"Seeing Is Believing": Additive Utility of 68Ga-PSMA-11 PET/CT in Prostate Cancer Diagnosis.

Widespread adoption of mpMRI has led to a decrease in the number of patients requiring prostate biopsies. 68Ga-PSMA-11 PET/CT has demonstrated added benefits in identifying csPCa. Integrating the use of these imaging techniques may hold promise for predicting the presence of csPCa without invasive biopsy. A retrospective analysis of 42 consecutive patients who underwent mpMRI, 68Ga-PSMA-11 PET/CT, prostatic biopsy, and radical prostatectomy (RP) was carried out. A lesion-based model (n = 122) using prostatectomy histopathology as reference standard was used to analyze the accuracy of 68Ga-PSMA-11 PET/CT, mpMRI alone, and both in combination to identify ISUP-grade group ≥ 2 lesions. 68Ga-PSMA-11 PET/CT demonstrated greater specificity and positive predictive value (PPV), with values of 73.3% (vs. 40.0%) and 90.1% (vs. 82.2%), while the mpMRI Prostate Imaging Reporting and Data System (PI-RADS) 4-5 had better sensitivity and negative predictive value (NPV): 90.2% (vs. 78.5%) and 57.1% (vs. 52.4%), respectively. When used in combination, the sensitivity, specificity, PPV, and NPV were 74.2%, 83.3%, 93.2%, and 51.0%, respectively. Subgroup analysis of PI-RADS 3, 4, and 5 lesions was carried out. For PI-RADS 3 lesions, 68Ga-PSMA-11 PET/CT demonstrated a NPV of 77.8%. For PI-RADS 4-5 lesions, 68Ga-PSMA-11 PET/CT achieved PPV values of 82.1% and 100%, respectively, with an NPV of 100% in PI-RADS 5 lesions. A combination of 68Ga-PSMA-11 PET/CT and mpMRI improved the radiological diagnosis of csPCa. This suggests that avoidance of prostate biopsy prior to RP may represent a valid option in a selected subgroup of high-risk patients with a high suspicion of csPCa on mpMRI and 68Ga-PSMA-11 PET/CT.

Evaluation of a Cascaded Deep Learning-based Algorithm for Prostate Lesion Detection at Biparametric MRI.

Background Multiparametric MRI (mpMRI) improves prostate cancer (PCa) detection compared with systematic biopsy, but its interpretation is prone to interreader variation, which results in performance inconsistency. Artificial intelligence (AI) models can assist in mpMRI interpretation, but large training data sets and extensive model testing are required. Purpose To evaluate a biparametric MRI AI algorithm for intraprostatic lesion detection and segmentation and to compare its performance with radiologist readings and biopsy results. Materials and Methods This secondary analysis of a prospective registry included consecutive patients with suspected or known PCa who underwent mpMRI, US-guided systematic biopsy, or combined systematic and MRI/US fusion-guided biopsy between April 2019 and September 2022. All lesions were prospectively evaluated using Prostate Imaging Reporting and Data System version 2.1. The lesion- and participant-level performance of a previously developed cascaded deep learning algorithm was compared with histopathologic outcomes and radiologist readings using sensitivity, positive predictive value (PPV), and Dice similarity coefficient (DSC). Results A total of 658 male participants (median age, 67 years [IQR, 61-71 years]) with 1029 MRI-visible lesions were included. At histopathologic analysis, 45% (294 of 658) of participants had lesions of International Society of Urological Pathology (ISUP) grade group (GG) 2 or higher. The algorithm identified 96% (282 of 294; 95% CI: 94%, 98%) of all participants with clinically significant PCa, whereas the radiologist identified 98% (287 of 294; 95% CI: 96%, 99%; P = .23). The algorithm identified 84% (103 of 122), 96% (152 of 159), 96% (47 of 49), 95% (38 of 40), and 98% (45 of 46) of participants with ISUP GG 1, 2, 3, 4, and 5 lesions, respectively. In the lesion-level analysis using radiologist ground truth, the detection sensitivity was 55% (569 of 1029; 95% CI: 52%, 58%), and the PPV was 57% (535 of 934; 95% CI: 54%, 61%). The mean number of false-positive lesions per participant was 0.61 (range, 0-3). The lesion segmentation DSC was 0.29. Conclusion The AI algorithm detected cancer-suspicious lesions on biparametric MRI scans with a performance comparable to that of an experienced radiologist. Moreover, the algorithm reliably predicted clinically significant lesions at histopathologic examination. ClinicalTrials.gov Identifier: NCT03354416 © RSNA, 2024 Supplemental material is available for this article.

