteristic curves and the exact Fisher-Yates test.

KEY FINDINGS AND LIMITATIONS

Overall, csPCa was detected in 56% (146/262) of men, with sensitivity of 92% and 97% (p = 0.007) for PI-RADS- and CAD-directed TPB, respectively. In 4% (10/262), csPCa was detected solely by CAD-directed biopsies; in 8% (22/262), additional csPCa lesions were detected. However, the number of targeted lesions increased by 54% (518 vs 336) and the false-positive rate doubled (0.66 vs 1.39; p = 0.009). Limitations include biopsies only for men at clinical/radiological suspicion and no multidisciplinary review of MRI before biopsy.

CONCLUSIONS AND CLINICAL IMPLICATIONS

The tested CAD tool for TPB planning improves csPCa detection at the cost of an increased number of lesions sampled and false positives. This may enable more personalised biopsy planning depending on urological and patient preferences.

PATIENT SUMMARY

The computer-aided diagnosis tool tested for transperineal prostate biopsy planning improves the detection of clinically significant prostate cancer at the cost of an increased number of lesions sampled and false positives. This may enable more personalised biopsy planning depending on urological and patient preferences.

An Update on the Role of mpMRI and 68Ga-PSMA PET Imaging in Primary and Recurrent Prostate Cancer

The objective of this work was to review comparisons of the efficacy of 68Ga-PSMA-11 (prostate-specific membrane antigen) PET/CT and multiparametric magnetic resonance imaging (mpMRI) in the detection of prostate cancer among patients undergoing initial staging prior to radical prostatectomy or experiencing recurrent prostate cancer, based on histopathological data. A comprehensive search was conducted in PubMed and Web of Science, and relevant articles were analyzed with various parameters, including year of publication, study design, patient count, age, PSA (prostate-specific antigen) value, Gleason score, standardized uptake value (SUVmax), detection rate, treatment history, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and PI-RADS (prostate imaging reporting and data system) scores. Only studies directly comparing PSMA-PET and mpMRI were considered, while those examining combined accuracy or focusing on either modality alone were excluded. In total, 24 studies comprising 1717 patients were analyzed, with the most common indication for screening being staging, followed by relapse. The findings indicated that 68Ga-PSMA-PET/CT effectively diagnosed prostate cancer in patients with suspected or confirmed disease, and both methods exhibited comparable efficacy in identifying lesion-specific information. However, notable heterogeneity was observed, highlighting the necessity for standardization of imaging and histopathology systems to mitigate inter-study variability. Future research should prioritize evaluating the combined diagnostic performance of both modalities to enhance sensitivity and reduce unnecessary biopsies. Overall, the utilization of PSMA-PET and mpMRI in combination holds substantial potential for significantly advancing the diagnosis and management of prostate cancer.

Optimal size threshold for PIRADSv2 category 5 upgrade and its positive predictive value: is it predictive of "very high" likelihood of clinically-significant cancer?

AIM

To identify the optimal size metric and threshold for Prostate Imaging Reporting and Data System (PIRADS) 5 upgrade, calculate its positive predictive value (PPV) for clinically-significant prostate cancer (csPCA), and determine if it is indicative of a "very high" likelihood of csPCA.

MATERIALS AND METHODS

One hundred and forty-three PIRADS 4 or 5 lesions were evaluated. Lesion diameters were used to calculate lesion volume (LV). Pearson correlation between maximum lesion diameter (MLD) and LV was calculated. Area under the curve (AUC) for discriminating csPCA (Gleason grade ≥ 3 + 4) was calculated using MLD and LV. Optimal size thresholds (using Youden index) and highly predictive size thresholds were identified for the whole prostate (WP), peripheral zone (PZ), and transitional zone (TZ).

RESULTS

There was high correlation between MLD and LV (r=0.77-0.81), with comparable AUCs for MLD and LV in the identification of csPCA in the WP (0.73, 0.72), PZ (0.73, 0.73), and TZ (0.79, 0.75). Optimal MLD thresholds were 1.4, 1.4, and 1.6 cm in the WP, PZ, and TZ respectively, with PPVs of 76%, 81%, and 69%, respectively. An MLD threshold of 2.7 cm would be needed in the WP to achieve a PPV approaching 90%, with sensitivity decreasing to 10%.

CONCLUSIONS

There is high correlation between MLD and LV with comparable discrimination of csPCA using each. PIRADSv2's 1.5 cm MLD threshold is near the optimal threshold for PIRADS 5 upgrade but has moderate PPV. A much higher threshold would be needed to increase its PPV, with significant sacrifice in sensitivity.

Assessing the Feasibility of Using Artificial Intelligence-Segmented Dominant Intraprostatic Lesion for Focal Intraprostatic Boost With External Beam Radiation Therapy

PURPOSE

The delineation of dominant intraprostatic gross tumor volumes (GTVs) on multiparametric magnetic resonance imaging (mpMRI) can be subject to interobserver variability. We evaluated whether deep learning artificial intelligence (AI)-segmented GTVs can provide a similar degree of intraprostatic boosting with external beam radiation therapy (EBRT) as radiation oncologist (RO)-delineated GTVs.

METHODS AND MATERIALS

We identified 124 patients who underwent mpMRI followed by EBRT between 2010 and 2013. A reference GTV was delineated by an RO and approved by a board-certified radiologist. We trained an AI algorithm for GTV delineation on 89 patients, and tested the algorithm on 35 patients, each with at least 1 PI-RADS (Prostate Imaging Reporting and Data System) 4 or 5 lesion (46 total lesions). We then asked 5 additional ROs to independently delineate GTVs on the test set. We compared lesion detectability and geometric accuracy of the GTVs from AI and 5 ROs against the reference GTV. Then, we generated EBRT plans (77 Gy prostate) that boosted each observer-specific GTV to 95 Gy. We compared reference GTV dose (D98%) across observers using a mixed-effects model.

RESULTS

On a lesion level, AI GTV exhibited a sensitivity of 82.6% and positive predictive value of 86.4%. Respective ranges among the 5 RO GTVs were 84.8% to 95.7% and 95.1% to 100.0%. Among 30 GTVs mutually identified by all observers, no significant differences in Dice coefficient were detected between AI and any of the 5 ROs. Across all patients, only 2 of 5 ROs had a reference GTV D98% that significantly differed from that of AI by 2.56 Gy (P = .02) and 3.20 Gy (P = .003). The presence of false-negative (-5.97 Gy; P < .001) but not false-positive (P = .24) lesions was associated with reference GTV D98%.

CONCLUSIONS

AI-segmented GTVs demonstrate potential for intraprostatic boosting, although the degree of boosting may be adversely affected by false-negative lesions. Prospective review of AI-segmented GTVs remains essential.

AI-powered radiomics: revolutionizing detection of urologic malignancies

PURPOSE OF REVIEW

This review aims to highlight the integration of artificial intelligence-powered radiomics in urologic oncology, focusing on the diagnostic and prognostic advancements in the realm of managing prostate, kidney, and bladder cancers.

RECENT FINDINGS

As artificial intelligence continues to shape the medical imaging landscape, its integration into the field of urologic oncology has led to impressive results. For prostate cancer diagnostics, machine learning has shown promise in refining clinically-significant lesion detection, with some success in deciphering ambiguous lesions on multiparametric MRI. For kidney cancer, radiomics has emerged as a valuable tool for better distinguishing between benign and malignant renal masses and predicting tumor behavior from CT or MRI scans. Meanwhile, in the arena of bladder cancer, there is a burgeoning emphasis on prediction of muscle invasive cancer and forecasting disease trajectory. However, many studies showing promise in these areas face challenges due to limited sample sizes and the need for broader external validation.

SUMMARY

Radiomics integrated with artificial intelligence offers a pioneering approach to urologic oncology, ushering in an era of enhanced diagnostic precision and reduced invasiveness, guiding patient-tailored treatment plans. Researchers must embrace broader, multicentered endeavors to harness the full potential of this field.

Retrospective T2 quantification from conventional weighted MRI of the prostate based on deep learning

Purpose

To develop a deep learning-based method to retrospectively quantify T2 from conventional T1- and T2-weighted images.

Methods

Twenty-five subjects were imaged using a multi-echo spin-echo sequence to estimate reference prostate T2 maps. Conventional T1- and T2-weighted images were acquired as the input images. A U-Net based neural network was developed to directly estimate T2 maps from the weighted images using a four-fold cross-validation training strategy. The structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean percentage error (MPE), and Pearson correlation coefficient were calculated to evaluate the quality of network-estimated T2 maps. To explore the potential of this approach in clinical practice, a retrospective T2 quantification was performed on a high-risk prostate cancer cohort (Group 1) and a low-risk active surveillance cohort (Group 2). Tumor and non-tumor T2 values were evaluated by an experienced radiologist based on region of interest (ROI) analysis.