Automated Detection and Grading of Extraprostatic Extension of Prostate Cancer at MRI via Cascaded Deep Learning and Random Forest Classification.

RATIONALE AND OBJECTIVES: Extraprostatic extension (EPE) is well established as a significant predictor of prostate cancer aggression and recurrence. Accurate EPE assessment prior to radical prostatectomy can impact surgical approach. We aimed to utilize a deep learning-based AI workflow for automated EPE grading from prostate T2W MRI, ADC map, and High B DWI. MATERIAL AND METHODS: An expert genitourinary radiologist conducted prospective clinical assessments of MRI scans for 634 patients and assigned risk for EPE using a grading technique. The training set and held-out independent test set consisted of 507 patients and 127 patients, respectively. Existing deep-learning AI models for prostate organ and lesion segmentation were leveraged to extract area and distance features for random forest classification models. Model performance was evaluated using balanced accuracy, ROC AUCs for each EPE grade, as well as sensitivity, specificity, and accuracy compared to EPE on histopathology. RESULTS: A balanced accuracy score of .390 ± 0.078 was achieved using a lesion detection probability threshold of 0.45 and distance features. Using the test set, ROC AUCs for AI-assigned EPE grades 0-3 were 0.70, 0.65, 0.68, and 0.55 respectively. When using EPE≥ 1 as the threshold for positive EPE, the model achieved a sensitivity of 0.67, specificity of 0.73, and accuracy of 0.72 compared to radiologist sensitivity of 0.81, specificity of 0.62, and accuracy of 0.66 using histopathology as the ground truth. CONCLUSION: Our AI workflow for assigning imaging-based EPE grades achieves an accuracy for predicting histologic EPE approaching that of physicians. This automated workflow has the potential to enhance physician decision-making for assessing the risk of EPE in patients undergoing treatment for prostate cancer due to its consistency and automation.

Deep Learning-Based Detection and Classification of Bone Lesions on Staging Computed Tomography in Prostate Cancer: A Development Study.

RATIONALE AND OBJECTIVES: Efficiently detecting and characterizing metastatic bone lesions on staging CT is crucial for prostate cancer (PCa) care. However, it demands significant expert time and additional imaging such as PET/CT. We aimed to develop an ensemble of two automated deep learning AI models for 1) bone lesion detection and segmentation and 2) benign vs. metastatic lesion classification on staging CTs and to compare its performance with radiologists. MATERIALS AND METHODS: This retrospective study developed two AI models using 297 staging CT scans (81 metastatic) with 4601 benign and 1911 metastatic lesions in PCa patients. Metastases were validated by follow-up scans, bone biopsy, or PET/CT. Segmentation AI (3DAISeg) was developed using the lesion contours delineated by a radiologist. 3DAISeg performance was evaluated with the Dice similarity coefficient, and classification AI (3DAIClass) performance on AI and radiologist contours was assessed with F1-score and accuracy. Training/validation/testing data partitions of 70:15:15 were used. A multi-reader study was performed with two junior and two senior radiologists within a subset of the testing dataset (n = 36). RESULTS: In 45 unseen staging CT scans (12 metastatic PCa) with 669 benign and 364 metastatic lesions, 3DAISeg detected 73.1% of metastatic (266/364) and 72.4% of benign lesions (484/669). Each scan averaged 12 extra segmentations (range: 1-31). All metastatic scans had at least one detected metastatic lesion, achieving a 100% patient-level detection. The mean Dice score for 3DAISeg was 0.53 (median: 0.59, range: 0-0.87). The F1 for 3DAIClass was 94.8% (radiologist contours) and 92.4% (3DAISeg contours), with a median false positive of 0 (range: 0-3). Using radiologist contours, 3DAIClass had PPV and NPV rates comparable to junior and senior radiologists: PPV (semi-automated approach AI 40.0% vs. Juniors 32.0% vs. Seniors 50.0%) and NPV (AI 96.2% vs. Juniors 95.7% vs. Seniors 91.9%). When using 3DAISeg, 3DAIClass mimicked junior radiologists in PPV (pure-AI 20.0% vs. Juniors 32.0% vs. Seniors 50.0%) but surpassed seniors in NPV (pure-AI 93.8% vs. Juniors 95.7% vs. Seniors 91.9%). CONCLUSION: Our lesion detection and classification AI model performs on par with junior and senior radiologists in discerning benign and metastatic lesions on staging CTs obtained for PCa.

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

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

Evaluation of Compressed SENSE on Image Quality and Reduction of MRI Acquisition Time: A Clinical Validation Study.

RATIONALE AND OBJECTIVES: To evaluate the effect of compressed SENSE (CS) in clinical settings on scan time reduction and image quality. MATERIALS AND METHODS: Ninety-five magnetic resonance imaging (MRI) scans from different anatomical regions were acquired, consisting of a standard protocol sequence (SS) and sequence accelerated with CS. Anonymized paired sequences were randomly displayed and rated by six blinded subspecialty radiologists. Side-by-side evaluation on perceived sharpness, perceived signal-to-noise-ratio (SNR), lesion conspicuity, and artifacts were compared and scored on a five-point Likert scale, and individual image quality was evaluated on a four-point Likert scale. RESULTS: CS reduced overall scan time by 32% while maintaining acceptable MRI quality for all regions. The largest time savings were seen in the spine (mean = 68 seconds, 44% reduction) followed by the brain (mean = 86 seconds, 37% reduction). The sequence with maximum time savings was intracranial 3D-time-of-flight magnetic resonance angiography (202 seconds, 56% reduction). CS was mildly inferior to SS on perceived sharpness, perceived SNR, and lesion conspicuity (mean scores = 2.32-2.96, P < .001 [1: SS superior; 3: equivalent; 5: CS superior]). CS was equivalent to SS for joint and body scans on overall image quality (CS = 3.02-3.37, SS = 3.04-3.68, P > .05, [1: lowest quality and 4: highest quality]). The overall image quality of CS was slightly less for brain and spine scans (mean CS = 2.79-3.05, mean SS = 3.13-3.43, P = .021) but still diagnostic. Good overall clinical acceptance for CS (88%) was noted with full clinical acceptance for body scans (100%) and high acceptance for other regions (68%-95%). CONCLUSION: CS significantly reduced MR acquisition time while maintaining acceptable image quality. The implementation of CS may improve departmental workflows and enhance patient care.

Deep Learning-Based Interpretable AI for Prostate T2W MRI Quality Evaluation.