Results

The T2 maps generated by the trained network were consistent with the corresponding reference. Prostate tissue structures and contrast were well preserved, with a PSNR of 26.41 ± 1.17 dB, an SSIM of 0.85 ± 0.02, and a Pearson correlation coefficient of 0.86. Quantitative ROI analyses performed on 38 prostate cancer patients revealed estimated T2 values of 80.4 ± 14.4 ms and 106.8 ± 16.3 ms for tumor and non-tumor regions, respectively. ROI measurements showed a significant difference between tumor and non-tumor regions of the estimated T2 maps (P < 0.001). In the two-timepoints active surveillance cohort, patients defined as progressors exhibited lower estimated T2 values of the tumor ROIs at the second time point compared to the first time point. Additionally, the T2 difference between two time points for progressors was significantly greater than that for non-progressors (P = 0.010).

Conclusion

A deep learning method was developed to estimate prostate T2 maps retrospectively from clinically acquired T1- and T2-weighted images, which has the potential to improve prostate cancer diagnosis and characterization without requiring extra scans.

T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence

Prostate cancer (PCa) is the most diagnosed non-cutaneous cancer in men. Despite therapies such as radical prostatectomy, which is considered curative, distant metastases may form, resulting in biochemical recurrence (BCR). This study used radiomic features calculated from multi-parametric magnetic resonance imaging (MP-MRI) to evaluate their ability to predict BCR and PCa presence. Data from a total of 279 patients, of which 46 experienced BCR, undergoing MP-MRI prior to surgery were assessed for this study. After surgery, the prostate was sectioned using patient-specific 3D-printed slicing jigs modeled using the T2-weighted imaging (T2WI). Sectioned tissue was stained, digitized, and annotated by a GU-fellowship trained pathologist for cancer presence. Digitized slides and annotations were co-registered to the T2WI and radiomic features were calculated across the whole prostate and cancerous lesions. A tree regression model was fitted to assess the ability of radiomic features to predict BCR, and a tree classification model was fitted with the same radiomic features to classify regions of cancer. We found that 10 radiomic features predicted eventual BCR with an AUC of 0.97 and classified cancer at an accuracy of 89.9%. This study showcases the application of a radiomic feature-based tool to screen for the presence of prostate cancer and assess patient prognosis, as determined by biochemical recurrence.

External Validation of the IMPROD-MRI Volumetric Model to Predict the Utility of Systematic Biopsies at the Time of Targeted Biopsy

Background: The aim of this study was to validate externally a nomogram that relies on MRI volumetric parameters and clinical data to determine the need for a standard biopsy in addition to a target biopsy for men with suspicious prostate MRI findings. Methods: We conducted a retrospective analysis of a prospectively maintained database of 469 biopsy-naïve men who underwent prostate biopsies. These biopsies were guided by pre-biopsy multiparametric Magnetic Resonance Imaging (mpMRI) and were performed at two different institutions. We included men with a PIRADSsv 2.1 score from 3 to 5. Each patient underwent both an MRI-ultrasound fusion biopsy of identified MRI-suspicious lesions and a systematic biopsy according to our protocol. The lesion volume percentage was determined as the proportion of cancer volume on MRI relative to the entire prostate volume. The study's outcomes were iPCa (Gleason Grade Group 1) and csPCa (Gleason Grade Group > 1). We evaluated the model's performance using AUC decision curve analyses and a systematic analysis of model-derived probability cut-offs in terms of the potential to avoid diagnosing iPCa and to accurately diagnose csPCa. Results: The nomogram includes age, PSA value, prostate volume, PIRADSsv 2.1 score, percentage of MRI-suspicious lesion volume, and lesion location. AUC was determined to be 0.73. By using various nomogram cut-off thresholds (ranging from 5% to 30%), it was observed that 19% to 58% of men could potentially avoid undergoing standard biopsies. In this scenario, the model might miss 0% to 10% of diagnosis of csPCa and could prevent identifying 6% to 31% of iPCa cases. These results are in line with findings from the multi-institutional external validation study based on the IMPROD trial (n = 122) and the MULTI-IMPROD trial (n = 262). According to DCA, the use of this nomogram led to an increased overall net clinical benefit when the threshold probability exceeded 10%. Conclusions: This study supports the potential value of a model relying on MRI volumetric measurements for selecting individuals with clinical suspicion of prostate cancer who would benefit from undergoing a standard biopsy in addition to a targeted biopsy.

Up-to-Date Imaging and Diagnostic Techniques for Prostate Cancer: A Literature Review

Prostate cancer (PCa) faces great challenges in early diagnosis, which often leads not only to unnecessary, invasive procedures, but to over-diagnosis and treatment as well, thus highlighting the need for modern PCa diagnostic techniques. The review aims to provide an up-to-date summary of chronologically existing diagnostic approaches for PCa, as well as their potential to improve clinically significant PCa (csPCa) diagnosis and to reduce the proliferation and monitoring of PCa. Our review demonstrates the primary outcomes of the most significant studies and makes comparisons across the diagnostic efficacies of different PCa tests. Since prostate biopsy, the current mainstream PCa diagnosis, is an invasive procedure with a high risk of post-biopsy complications, it is vital we dig out specific, sensitive, and accurate diagnostic approaches in PCa and conduct more studies with milestone findings and comparable sample sizes to validate and corroborate the findings.

Contemporary Review of Multimodality Imaging of the Prostate Gland

Tissue changes and the enlargement of the prostate, whether benign or malignant, are among the most common groups of diseases that affect men and can have significant impacts on length and quality of life. The prevalence of benign prostatic hyperplasia (BPH) increases significantly with age and affects nearly all men as they grow older. Other than skin cancers, prostate cancer is the most common cancer among men in the United States. Imaging is an essential component in the diagnosis and management of these conditions. Multiple modalities are available for prostate imaging, including several novel imaging modalities that have changed the landscape of prostate imaging in recent years. This review will cover the data relating to commonly used standard-of-care prostate imaging modalities, advances in newer technologies, and newer standards that impact prostate gland imaging.

Nanoparticles Targeted to Fibroblast Activation Protein Outperform PSMA for MRI Delineation of Primary Prostate Tumors

Accurate delineation of gross tumor volumes remains a barrier to radiotherapy dose escalation and boost dosing in the treatment of solid tumors, such as prostate cancer. Magnetic resonance imaging (MRI) of tumor targets has the power to enable focal dose boosting, particularly when combined with technological advances such as MRI-linear accelerator. Fibroblast activation protein (FAP) is overexpressed in stromal components of >90% of epithelial carcinomas. Herein, the authors compare targeted MRI of prostate specific membrane antigen (PSMA) with FAP in the delineation of orthotopic prostate tumors. Control, FAP, and PSMA-targeting iron oxide nanoparticles were prepared with modification of a lymphotropic MRI agent (FerroTrace, Ferronova). Mice with orthotopic LNCaP tumors underwent MRI 24 h after intravenous injection of nanoparticles. FAP and PSMA nanoparticles produced contrast enhancement on MRI when compared to control nanoparticles. FAP-targeted MRI increased the proportion of tumor contrast-enhancing black pixels by 13%, compared to PSMA. Analysis of changes in R2 values between healthy prostates and LNCaP tumors indicated an increase in contrast-enhancing pixels in the tumor border of 15% when targeting FAP, compared to PSMA. This study demonstrates the preclinical feasibility of PSMA and FAP-targeted MRI which can enable targeted image-guided focal therapy of localized prostate cancer.

Reliability of prostate imaging reporting and data system version 2.1 for excluding clinically significant prostate cancer using a 1.5 tesla scanner

INTRODUCTION

Multiparametric magnetic resonance imaging (mpMRI) of the prostate gland is now the recommended initial investigation of choice for the detection of Prostate cancer (PCa). It effectively identifies patients who require prostate biopsies due to the risk of clinically significant PCa. It helps patients with clinically insignificant PCa avoid the invasive biopsies and possible accompanying complications. Large clinical trials have investigated the accuracy of mpMRI in detecting PCa. We performed a local review to examine the reliability of omitting tissue sampling in men with a negative (PIRADS 2 (P2) or less) mpMRI in the primary diagnostic setting.