RATIONALE AND OBJECTIVES: Prostate MRI quality is essential in guiding prostate biopsies. However, assessment of MRI quality is subjective with variation. Quality degradation sources exert varying impacts based on the sequence under consideration, such as T2W versus DWI. As a result, employing sequence-specific techniques for quality assessment could yield more advantageous outcomes. This study aims to develop an AI tool that offers a more consistent evaluation of T2W prostate MRI quality, efficiently identifying suboptimal scans while minimizing user bias. MATERIALS AND METHODS: This retrospective study included 1046 patients from three cohorts (ProstateX [n = 347], All-comer in-house [n = 602], enriched bad-quality MRI in-house [n = 97]) scanned between January 2011 and May 2022. An expert reader assigned T2W MRIs a quality score. A train-validation-test split of 70:15:15 was applied, ensuring equal distribution of MRI scanners and protocols across all partitions. T2W quality AI classification model was based on 3D DenseNet121 architecture using MONAI framework. In addition to multiclassification, binary classification was utilized (Classes 0/1 vs. 2). A score of 0 was given to scans considered non-diagnostic or unusable, a score of 1 was given to those with acceptable diagnostic quality with some usability but with some quality distortions present, and a score of 2 was given to those considered optimal diagnostic quality and usability. Partial occlusion sensitivity maps were generated for anatomical correlation. Three body radiologists assessed reproducibility within a subgroup of 60 test cases using weighted Cohen Kappa. RESULTS: The best validation multiclass accuracy of 77.1% (121/157) was achieved during training. In the test dataset, multiclassification accuracy was 73.9% (116/157), whereas binary accuracy was 84.7% (133/157). Sub-class sensitivity for binary quality distortion classification for class 0 was 100% (18/18), and sub-class specificity for T2W classification of absence/minimal quality distortions for class 2 was 90.5% (95/105). All three readers showed moderate to substantial agreement with ground truth (R1-R3 κ = 0.588, κ = 0.649, κ = 0.487, respectively), moderate to substantial agreement with each other (R1-R2 κ = 0.599, R1-R3 κ = 0.612, R2-R3 κ = 0.685), fair to moderate agreement with AI (R1-R3 κ = 0.445, κ = 0.410, κ = 0.292, respectively). AI showed substantial agreement with ground truth (κ = 0.704). 3D quality heatmap evaluation revealed that the most critical non-diagnostic quality imaging features from an AI perspective related to obscuration of the rectoprostatic space (94.4%, 17/18). CONCLUSION: The 3D AI model can assess T2W prostate MRI quality with moderate accuracy and translate whole sequence-level classification labels into 3D voxel-level quality heatmaps for interpretation. Image quality has a significant downstream impact on ruling out clinically significant cancers. AI may be able to help with reproducible identification of MRI sequences requiring re-acquisition with explainability.

Quality of T2-weighted MRI re-acquisition versus deep learning GAN image reconstruction: A multi-reader study.

PURPOSE: To evaluate CycleGAN's ability to enhance T2-weighted image (T2WI) quality. METHOD: A CycleGAN algorithm was used to enhance T2WI quality. 96 patients (192 scans) were identified from patients who underwent multiple axial T2WI due to poor quality on the first attempt (RAD1) and improved quality on re-acquisition (RAD2). CycleGAN algorithm gave DL classifier scores (0-1) for quality quantification and produced enhanced versions of QI1 and QI2 from RAD1 and RAD2, respectively. A subset (n = 20 patients) was selected for a blinded, multi-reader study, where four radiologists rated T2WI on a scale of 1-4 for quality. The multi-reader study presented readers with 60 image pairs (RAD1 vs RAD2, RAD1 vs QI1, and RAD2 vs QI2), allowing for selecting sequence preferences and quantifying the quality changes. RESULTS: The DL classifier correctly discerned 71.9 % of quality classes, with 90.6 % (96/106) as poor quality and 48.8 % (42/86) as diagnostic in original sequences (RAD1, RAD2). CycleGAN images (QI1, QI2) demonstrated quantitative improvements, with consistently higher DL classifier scores than original scans (p < 0.001). In the multi-reader analysis, CycleGAN demonstrated no qualitative improvements, with diminished overall quality and motion in QI2 in most patients compared to RAD2, with noise levels remaining similar (8/20). No readers preferred QI2 to RAD2 for diagnosis. CONCLUSION: Despite quantitative enhancements with CycleGAN, there was no qualitative boost in T2WI diagnostic quality, noise, or motion. Expert radiologists didn't favor CycleGAN images over standard scans, highlighting the divide between quantitative and qualitative metrics.

Surveillance one year post focal cryotherapy for clinically significant prostate cancer using mpMRI and PIRADS v2.1: An initial experience from a prospective phase II mandatory biopsy study.

Objectives: Multiparametric magnetic resonance imaging (mpMRI) surveillance post focal cryotherapy (FT) of prostate cancer is challenging as post treatment artefacts alter mpMRI findings. In this initial experience, we assessed diagnostic performance of mpMRI in detecting clinically significant prostate cancer (csPCa) after FT. Materials and methods: This single-centre phase II prospective clinical trial recruited 28 men with localized csPCa for FT between October 2019 and April 2021. 12-months post FT mpMRI were performed prior to biopsy and sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of all mpMRI positive subjects were analysed. Chi square goodness of fit test correlated biopsy positive PIRADS3 (P3) and PIRADS4/5 lesions with histology grade group. One way ANOVA test assessed performance of ADC values in differentiating csPCa, non csPCa and benign lesions. Results: Sensitivity, specificity, PPV and NPV of mpMRI were 100%, 14.28%, 53.84% and 100% for subjects with histologically proven cancer. Correlation of PIRADS v2.1 scores with histologically proven prostate cancer was statistically significant (p < 0.5). Correlation of P3 lesions with non-csPCa was statistically significant (p < 0.02535). Higher ADC value was associated with benign histology (adjusted odds ratio OR 0.97, 95% confidence interval: 0.94, 0.99) (p = 0.008). Among the malignant lesions, higher ADC value was associated with non-csPCa (adjusted OR: 0.97; 95% CI: 0.95, 0.99) (p = 0.032). Conclusion: mpMRI is highly sensitive in detecting residual cancer. ADC values and PIRADS scores may be of value in differentiating csPCa from non-csPCa with a potential for risk stratification of men requiring re-biopsy versus non-invasive surveillance of remnant prostate.

Deep Learning-Based T2-weighted MR Image Quality Assessment and Its Impact on Prostate Cancer Detection Rates.

BACKGROUND: Image quality evaluation of prostate MRI is important for successful implementation of MRI into localized prostate cancer diagnosis. PURPOSE: To examine the impact of image quality on prostate cancer detection using an in-house previously developed artificial intelligence (AI) algorithm. STUDY TYPE: Retrospective. SUBJECTS: 615 consecutive patients (median age 67 [interquartile range [IQR]: 61-71] years) with elevated serum PSA (median PSA 6.6 [IQR: 4.6-9.8] ng/mL) prior to prostate biopsy. FIELD STRENGTH/SEQUENCE: 3.0T/T2-weighted turbo-spin-echo MRI, high b-value echo-planar diffusion-weighted imaging, and gradient recalled echo dynamic contrast-enhanced. ASSESSMENTS: Scans were prospectively evaluated during clinical readout using PI-RADSv2.1 by one genitourinary radiologist with 17 years of experience. For each patient, T2-weighted images (T2WIs) were classified as high-quality or low-quality based on evaluation of both general distortions (eg, motion, distortion, noise, and aliasing) and perceptual distortions (eg, obscured delineation of prostatic capsule, prostatic zones, and excess rectal gas) by a previously developed in-house AI algorithm. Patients with PI-RADS category 1 underwent 12-core ultrasound-guided systematic biopsy while those with PI-RADS category 2-5 underwent combined systematic and targeted biopsies. Patient-level cancer detection rates (CDRs) were calculated for clinically significant prostate cancer (csPCa, International Society of Urological Pathology Grade Group ≥2) by each biopsy method and compared between high- and low-quality images in each PI-RADS category. STATISTICAL TESTS: Fisher's exact test. Bootstrap 95% confidence intervals (CI). A P value <0.05 was considered statistically significant. RESULTS: 385 (63%) T2WIs were classified as high-quality and 230 (37%) as low-quality by AI. Targeted biopsy with high-quality T2WIs resulted in significantly higher clinically significant CDR than low-quality images for PI-RADS category 4 lesions (52% [95% CI: 43-61] vs. 32% [95% CI: 22-42]). For combined biopsy, there was no significant difference in patient-level CDRs for PI-RADS 4 between high- and low-quality T2WIs (56% [95% CI: 47-64] vs. 44% [95% CI: 34-55]; P = 0.09). DATA CONCLUSION: Higher quality T2WIs were associated with better targeted biopsy clinically significant cancer detection performance for PI-RADS 4 lesions. Combined biopsy might be needed when T2WI is lower quality. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

Local Therapy on Clinically Lymph Node-positive Prostate Cancer: A Systematic Review and Meta-analysis.