METHODS

This was a retrospective study of patients with clinical suspicion of PCa within a 2-year period. Patients had a mpMRI prior to having trans-perineal prostate gland biopsies. Clinically significant disease was defined as Gleason 7 and above. The descriptive data was analysed using contingency table methods. A p-value less than 0.05 was statistically significant.

RESULTS

Out of 700 patients 90 had an mpMRI score of PIRADS 2. Seventy-seven (85.5%) of these patients had a negative biopsy, 9(10%) showed Gleason 6, 4 patients showed Gleason 7 or above. 78 patients with PIRADS 2 had a PSA density of < 0.15, none of which had a clinically significant biopsy result. The negative predictive value of mpMRI from this study is 95%.

CONCLUSION

Our results are in line with negative predictive values demonstrated in the current literature. This local study, likely applicable to other district general hospitals, shows that mpMRI is a safe and reliable initial investigation to aid decisions on which patients require biopsies.

Predicting prostate cancer in men with PSA levels of 4-10 ng/mL: MRI-based radiomics can help junior radiologists improve the diagnostic performance

To develop MRI-based radiomics model for predicting prostate cancer (PCa) in men with prostate-specific antigen (PSA) levels of 4-10 ng/mL, to compare the performance of radiomics model and PI-RADS v2.1, and to further verify the predictive ability of radiomics model for lesions with different PI-RADS v2.1 score. 171 patients with PSA levels of 4-10 ng/mL were divided into training (n = 119) and testing (n = 52) groups. PI-RADS v2.1 score was assessed by two radiologists. All volumes of interest were segmented on T₂-weighted imaging, diffusion weighted imaging, and apparent diffusion coefficient sequences, from which quantitative radiomics features were extracted. Multivariate logistic regression analysis was performed to establish radiomics model for predicting PCa. The diagnostic performance was assessed using receiver operating characteristic curve analysis. The radiomics model exhibited the best performance in predicting PCa, which was better than the performance of PI-RADS v2.1 scoring by the junior radiologist in the training group [area under the curve (AUC): 0.932 vs 0.803], testing group (AUC: 0.922 vs 0.797), and the entire cohort (AUC: 0.927 vs 0.801) (P < 0.05). The radiomics model performed well for lesions with PI-RADS v2.1 score of 3 (AUC = 0.854, sensitivity = 84.62%, specificity = 84.34%) and PI-RADS v2.1 score of 4-5 (AUC = 0.967, sensitivity = 98.11%, specificity = 86.36%) assigned by junior radiologist. The radiomics model quantitatively outperformed PI-RADS v2.1 for noninvasive prediction of PCa in men with PSA levels of 4-10 ng/mL. The model can help improve the diagnostic performance of junior radiologists and facilitate better decision-making by urologists for management of lesions with different PI-RADS v2.1 score.

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.

Temporal changes of PIRADS scoring by radiologists and correlation to radical prostatectomy pathological outcomes

Purpose

To assess temporal improvement of prostate image reporting and data system (PIRADS) 3-5 lesion correlation to histopathologic findings from radical prostatectomy (RP) in prostate cancer (PCa).

Materials and methods

A total of 1481 patients who underwent RP for biopsy-proven PCa between 2015 and 2019 were divided into 14 groups of 100 sequential readings for the evaluation of histopathological correlation with PIRADS readings. Temporal trends of PIRADS distribution and predictive performance for RP pathology were evaluated to assess underlying changes in prostate magnetic resonance imaging (MRI) interpretation by radiologists.

Results

PIRADS 4-5 lesions were significantly correlated with the increasing rates of Gleason Group (GG) upgrade (p = 0.044) and decreasing rate of GG downgrade (p = 0.016) over time. PIRADS ≥3 lesions read after median 2 years of experience were shown to independently predict intermediate-high-risk (GG ≥ 3) PCa (odds ratio 2.93, 95% confidence interval 1.00-8.54; P= 0.049) in RP pathology. Preoperative GG ≥ 3 biopsy lesions with PIRADS 4-5 lesions were significantly more susceptible to GG upgrade (P= 0.035) and GG ≥ 4 RP pathology (p = 0.003) in experienced reads, in contrast to insignificant findings in early readings (p = 0.588 and 0.248, respectively).

Conclusion

Preoperative MRI reports matched with RP pathology suggest an improved prediction of adverse pathology in PIRADS 3-5 lesions over time, suggesting a temporal change in PIRADS interpretation and predictive accuracy. Institutions with low volume experience should use caution in solely relying on MRI for predicting tumor characteristics. Future prospective trials and larger scale assessments are required to further validate our results.

Multi-institutional analysis of clinical and imaging risk factors for detecting clinically significant prostate cancer in men with PI-RADS 3 lesions

BACKGROUND

Most Prostate Imaging-Reporting and Data System (PI-RADS) 3 lesions do not contain clinically significant prostate cancer (CSPCa; grade group ≥2). This study was aimed at identifying clinical and magnetic resonance imaging (MRI)-derived risk fac- tors that predict CSPCa in men with PI-RADS 3 lesions.

METHODS

This study analyzed the detection of CSPCa in men who underwent MRI-targeted biopsy for PI-RADS 3 lesions. Multivariable logistic regression models with goodness-of-fit testing were used to identify variables associated with CSPCa. Receiver operating curves and decision curve analyses were used to estimate the clinical utility of a predictive model.

RESULTS

Of the 1784 men reviewed, 1537 were included in the training cohort, and 247 were included in the validation cohort. The 309 men with CSPCa (17.3%) were older, had a higher prostate-specific antigen (PSA) density, and had a greater likelihood of an anteriorly located lesion than men without CSPCa (p < .01). Multivariable analysis revealed that PSA density (odds ratio [OR], 1.36; 95% confidence interval [CI], 1.05-1.85; p < .01), age (OR, 1.05; 95% CI, 1.02-1.07; p < .01), and a biopsy-naive status (OR, 1.83; 95% CI, 1.38-2.44) were independently associated with CSPCa. A prior negative biopsy was negatively associated (OR, 0.35; 95% CI, 0.24-0.50; p < .01). The application of the model to the validation cohort resulted in an area under the curve of 0.78. A predicted risk threshold of 12% could have prevented 25% of biopsies while detecting almost 95% of CSPCas with a sensitivity of 94% and a specificity of 34%.

CONCLUSIONS

For PI-RADS 3 lesions, an elevated PSA density, older age, and a biopsy-naive status were associated with CSPCa, whereas a prior negative biopsy was negatively associated. A predictive model could prevent PI-RADS 3 biopsies while missing few CSPCas.

LAY SUMMARY

Among men with an equivocal lesion (Prostate Imaging-Reporting and Data System 3) on multiparametric magnetic resonance imaging (mpMRI), those who are older, those who have a higher prostate-specific antigen density, and those who have never had a biopsy before are at higher risk for having clinically significant prostate cancer (CSPCa) on subsequent biopsy. However, men with at least one negative biopsy have a lower risk of CSPCa. A new predictive model can greatly reduce the need to biopsy equivocal lesions noted on mpMRI while missing only a few cases of CSPCa.

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

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

Artificial Intelligence for Automated Cancer Detection on Prostate MRI: Opportunities and Ongoing Challenges, From the AJR Special Series on AI Applications

Use of prostate MRI has increased greatly in the past decade, primarily in directing targeted prostate biopsy. However, prostate MRI interpretation remains prone to interreader variation. Artificial intelligence (AI) has the potential to standardize detection of lesions on MRI that are suspicious for prostate cancer (PCa). The purpose of this review is to explore the current status of AI for the automated detection of PCa on MRI. Recent literature describing promising results regarding AI models for PCa detection on MRI is highlighted. Numerous limitations of the existing literature are also described, including biases in model validation, heterogeneity in reporting of performance metrics, and lack of sufficient evidence of clinical translation. Challenges related to AI ethics and data governance are also discussed. An outlook is provided for AI in lesion detection on prostate MRI in the coming years, emphasizing current research needs. Future investigations, incorporating large-scale diverse multiinstitutional training and testing datasets, are anticipated to enable the development of more robust AI models for PCa detection on MRI, though prospective clinical trials will ultimately be required to establish benefit of AI in patient management.