CONTEXT: Patients with clinically lymph node-positive (cN1) prostate cancer (PCa) are traditionally regarded to have metastatic disease, and the role of local therapy (LT) in their treatment remains unclear. OBJECTIVE: To evaluate the outcomes of cN1 PCa patients treated with LT, and secondarily to compare between different modalities of LT, including radiotherapy (RT) and radical prostatectomy (RP). EVIDENCE ACQUISITION: A bibliographic search was performed using Medline, Embase, and the Cochrane Library to identify studies comparing the survival outcomes of cN1 PCa patients treated with LT (RT or RP) with those who did not receive any form of LT (observation or androgen deprivation therapy alone). The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) recommendations were followed. Survival outcomes of the addition of LT were assessed using a random-effect model. EVIDENCE SYNTHESIS: A total of 8522 patients across eight studies were included. LT significantly improved overall survival (OS) across all time points from 2 to 10 yr compared with patients without LT, most notably providing a durable benefit in 10-yr OS (odds ratio [OR]: 1.49, 95% confidence interval [CI] 1.06-2.10). Both RT and RP were associated with benefits to both OS and recurrence-free survival, with no significant difference in OS between both modalities in medium-term follow-up (4-yr OR: 0.76, 95% CI 0.41-1.40, p = 0.19). CONCLUSIONS: Regardless of modality, the use of LT in cN1 patients improved OS. Future studies should aim to identify patients who could benefit from LT and include more comprehensive survival data including biochemical recurrence. PATIENT SUMMARY: In this study, we evaluated the outcomes of clinically lymph node-positive (cN1) prostate cancer (PCa) patients treated with local therapy (LT) and compared between different modalities of LT, including radiotherapy (RT) and radical prostatectomy (RP). We found that the addition of LT for cN1 PCa patients leads to a significant improvement in survival outcomes, most notably for overall survival, with no significant difference between RT and RP.

Intensive sampling of the umbra and penumbra improves clinically significant prostate cancer detection and reduces risk of grade group upgrading at radical prostatectomy.

PURPOSE: Our objective is to evaluate the clinically significant prostate cancer detection rate of overlapping and perilesional systematic biopsy cores and its impact on grade group (GG) concordance at prostatectomy. MATERIALS AND METHODS: Biopsy maps of those undergoing MRI-targeted (TB) and systematic biopsy (SB) were reviewed to reclassify systematic cores. Perilesional (PL) cores were defined as adjacent cores within 10 mm of the target lesion ("penumbra") whilst overlap (OL) cores were defined as cores within the ROI itself ("umbra"). All other cores were designated as distant cores (DC). The incremental csPCa detection rate (GG ≥ 2) and the rate of GG upgrading on prostatectomy as OL, PL and DC sequentially added to TB were determined. RESULTS: Out of the 398 patients included, the median number of OL and PL cores was 5 (IQR 4-7) and 5 (IQR 3-6) respectively. OL cores detected more csPCa than PL cores (31 vs 16%, p < 0.001). OL and PL cores improved the csPCa detection rate of TB from 34 to 39% (p < 0.001) and 37% (p = 0.001) respectively. TB+OL+PL had greater csPCa detection compared to just TB+OL (41 vs 39%, p = 0.016) and TB+PL (41 vs 37%, p < 0.001). Of the 104 patients who underwent prostatectomy, GG upgrading rate for TB+OL+PL was lower compared to TB (21 vs 36%, p < 0.001) and was not significantly different compared to TB+OL+PL+DC (21 vs 19%, p = 0.500). CONCLUSION: A biopsy strategy incorporating both intensive sampling of the umbra and penumbra improved csPCa detection and reduced risk of GG upgrading at prostatectomy.