Comparison of Multiparametric Magnetic Resonance Imaging with Prostate-Specific Membrane Antigen Positron-Emission Tomography Imaging in Primary Prostate Cancer Diagnosis: A Systematic Review and Meta-Analysis

Multiparametric magnetic-resonance imaging (mpMRI) has proven utility in diagnosing primary prostate cancer. However, the diagnostic potential of prostate-specific membrane antigen positron-emission tomography (PSMA PET) has yet to be established. This study aims to systematically review the current literature comparing the diagnostic performance of mpMRI and PSMA PET imaging to diagnose primary prostate cancer. A systematic literature search was performed up to December 2021. Quality analyses were conducted using the QUADAS-2 tool. The reference standard was whole-mount prostatectomy or prostate biopsy. Statistical analysis involved the pooling of the reported diagnostic performances of each modality, and differences in per-patient and per-lesion analysis were compared using a Fisher’s exact test. Ten articles were included in the meta-analysis. At a per-patient level, the pooled values of sensitivity, specificity, and area under the curve (AUC) for mpMRI and PSMA PET/CT were 0.87 (95% CI: 0.83−0.91) vs. 0.93 (95% CI: 0.90−0.96, p < 0.01); 0.47 (95% CI: 0.23−0.71) vs. 0.54 (95% CI: 0.23−0.84, p > 0.05); and 0.84 vs. 0.91, respectively. At a per-lesion level, the pooled sensitivity, specificity, and AUC value for mpMRI and PSMA PET/CT were lower, at 0.63 (95% CI: 0.52−0.74) vs. 0.79 (95% CI: 0.62−0.92, p < 0.001); 0.88 (95% CI: 0.81−0.95) vs. 0.71 (95% CI: 0.47−0.90, p < 0.05); and 0.83 vs. 0.84, respectively. High heterogeneity was observed between studies. PSMA PET/CT may better confirm the presence of prostate cancer than mpMRI. However, both modalities appear comparable in determining the localisation of the lesions.

Promoting the use of the PRECISE score for prostate MRI during active surveillance: results from the ESOR Nicholas Gourtsoyiannis teaching fellowship

Objectives

The PRECISE criteria for serial multiparametric magnetic resonance imaging (MRI) of the prostate during active surveillance recommend the use of a dedicated scoring system (PRECISE score) to assess the likelihood of clinically significant radiological change. This pilot study assesses the effect of an interactive teaching course on prostate MRI during active surveillance in assessing radiological change in serial imaging.

Methods

Eleven radiology fellows and registrars with different experience in prostate MRI reading participated in a dedicated teaching course where they initially evaluated radiological change (based on their previous training in prostate MRI reading) independently in fifteen patients on active surveillance (baseline and follow-up scan), and then attended a lecture on the PRECISE score. The initial scans were reviewed for teaching purposes and afterwards the participants re-assessed the degree of radiological change in a new set of images (from fifteen different patients) applying the PRECISE score. Receiver operating characteristic analysis was performed. Confirmatory biopsies and PRECISE scores given in consensus by two radiologists (involved in the original draft of the PRECISE score) were the reference standard.

Results

There was a significant improvement in the average area under the curve (AUC) for the assessment of radiological change from baseline (AUC: 0.60 [Confidence Intervals: 0.51–0.69] to post-teaching (AUC: 0.77 [0.70–0.84]). This was an improvement of 0.17 [0.016–0.28] (p = 0.004).

Conclusions

A dedicated teaching course on the use of the PRECISE score improves the accuracy in the assessment of radiological change in serial MRI of the prostate.

Multicenter Evaluation of Multiparametric MRI Clear Cell Likelihood Scores in Solid Indeterminate Small Renal Masses

Background Solid small renal masses (SRMs) (≤4 cm) represent benign and malignant tumors. Among SRMs, clear cell renal cell carcinoma (ccRCC) is frequently aggressive. When compared with invasive percutaneous biopsies, the objective of the proposed clear cell likelihood score (ccLS) is to classify ccRCC noninvasively by using multiparametric MRI, but it lacks external validation. Purpose To evaluate the performance of and interobserver agreement for ccLS to diagnose ccRCC among solid SRMs. Materials and Methods This retrospective multicenter cross-sectional study included patients with consecutive solid (≥25% approximate volume enhancement) SRMs undergoing multiparametric MRI between December 2012 and December 2019 at five academic medical centers with histologic confirmation of diagnosis. Masses with macroscopic fat were excluded. After a 1.5-hour training session, two abdominal radiologists per center independently rendered a ccLS for 50 masses. The diagnostic performance for ccRCC was calculated using random-effects logistic regression modeling. The distribution of ccRCC by ccLS was tabulated. Interobserver agreement for ccLS was evaluated with the Fleiss κ statistic. Results A total of 241 patients (mean age, 60 years ± 13 [SD]; 174 men) with 250 solid SRMs were evaluated. The mean size was 25 mm ± 8 (range, 10-39 mm). Of the 250 SRMs, 119 (48%) were ccRCC. The sensitivity, specificity, and positive predictive value for the diagnosis of ccRCC when ccLS was 4 or higher were 75% (95% CI: 68, 81), 78% (72, 84), and 76% (69, 81), respectively. The negative predictive value of a ccLS of 2 or lower was 88% (95% CI: 81, 93). The percentages of ccRCC according to the ccLS were 6% (range, 0%-18%), 38% (range, 0%-100%), 32% (range, 60%-83%), 72% (range, 40%-88%), and 81% (range, 73%-100%) for ccLSs of 1-5, respectively. The mean interobserver agreement was moderate (κ = 0.58; 95% CI: 0.42, 0.75). Conclusion The clear cell likelihood score applied to multiparametric MRI had moderate interobserver agreement and differentiated clear cell renal cell carcinoma from other solid renal masses, with a negative predictive value of 88%. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Mileto and Potretzke in this issue.

Infiltrative growth pattern of prostate cancer is associated with lower uptake on PSMA PET and reduced diffusion restriction on mpMRI

Purpose

Recently, a significant association was shown between novel growth patterns on histopathology of prostate cancer (PCa) and prostate-specific membrane antigen (PSMA) uptake on [⁶⁸Ga]PSMA-PET. It is the aim of this study to evaluate the association between these growth patterns and ADC (mm²/1000 s) values in comparison to [⁶⁸Ga]PSMA uptake on PET/MRI.

Methods

We retrospectively evaluated patients who underwent [⁶⁸Ga]PSMA PET/MRI for staging or biopsy guidance, followed by radical prostatectomy at our institution between 07/2016 and 01/2020. The dominant lesion per patient was selected based on histopathology and correlated to PET/MRI in a multidisciplinary meeting, and quantified using SUV_max for PSMA uptake and ADC_mean for diffusion restriction. PCa growth pattern was classified as expansive (EXP) or infiltrative (INF) according to its properties of forming a tumoral mass or infiltrating diffusely between benign glands by two independent pathologists. Furthermore, the corresponding WHO2016 ISUP tumor grade was evaluated. The t test was used to compare means, Pearson’s test for categorical correlation, Cohen’s kappa test for interrater agreement, and ROC curve to determine the best cutoff.

Results

Sixty-two patients were included (mean PSA 11.7 ± 12.5). The interrater agreement between both pathologists was almost perfect with κ = 0.81. While 25 lesions had an EXP-growth with an ADC_mean of 0.777 ± 0.109, 37 showed an INF-growth with a significantly higher ADC_mean of 1.079 ± 0.262 (p < 0.001). We also observed a significant difference regarding PSMA SUV_max for the EXP-growth (19.2 ± 10.9) versus the INF-growth (9.4 ± 6.2, p < 0.001). Within the lesions encompassing the EXP- or the INF-growth, no significant correlation between the ISUP groups and ADC_mean could be observed (p = 0.982 and p = 0.861, respectively).

Conclusion

PCa with INF-growth showed significantly lower SUV_max and higher ADC_mean values compared to PCa with EXP-growth. Within the growth groups, ADC_mean values were independent from ISUP grading.

Supplementary information

The online version contains supplementary material available at 10.1007/s00259-022-05787-9.

Development of a nomogram combining multiparametric magnetic resonance imaging and PSA-related parameters to enhance the detection of clinically significant cancer across different region

OBJECTIVE

Prostate cancer (PCa) is the most prevalent cancer among males. This study attempted to develop a clinically significant prostate cancer (csPCa) risk nomogram including Prostate Imaging-Reporting and Data System (PI-RADS) score and other clinical indexes for initial prostate biopsy in light of the different prostate regions, and internal validation was further conducted.

PATIENTS AND METHODS

A retrospective study was performed including 688 patients who underwent ultrasound-guided transperineal magnetic resonance imaging fusion prostate biopsy from December 2016 to July 2019. We constructed nomograms combining PI-RADS score and clinical variables (prostate-specific antigen [PSA], prostate volume (PV), age, free/total PSA, and PSA density) through univariate and multivariate logistic regression to identify patients eligible for biopsy. The performance of the predictive model was evaluated by bootstrap resampling. The area under the curve (AUC) of the receiver-operating characteristic (ROC) analysis was appointed to quantify the accuracy of the primary nomogram model for csPCa. Calibration curves were used to assess the agreement between the biopsy specimen and the predicted probability of the new nomogram. The χ² test was also applied to evaluate the heterogeneity between fusion biopsy and systematic biopsy based on different PI-RADS scores and prostate regions.

RESULTS

A total of 320 of 688 included patients were diagnosed with csPCa. csPCa was defined as Gleason score ≥7. The ROC and concordance-index both presented good performance. The nomogram reached an AUC of 0.867 for predicting csPCa at the peripheral zone; meanwhile, AUC for transitional and apex zones were 0.889 and 0.757, respectively. Statistical significance was detected between fusion biopsy and systematic biopsy for PI-RADS score >3 lesions and lesions at the peripheral and transitional zones.

CONCLUSION

We produced a novel nomogram predicting csPCa in patients with suspected imaging according to different locations. Our results indicated that PI-RADS score combined with other clinical parameters showed a robust predictive capacity for csPCa before prostate biopsy. The new nomogram, which incorporates prebiopsy data including PSA, PV, age, and PI-RADS score, can be helpful for clinical decision-making to avoid unnecessary biopsy.

Diffusion-Weighted MRI in the Genitourinary System

Diffusion weighted imaging (DWI) constitutes a major functional parameter performed in Magnetic Resonance Imaging (MRI). The DW sequence is performed by acquiring a set of native images described by their b-values, each b-value representing the strength of the diffusion MR gradients specific to that sequence. By fitting the data with models describing the motion of water in tissue, an apparent diffusion coefficient (ADC) map is built and allows the assessment of water mobility inside the tissue. The high cellularity of tumors restricts the water diffusion and decreases the value of ADC within tumors, which makes them appear hypointense on ADC maps. The role of this sequence now largely exceeds its first clinical apparitions in neuroimaging, whereby the method helped diagnose the early phases of cerebral ischemic stroke. The applications extend to whole-body imaging for both neoplastic and non-neoplastic diseases. This review emphasizes the integration of DWI in the genitourinary system imaging by outlining the sequence's usage in female pelvis, prostate, bladder, penis, testis and kidney MRI. In gynecologic imaging, DWI is an essential sequence for the characterization of cervix tumors and endometrial carcinomas, as well as to differentiate between leiomyosarcoma and benign leiomyoma of the uterus. In ovarian epithelial neoplasms, DWI provides key information for the characterization of solid components in heterogeneous complex ovarian masses. In prostate imaging, DWI became an essential part of multi-parametric Magnetic Resonance Imaging (mpMRI) to detect prostate cancer. The Prostate Imaging-Reporting and Data System (PI-RADS) scoring the probability of significant prostate tumors has significantly contributed to this success. Its contribution has established mpMRI as a mandatory examination for the planning of prostate biopsies and radical prostatectomy. Following a similar approach, DWI was included in multiparametric protocols for the bladder and the testis. In renal imaging, DWI is not able to robustly differentiate between malignant and benign renal tumors but may be helpful to characterize tumor subtypes, including clear-cell and non-clear-cell renal carcinomas or low-fat angiomyolipomas. One of the most promising developments of renal DWI is the estimation of renal fibrosis in chronic kidney disease (CKD) patients. In conclusion, DWI constitutes a major advancement in genitourinary imaging with a central role in decision algorithms in the female pelvis and prostate cancer, now allowing promising applications in renal imaging or in the bladder and testicular mpMRI.

Concordance between Preoperative mpMRI and Pathological Stage and Its Influence on Nerve-Sparing Surgery in Patients with High-Risk Prostate Cancer

Background: We aimed to determine the concordance between the radiologic stage (rT), using multiparametric magnetic resonance imaging (mpMRI), and pathologic stage (pT) in patients with high-risk prostate cancer and its influence on nerve-sparing surgery compared to the use of the intraoperative frozen section technique (IFST). Methods: The concordance between rT and pT and the rates of nerve-sparing surgery and positive surgical margin were assessed for patients with high-risk prostate cancer who underwent radical prostatectomy. Results: The concordance between the rT and pT stages was shown in 66.4% (n = 77) of patients with clinical high-risk prostate cancer. The detection of patients with extraprostatic disease (≥pT3) by preoperative mpMRI showed a sensitivity, negative predictive value and accuracy of 65.1%, 51.7% and 67.5%. In addition to the suspicion of extraprostatic disease in mpMRI (≥rT3), 84.5% (n = 56) of patients with ≥rT3 underwent primary nerve-sparing surgery with IFST, resulting in 94.7% (n = 54) of men with at least unilateral nerve-sparing surgery after secondary resection with a positive surgical margin rate related to an IFST of 1.8% (n = 1). Conclusion: Patients with rT3 should not be immediately excluded from nerve-sparing surgery, as by using IFST some of these patients can safely undergo nerve-sparing surgery.

The Prognostic Value of PI-RADS Score in CyberKnife Ultra-Hypofractionated Radiotherapy for Localized Prostate Cancer

Prostate Imaging-Reporting and Data System (PI-RADS) has been widely implemented as a diagnostic tool for significant prostate cancer (PCa); less is known about its prognostic value, especially in the setting of primary radiotherapy. We aimed to analyze the association between PI-RADS v. 2.1 classification and risk of metastases, based on a group of 152 patients treated with ultra-hypofractionated stereotactic CyberKnife radiotherapy for localized low or intermediate risk-group prostate cancer. We found that all distant failures (n = 5) occurred in patients diagnosed with a PI-RADS score of 5, and axial measurements of the target lesion were associated with the risk of developing metastases (p < 0.001). The best risk stratification model (based on a combination of greatest dimension, the product of multiplication of PI-RADS target lesion axial measurements, and age) achieved a c-index of 0.903 (bootstrap-validated bias-corrected 95% CI: 0.848−0.901). This creates a hypothesis that PI-RADS 5 and the size of the target lesion are important prognostic factors in early-stage PCa patients and should be considered as an adverse prognostic measure for patients undergoing early treatment such as radiation or focal therapy.

Comparison of prostate imaging reporting and data system v2.1 and 2 in transition and peripheral zones: evaluation of interreader agreement and diagnostic performance in detecting clinically significant prostate cancer

OBJECTIVE

To evaluate the interreader agreement and diagnostic performance of the Prostate Imaging Reporting and Data System (PI-RADS) v. 2.1, in comparison with v. 2.

METHODS

Institutional review board approval was obtained for this retrospective study. 77 consecutive patients who underwent a prostate multiparametric magnetic resonance imaging at 3.0 T before radical prostatectomy were included. Four radiologists (two experienced uroradiologists and two inexperienced radiologists) independently scored eight regions [six peripheral zones (PZ) and two transition zones (TZ)] using v. 2.1 and v. 2. Interreader agreement was assessed using κ statistics. To evaluate diagnostic performance for clinically significant prostate cancer (csPC), area under the curve (AUC) was estimated.

RESULTS

228 regions were pathologically diagnosed as positive for csPC. With a cut-off ≥3, the agreement among all readers was better with v. 2.1 than v. 2 in TZ, PZ, or both zones combined (κ-value: TZ, 0.509 vs 0.414; PZ, 0.686 vs 0.568; both zones combined, 0.644 vs 0.531). With a cut-off ≥4, the agreement among all readers was also better with v. 2.1 than v. 2 in the PZ or both zones combined (κ-value: PZ, 0.761 vs 0.701; both zones combined, 0.756 vs 0.709). For all readers, AUC with v. 2.1 was higher than with v. 2 (TZ, 0.826-0.907 vs 0.788-0.856; PZ, 0.857-0.919 vs 0.853-0.902).

CONCLUSION

Our study suggests that the PI-RADS v. 2.1 could improve the interreader agreement and might contribute to improved diagnostic performance compared with v. 2.

ADVANCES IN KNOWLEDGE

PI-RADS v. 2.1 has a potential to improve interreader variability and diagnostic performance among radiologists with different levels of expertise.

Investigating the heterogeneity of viscoelastic properties in prostate cancer using MR elastography at 9.4T in fresh prostatectomy specimens

PURPOSE

To quantify the heterogeneity of viscoelastic tissue properties in prostatectomy specimens from men with prostate cancer (PC) using MR elastography (MRE) with histopathology as reference.