Comparison of MRI-Based Staging and Pathologic Staging for Predicting Biochemical Recurrence of Prostate Cancer After Radical Prostatectomy.

BACKGROUND. Currently most clinical models for predicting biochemical recurrence (BCR) of prostate cancer (PCa) after radical prostatectomy (RP) incorporate staging information from RP specimens, creating a gap in preoperative risk assessment. OBJECTIVE. The purpose of our study was to compare the utility of presurgical staging information from MRI and postsurgical staging information from RP pathology in predicting BCR in patients with PCa. METHODS. This retrospective study included 604 patients (median age, 60 years) with PCa who underwent prostate MRI before RP from June 2007 to December 2018. A single genitourinary radiologist assessed MRI examinations for extraprostatic extension (EPE) and seminal vesicle invasion (SVI) during clinical interpretations. The utility of EPE and SVI on MRI and RP pathology for BCR prediction was assessed through Kaplan-Meier and Cox proportional hazards analyses. Established clinical BCR prediction models, including the University of California San Francisco Cancer of the Prostate Risk Assessment (UCSF-CAPRA) model and the Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) model, were evaluated in a subset of 374 patients with available Gleason grade groups from biopsy and RP pathology; two CAPRA-MRI models (CAPRA-S model with modifications to replace RP pathologic staging features with MRI staging features) were also assessed. RESULTS. Univariable predictors of BCR included EPE on MRI (HR = 3.6), SVI on MRI (HR = 4.4), EPE on RP pathology (HR = 5.0), and SVI on RP pathology (HR = 4.6) (all p < .001). Three-year BCR-free survival (RFS) rates for patients without versus with EPE were 84% versus 59% for MRI and 89% versus 58% for RP pathology, and 3-year RFS rates for patients without versus with SVI were 82% versus 50% for MRI and 83% versus 54% for RP histology (all p < .001). For patients with T3 disease on RP pathology, 3-year RFS rates were 67% and 41% for patients without and with T3 disease on MRI. AUCs of CAPRA models, including CAPRA-MRI models, ranged from 0.743 to 0.778. AUCs were not significantly different between CAPRA-S and CAPRA-MRI models (p > .05). RFS rates were significantly different between low- and intermediate-risk groups for only CAPRA-MRI models (80% vs 51% and 74% vs 44%; both p < .001). CONCLUSION. Presurgical MRI-based staging features perform comparably to postsurgical pathologic staging features for predicting BCR. CLINICAL IMPACT. MRI staging can preoperatively identify patients at high BCR risk, helping to inform early clinical decision-making. TRIAL REGISTRATION. ClinicalTrials.gov NCT00026884 and NCT02594202.

Focal IgG4-related periprostatic "PI-RADS 5" pseudotumor mimicking prostatic adenocarcinoma.

Immunoglobulin G4-related disease (IgG4-RD) is a systemic fibroinflammatory disease characterized by raised serum IgG4 levels and tumefactive inflammation affecting multiple organ systems, typically involving the pancreas and biliary tree. Though rare, prostatic involvement has been reported in a few cases and is suspected to be an underreported entity. Our patient is a 63-year-old gentleman who has presented with an incidental "PI-RADS 5" (Prostate Imaging Reporting & Data System) prostate lesion and perivascular soft tissue cuffing of the superior rectal vessels on MRI rectum performed for surveillance of rectal neuroendocrine tumor. He had a history of lacrimal gland IgG4-RD. The lentiform prostate lesion subtly indents the prostate capsule, reminiscent of a periprostatic rather than an intraprostatic lesion. Perivascular cuffing of superior rectal vessels suggest inflammatory vasculitis of IgG4-RD. Differential diagnosis of periprostatic inflammatory IgG4-RD was considered, subsequently proven on MRI-ultrasound fusion targeted biopsy. Reported radiological findings of prostate IgG4-RD typically show diffuse chronic inflammation of the prostate, with a minority of the reports describing focal involvement, often mimicking focal prostate adenocarcinoma. Focal periprostatic involvement of IgG4-RD is an unusual manifestation which should be considered in patients with IgG4-RD who present with a periprostatic pseudotumor. IgG4-RD of the prostate usually responds well to steroid treatment without the need for surgery.