METHODS

Twelve fresh prostatectomy specimens were examined in a preclinical 9.4T MRI scanner. Maps of the complex shear modulus (|G*| in kPa) with its real and imaginary part (G' and G" in kPa) were calculated at 500 Hz. Prostates were divided into 12 segments for segment-wise measurement of viscoelastic properties and histopathology. Coefficients of variation (CVs in %) were calculated for quantification of heterogeneity.

RESULTS

Group-averaged values of cancerous vs. benign segments were significantly increased: |G*| of 12.13 kPa vs. 6.14 kPa, G' of 10.84 kPa vs. 5.44 kPa and G" of 5.45 kPa vs. 2.92 kPa, all p < 0.001. In contrast, CVs were significantly increased for benign segments: 23.59% vs. 26.32% (p = 0.014) for |G*|, 27.05% vs. 37.84% (p < 0.003) for G', and 36.51% vs. 50.37% (p = 0.008) for G".

DISCUSSION

PC is characterized by a stiff yet homogeneous biomechanical signature, which may be due to the unique nondestructive growth pattern of PC with intervening stroma, providing a rigid scaffold in the affected area. In turn, increased heterogeneity in benign prostate segments may be attributable to the presence of different prostate zones with involvement by specific nonmalignant pathology.

Using IVIM Parameters to Differentiate Prostate Cancer and Contralateral Normal Tissue through Fusion of MRI Images with Whole-Mount Pathology Specimen Images by Control Point Registration Method

The intravoxel incoherent motion (IVIM) model may enhance the clinical value of multiparametric magnetic resonance imaging (mpMRI) in the detection of prostate cancer (PCa). However, while past IVIM modeling studies have shown promise, they have also reported inconsistent results and limitations, underscoring the need to further enhance the accuracy of IVIM modeling for PCa detection. Therefore, this study utilized the control point registration toolbox function in MATLAB to fuse T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) MRI images with whole-mount pathology specimen images in order to eliminate potential bias in IVIM calculations. Sixteen PCa patients underwent prostate MRI scans before undergoing radical prostatectomies. The image fusion method was then applied in calculating the patients’ IVIM parameters. Furthermore, MRI scans were also performed on 22 healthy young volunteers in order to evaluate the changes in IVIM parameters with aging. Among the full study cohort, the f parameter was significantly increased with age, while the D* parameter was significantly decreased. Among the PCa patients, the D and ADC parameters could differentiate PCa tissue from contralateral normal tissue, while the f and D* parameters could not. The presented image fusion method also provided improved precision when comparing regions of interest side by side. However, further studies with more standardized methods are needed to further clarify the benefits of the presented approach and the different IVIM parameters in PCa characterization.

Application Analysis of 124I-PPMN for Enhanced Retention in Tumors of Prostate Cancer Xenograft Mice

Background

In recent years, nuclear medicine imaging and therapy for prostate cancer have radically changed through the introduction of radiolabeled prostate-specific membrane antigen (PSMA)-binding peptides. However, these small molecular probes have some inherent limitations, including high nephrotoxicity and short circulation time, which limits their utility in biological systems.

Methods and Results

In this study, organic melanin nanoparticles were used to directly label the long half-life radionuclide ¹²⁴I (t_1/2=100.8 h), and PSMA small molecular groups were efficiently bonded on the surface of nanoparticles to construct the PSMA-targeted long-retention nanoprobe ¹²⁴I-PPMN, which has the potential to increase tumor uptake and prolong residence time. The results showed that the nanoprobe could substantially aggregate in the tumors of prostate cancer xenograft mice and was visible for more than 72 h. Positron Emission Computed Tomography (PET) imaging showed that the nanoprobe could be used for precise imaging of prostate cancer with high expression of PSMA. In addition, organic melanin nanoparticles labeled with an elemental radionuclide achieved a stable, metal-free structure. Cell experiments and mouse toxicity experiments indicated that the nanoprobe has high safety.

Conclusion

The new nanoprobe constructed in this study has high specificity and biocompatibility. In the future, combined with the multifunctional potential of melanin nanoparticles, this nanoprobe is expected to be used in the integrated theranostics of prostate cancer.

Deep learning-assisted prostate cancer detection on bi-parametric MRI: minimum training data size requirements and effect of prior knowledge

Objectives

To assess Prostate Imaging Reporting and Data System (PI-RADS)–trained deep learning (DL) algorithm performance and to investigate the effect of data size and prior knowledge on the detection of clinically significant prostate cancer (csPCa) in biopsy-naïve men with a suspicion of PCa.

Methods

Multi-institution data included 2734 consecutive biopsy-naïve men with elevated PSA levels (≥ 3 ng/mL) that underwent multi-parametric MRI (mpMRI). mpMRI exams were prospectively reported using PI-RADS v2 by expert radiologists. A DL framework was designed and trained on center 1 data (n = 1952) to predict PI-RADS ≥ 4 (n = 1092) lesions from bi-parametric MRI (bpMRI). Experiments included varying the number of cases and the use of automatic zonal segmentation as a DL prior. Independent center 2 cases (n = 296) that included pathology outcome (systematic and MRI targeted biopsy) were used to compute performance for radiologists and DL. The performance of detecting PI-RADS 4–5 and Gleason > 6 lesions was assessed on 782 unseen cases (486 center 1, 296 center 2) using free-response ROC (FROC) and ROC analysis.

Results

The DL sensitivity for detecting PI-RADS ≥ 4 lesions was 87% (193/223, 95% CI: 82–91) at an average of 1 false positive (FP) per patient, and an AUC of 0.88 (95% CI: 0.84–0.91). The DL sensitivity for the detection of Gleason > 6 lesions was 85% (79/93, 95% CI: 77–83) @ 1 FP compared to 91% (85/93, 95% CI: 84–96) @ 0.3 FP for a consensus panel of expert radiologists. Data size and prior zonal knowledge significantly affected performance (4%, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p<0.05$$\end{document}p<0.05).

Conclusion

PI-RADS-trained DL can accurately detect and localize Gleason > 6 lesions. DL could reach expert performance using substantially more than 2000 training cases, and DL zonal segmentation.

Key Points

• AI for prostate MRI analysis depends strongly on data size and prior zonal knowledge.

• AI needs substantially more than 2000 training cases to achieve expert performance.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-08320-y.

Role of 68Ga-PSMA-PET/CT for the detection of primary prostate cancer prior to biopsy: a prospective study

Introduction

Prostate-specific membrane antigen (PSMA) positron emission tomography/ computed tomography (PET-CT) is widely used as a staging tool for patients with prostate cancer (PCa). The objective of the study is to assess the diagnostic accuracy of 68Ga-PSMA-PET/CT for PCa, which may help us avoid unnecessary biopsies in patients with intermediate prostate-specific antigen (PSA) levels.

Material and methods

In this prospective study, 81 patients suspected of PCa, with either raised PSA between 4-20 ng/ml or abnormal digital rectal examination (DRE) findings were included. 68Ga-PSMA-PET/CT was performed for all patients followed by transrectal ultrasound (TRUS) guided prostate biopsy. SUVmax (maximum standardized uptake value) was measured and correlated with biopsy results.

Results

Out of 81 patients, 31 (38.3%) patients were found to have malignancy on biopsy. Median SUVmax of biopsy positive patients was 10.4 (IQR 6.5-16.1) and biopsy negative patients (n=50) was 3.5 (IQR 1-4.9), (p <0.001). At a cut-off of 6.15, 68GA-PSMA-PET/CT demonstrated sensitivity of 84%, specificity of 80%, positive predictive value of 72.2%, negative predictive value of 88.9% and accuracy of 81.5% with an AUC of 0.876 (95% CI: 0.799-0.953, p <0.001).

Conclusions

The 68Ga-PSMA-PET/CT helps to localize suspicious lesions and improving the detection of primary prostate cancer. Our findings indicate a significant correlation of SUVmax values with biopsy results. We were also able to determine a cut-off value of SUVmax below which prostate biopsy can be avoided in selected patients.

Bioptic prostatic inflammation correlates with false positive rates of multiparametric magnetic resonance imaging in detecting clinically significant prostate cancer

Introduction

The aim of this article was to determine the impact of bioptic prostatic inflammation (PI) on the false positive rate of multiparametric magnetic resonance imaging (mp-MRI) in detecting clinically significant prostate ancer (csPCa).

Material and methods

Our prostate biopsy database was queried to identify patients who underwent mp-MRI before PB at our institution. A dedicated uropathologist prospectively assessed bioptic PI using the Irani scores. We evaluated the association between mp-MRI findings, bioptic Gleason grade (GG) and aggressiveness of PI, and PCa detection.

Results

In total, 366 men were included. In patients with Prostate Imaging Reporting and Data System (PIRADS) 4-5 lesions, the csPCa (GG ≥2) rate was significantly higher in those with low-grade than in those with high-grade PI (36% vs 29.7%; p = 0.002), and in those with low-aggressive than in those with high-aggressive PI (37.7% vs 30.1%; p = 0.0003). The false positive rates of PIRADS 4–5 lesions for any PCa were 34.2% and 57.8% for low- and high-grade PI, respectively (p = 0.002); similarly, they were 29.5% and 59.4% for mildly and highly-aggressive PI (p = 0.0003). Potential study limitations include its retrospective analysis and single-center study and lack of assessment of the type of PI.

Conclusions

Bioptic PI directly correlates with false positive rates of mp-MRI in detecting csPCa. Clinicians should be aware that PI remains the most common pitfall of mp-MRI.

Combined Transfer Learning and Test-Time Augmentation Improves Convolutional Neural Network-Based Semantic Segmentation of Prostate Cancer from Multi-Parametric MR Images

PURPOSE

Multiparametric MRI (mp-MRI) is a widely used tool for diagnosing and staging prostate cancer. The purpose of this study was to evaluate whether transfer learning, unsupervised pre-training and test-time augmentation significantly improved the performance of a convolutional neural network (CNN) for pixel-by-pixel prediction of cancer vs. non-cancer using mp-MRI datasets.

METHODS

154 subjects undergoing mp-MRI were prospectively recruited, 16 of whom subsequently underwent radical prostatectomy. Logistic regression, random forest and CNN models were trained on mp-MRI data using histopathology as the gold standard. Transfer learning, unsupervised pre-training and test-time augmentation were used to boost CNN performance. Models were evaluated using Dice score and area under the receiver operating curve (AUROC) with leave-one-subject-out cross validation. Permutation feature importance testing was performed to evaluate the relative value of each MR contrast to CNN model performance. Statistical significance (p<0.05) was determined using the paired Wilcoxon signed rank test with Benjamini-Hochberg correction for multiple comparisons.

RESULTS

Baseline CNN outperformed logistic regression and random forest models. Transfer learning and unsupervised pre-training did not significantly improve CNN performance over baseline; however, test-time augmentation resulted in significantly higher Dice scores over both baseline CNN and CNN plus either of transfer learning or unsupervised pre-training. The best performing model was CNN with transfer learning and test-time augmentation (Dice score of 0.59 and AUROC of 0.93). The most important contrast was apparent diffusion coefficient (ADC), followed by Ktrans and T2, although each contributed significantly to classifier performance.

CONCLUSIONS

The addition of transfer learning and test-time augmentation resulted in significant improvement in CNN segmentation performance in a small set of prostate cancer mp-MRI data. Results suggest that these techniques may be more broadly useful for the optimization of deep learning algorithms applied to the problem of semantic segmentation in biomedical image datasets. However, further work is needed to improve the generalizability of the specific model presented herein.

A Novel Deep Learning Based Computer-Aided Diagnosis System Improves the Accuracy and Efficiency of Radiologists in Reading Biparametric Magnetic Resonance Images of the Prostate: Results of a Multireader, Multicase Study

OBJECTIVE

The aim of this study was to evaluate the effect of a deep learning based computer-aided diagnosis (DL-CAD) system on radiologists' interpretation accuracy and efficiency in reading biparametric prostate magnetic resonance imaging scans.

MATERIALS AND METHODS

We selected 100 consecutive prostate magnetic resonance imaging cases from a publicly available data set (PROSTATEx Challenge) with and without histopathologically confirmed prostate cancer. Seven board-certified radiologists were tasked to read each case twice in 2 reading blocks (with and without the assistance of a DL-CAD), with a separation between the 2 reading sessions of at least 2 weeks. Reading tasks were to localize and classify lesions according to Prostate Imaging Reporting and Data System (PI-RADS) v2.0 and to assign a radiologist's level of suspicion score (scale from 1-5 in 0.5 increments; 1, benign; 5, malignant). Ground truth was established by consensus readings of 3 experienced radiologists. The detection performance (receiver operating characteristic curves), variability (Fleiss κ), and average reading time without DL-CAD assistance were evaluated.

RESULTS

The average accuracy of radiologists in terms of area under the curve in detecting clinically significant cases (PI-RADS ≥4) was 0.84 (95% confidence interval [CI], 0.79-0.89), whereas the same using DL-CAD was 0.88 (95% CI, 0.83-0.94) with an improvement of 4.4% (95% CI, 1.1%-7.7%; P = 0.010). Interreader concordance (in terms of Fleiss κ) increased from 0.22 to 0.36 (P = 0.003). Accuracy of radiologists in detecting cases with PI-RADS ≥3 was improved by 2.9% (P = 0.10). The median reading time in the unaided/aided scenario was reduced by 21% from 103 to 81 seconds (P < 0.001).

CONCLUSIONS

Using a DL-CAD system increased the diagnostic accuracy in detecting highly suspicious prostate lesions and reduced both the interreader variability and the reading time.

The efficacy and reliability of VI-RADS in determining candidates for repeated transurethral resection in patients with high-risk non-muscle invasive bladder cancer

OBJECTIVE

Our study aims to evaluate the efficiency and reliability of Vesical Imaging Reporting Data System (VI-RADS) in prospectively identifying the patients to undergo RE-TURBT in the management of patients with high-risk non-muscle invasive Bladder Cancer(HR-NMIBC).The secondary objective was to evaluate the performance of the VI-RADS scoring system in differentiating between muscle-invasive bladder cancer (MIBC) and non-muscle invasive bladder cancer(NMIBC) prospectively.

METHODS

The study included 330 patients who underwent transurethral resection of bladder tumour(TURBT) for Bladder Cancer (BC) in our clinic. All patients underwent multiparametric-magnetic resonance imaging (Mp-MRI) before the operation and VI-RADS scoring was administered. The cut-off value of VI-RADS was accepted as three and above. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated for the differentiation between NMIBC and MIBC distinction in all patients. Receiver operating characteristic (ROC) analysis was performed to evaluate the performance of the VI-RADS scoring system. In the second phase of the study, patients with MIBC and low-risk NMIBC (LR-NMBIC) were excluded and 158 patients with HR-NMIBC were included, and their sensitivity, specificity, PPV and NPV values were measured. ROC analysis was performed.

RESULTS

In all patients, sensitivity, specificity, PPV and NPV values of the VI-RADS scoring in the differentiation of MIBC and NMIBC were 91.3, 91.8, 81.7 and 96.3 respectively. The AUC value was 0.934 (95%CI: 0.903-0.964). Sensitivity, specificity, PPV and NPV values were found to be 87, 91.8, 74.1, 95.2 in the evaluation specifically made for patients with HR-NMIBC. The AUC value was 0.900 (95% CI:0.843-0.957). Inter-reader agreement was excellent (Ƙ = 0.90, 95% CI:0.71-0.95).

CONCLUSIONS

The VI-RADS scoring system is an effective and reliable method in determining the patients who will undergo RE-TURBT and in differentiating MIBC and NMIBC.

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

PURPOSE

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

METHOD

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

RESULTS

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

CONCLUSION

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

Robotic Transrectal Computed Tomographic Ultrasound with Artificial Neural Network Analysis: First Validation and Comparison with MRI-Guided Biopsies and Radical Prostatectomy

INTRODUCTION

There is still a lack of availability of high-quality multiparametric magnetic resonance imaging (mpMRI) interpreted by experienced uro-radiologists to rule out clinically significant PC (csPC). Consequently, we developed a new imaging method based on computed tomographic ultrasound (US) supported by artificial neural network analysis (ANNA).

METHODS

Two hundred and two consecutive patients with visible mpMRI lesions were scanned and recorded by robotic CT-US during mpMRI-TRUS biopsy. Only significant index lesions (ISUP ≥2) verified by whole-mount pathology were retrospectively analyzed. Their visibility was reevaluated by 2 blinded investigators by grayscale US and ANNA.

RESULTS

In the cohort, csPC was detected in 105 cases (52%) by mpMRI-TRUS biopsy. Whole-mount histology was available in 44 cases (36%). In this subgroup, mean PSA level was 8.6 ng/mL, mean prostate volume was 33 cm3, and mean tumor volume was 0.5 cm3. Median PI-RADS and ISUP of index lesions were 4 and 3, respectively. Index lesions were visible in grayscale US and ANNA in 25 cases (57%) and 30 cases (68%), respectively. Combining CT-US-ANNA, we detected index lesions in 35 patients (80%).

CONCLUSIONS

The first results of multiparametric CT-US-ANNA imaging showed promising detection rates in patients with csPC. US imaging with ANNA has the potential to complement PC diagnosis.

Detection and PI-RADS classification of focal lesions in prostate MRI: Performance comparison between a deep learning-based algorithm (DLA) and radiologists with various levels of experience

PURPOSE

To compare the performance of lesion detection and Prostate Imaging-Reporting and Data System (PI-RADS) classification between a deep learning-based algorithm (DLA), clinical reports and radiologists with different levels of experience in prostate MRI.

METHODS

This retrospective study included 121 patients who underwent prebiopsy MRI and prostate biopsy. More than five radiologists (Reader groups 1, 2: residents; Readers 3, 4: less-experienced radiologists; Reader 5: expert) independently reviewed biparametric MRI (bpMRI). The DLA results were obtained using bpMRI. The reference standard was based on pathologic reports. The diagnostic performance of the PI-RADS classification of DLA, clinical reports, and radiologists was analyzed using AUROC. Dichotomous analysis (PI-RADS cutoff value ≥ 3 or 4) was performed, and the sensitivities and specificities were compared using McNemar's test.

RESULTS

Clinically significant cancer [CSC, Gleason score ≥ 7] was confirmed in 43 patients (35.5%). The AUROC of the DLA (0.828) for diagnosing CSC was significantly higher than that of Reader 1 (AUROC, 0.706; p = 0.011), significantly lower than that of Reader 5 (AUROC, 0.914; p = 0.013), and similar to clinical reports and other readers (p = 0.060-0.661). The sensitivity of DLA (76.7%) was comparable to those of all readers and the clinical reports at a PI-RADS cutoff value ≥ 4. The specificity of the DLA (85.9%) was significantly higher than those of clinical reports and Readers 2-3 and comparable to all others at a PI-RADS cutoff value ≥ 4.

CONCLUSIONS

The DLA showed moderate diagnostic performance at a level between those of residents and an expert in detecting and classifying according to PI-RADS. The performance of DLA was similar to that of clinical reports from various radiologists in clinical practice.

Feasibility of diffusion weighting with a local inside-out nonlinear gradient coil for prostate MRI

PURPOSE

Prostate cancer remains the 2nd leading cancer killer of men, yet it is also a disease with a high rate of overtreatment. Diffusion weighted imaging (DWI) has shown promise as a reliable, grade-sensitive imaging method, but it is limited by low image quality. Currently, DWI quality image is directly related to low gradient amplitudes, since weak gradients must be compensated with long echo times.

METHODS

We propose a new type of MRI accessory, an "inside-out" and nonlinear gradient, whose sole purpose is to deliver diffusion encoding to a region of interest. Performance was simulated in OPERA and the resulting fields were used to simulate DWI with two compartment and kurtosis models. Experiments with a nonlinear head gradient prove the accuracy of DWI and ADC maps diffusion encoded with nonlinear gradients.

RESULTS

Simulations validated thermal and mechanical safety while showing a 5 to 10-fold increase in gradient strength over prostate. With these strengths, lesion CNR in ADC maps approximately doubled for a range of anatomical positions. Proof-of-principle experiments show that spatially varying b-values can be corrected for accurate DWI and ADC.

CONCLUSIONS

Dedicated nonlinear diffusion encoding hardware could improve prostate DWI.

Tumor contact length of prostate cancer determined by a three-dimensional method on multiparametric magnetic resonance imaging predicts extraprostatic extension and biochemical recurrence

OBJECTIVE

To evaluate the clinical benefit of tumor contact length as a predictor of pathological extraprostatic extension and biochemical recurrence in patients undergoing prostatectomy.

METHODS

A total of 91 patients who underwent 3T multiparametric magnetic resonance imaging before prostatectomy from April 2014 to July 2019 were included. A total of 94 prostate cancer foci were analyzed retrospectively. We evaluated maximum tumor contact length, which was determined to be the maximum value in the three-dimensional directions, as a predictor of pathological extraprostatic extension and biochemical recurrence.

RESULTS

A total of 19 lesions (20.2%) had positive pathological extraprostatic extension. Areas under the curves showed maximum tumor contact length to be a significantly better parameter to predict pathological extraprostatic extension than the Prostate Imaging Reporting and Data System (P = 0.002), tumor maximal diameter (P = 0.001), prostate-specific antigen (P = 0.020), Gleason score (P < 0.001), and clinical T stage (P < 0.001). Multivariate analysis showed maximum tumor contact length (P = 0.003) to be an independent risk factor for predicting biochemical recurrence. We classified the patients using preoperative factors (prostate-specific antigen >10, Gleason score >3 + 4 and maximum tumor contact length >10 mm) into three groups: (i) high-risk group (patients having all factors); (ii) intermediate-risk group (patients having two of three factors); and (iii) low-risk group (patients having only one or none of the factors). Kaplan-Meier curves showed that the high-risk group had significantly worse biochemical recurrence than the intermediate-risk group (P = 0.042) and low-risk group (P < 0.001).

CONCLUSIONS

Our findings suggest that maximum tumor contact length is an independent predictor of pathological extraprostatic extension and biochemical recurrence. A risk stratification system using prostate-specific antigen, Gleason score and maximum tumor contact length might be useful for preoperative assessment of prostate cancer patients.

Increasing Utilization of MRI Before Prostate Biopsy in Black and Non-Black Men: An Analysis of the SEER-Medicare Cohort

OBJECTIVE. The potential for significant disparities exists in the setting of increased adoption of prostate MRI. We sought to assess temporal trends in the utilization of MRI before prostate biopsy in a nationally representative sample. MATERIALS AND METHODS. Using the SEER-Medicare linked database, we identified men undergoing prostate biopsy who had an MRI within 6 months of diagnosis of prostate cancer. Men were stratified according to whether they were biopsy naive or had undergone a prior negative prostate biopsy. RESULTS. We identified 82,483 men undergoing prostate biopsy in SEER-Medicare from 2008 to 2015 of whom 78,253 were biopsy naive and 4230 had a known prior negative biopsy. We found that the percentage of patients who received an MRI before biopsy has increased from 2008 to 2015 in biopsy-naive men (0.5-8.2%; p < .001), men with a prior negative biopsy (1.4-25.5%; p < .001), and overall (0.5-9.2%; p < .001). On multivariable modeling, the odds ratio (OR) of a patient undergoing an MRI before biopsy for Black men (OR, 0.4; 95% CI, 0.3-0.5; p < .001) was half that of White men, and the OR of MRI before biopsy in men from the Northeast (OR, 3.4; 95% CI, 2.8-4.3; p < .001) was more than three times that of men from the West. CONCLUSION. The steady overall increase in the utilization of MRI before prostate biopsy since 2008 has been associated with significant racial and regional disparities in utilization.

Usability and diagnostic accuracy of different MRI/ultrasound-guided fusion biopsy systems for the detection of clinically significant and insignificant prostate cancer: a prospective cohort study

PURPOSE

To explore the usability and diagnostic accuracy for prostate cancer of three multiparametric magnetic resonance imaging (mpMRI)/transrectal ultrasound (TRUS)-guided fusion biopsy systems operated by the same urologists.

METHODS

We performed a prospective, observational study including patients that underwent prostate biopsy due to a visible lesion in mpMRI (PI-RADS ≥ 3). We consecutively assessed two platforms with a rigid image registration (BioJet, D&K Technologies and UroNav, Invivo Corporation) and one with an elastic registration (Trinity, KOELIS). Four urologists evaluated each fusion system in terms of usability based on the System Usability Scale and diagnostic accuracy based on the detection of prostate cancer.

RESULTS

We enrolled 60 consecutive patients that received mpMRI/TRUS-guided prostate biopsy with the BioJet (n = 20), UroNav (n = 20) or Trinity (n = 20) fusion system. Comparing the rigid with the elastic registration systems, the rigid registration systems were more user-friendly compared to the elastic registration systems (p = 0.012). Similarly, the prostate biopsy with the rigid registration systems had a shorter duration compared to the elastic registration system (p < 0.001). Overall, 40 cases of prostate cancer were detected. Of them, both the BioJet and UroNav fusion systems detected 13 prostate cancer cases, while the Trinity detected 14. No significant differences were demonstrated among the three fusion biopsy systems in terms of highest ISUP Grade Group (p > 0.99).

CONCLUSIONS

Rigid fusion biopsy systems are easier to use and provide shorter operative time compared to elastic systems, while both types of platforms display similar detection rates for prostate cancer. Still, further high-quality, long-term results are mandatory.