Persistent acidosis and chronic kidney disease in a patient with type 1 glycogen storage disease.

Type 1 glycogen storage disease (GSDI) is a rare autosomal recessive disorder caused by glucose-6-phosphatase (G6Pase) deficiency. We discuss a case of a 29-year-old gentleman who had GSDI with metabolic complications of hypoglycemia, hypertriglyceridemia, hyperuricemia, and short stature. He also suffered from advanced chronic kidney disease, nephrotic range proteinuria, and hepatic adenomas. He presented with acute pneumonia and refractory metabolic acidosis despite treatment with isotonic bicarbonate infusion, reversal of hypoglycemia, and lactic acidosis. He eventually required kidney replacement therapy. The case report highlights the multiple contributing mechanisms and challenges to managing refractory metabolic acidosis in a patient with GSDI. Important considerations for dialysis initiation, decision for long-term dialysis modality and kidney transplantation for patients with GSDI are also discussed in this case report.

VI-RADS in bladder cancer: Overview, pearls and pitfalls.

Multiparametric magnetic resonance imaging (mpMRI) of the urinary bladder has shown high diagnostic performance in accurate staging of bladder cancer. Vesical Imaging Reporting and Data System (VI-RADS) scoring was developed in 2018 to standardize imaging and reporting of bladder cancer on mpMRI and is an excellent tool in preoperative T-staging of patients with high risk bladder cancer. However, there is no concise guide in the literature for practical use of VI-RADS in everyday clinical reporting. In this review, we describe our experience with mpMRI in pretreatment workup of bladder cancer, illustrate the imaging characteristics of VI-RADS categories 1 to 5 using case review, and discuss practical pearls and pitfalls in the use of mpMRI and VI-RADS in the hope of providing an accessible reference for radiologists in daily reporting.

Reducing the number of systematic biopsy cores in the era of MRI targeted biopsy-implications on clinically-significant prostate cancer detection and relevance to focal therapy planning.

BACKGROUND: The optimal number of systematic biopsy cores in the era of multi-parametric MRI targeted biopsy remains unclear, especially on its impact of focal therapy planning. Our objective is to investigate the impact of reducing the number of systematic cores on prostate cancer detection in the era of MRI-US fusion targeted biopsy and as well as its relevance in template planning for focal therapy. MATERIALS AND METHODS: A retrospective analysis of 398 consecutive men who underwent both systematic saturation (~24 cores) and MRI-US fusion targeted biopsy was performed. Four reduced-core systematic biopsy strategies (two-thirds, half, one-third and one-quarter systematic cores) were modelled and the detection rates of clinically-significant prostate cancer (csPCa defined as grade group ≥2) were compared to that of a full systematic biopsy using McNemar's test. Focal therapy treatment plans were made based on positive cores on combined (targeted and systematic) biopsy and the various reduced-cores strategies to compare the proportion who had a change in treatment plan. RESULTS: csPCa was detected in 42% (168/398) of this patient cohort. Non-targeted systematic saturation biopsy had a 21% (83/398) csPCa detection rate. Our four strategies reduced the mean number of non-targeted systematic cores from 21.8 to 14.5, 10.9, 7.3 and 5.4 cores and their csPCa detection rates were significantly decreased to 16%, 13%, 9% and 8% respectively (all p < 0.05). Compared to the reduced-core strategies, a full systematic saturation biopsy resulted in change to the focal therapy treatment plan in 12% (2/3 cores), 19% (1/2 cores), 24% (1/3 cores) and 29% (1/4 cores) of the time (p = 0.0434). CONCLUSIONS: Reducing the number of systematic biopsies when performing an MRI-targeted biopsy leads to reduced detection of csPCa and alter the treatment plans for focal therapy, possibly limiting its oncological efficacy.

Use of the Descending Branch of the Lateral Circumflex Femoral Artery as an Arterial Graft in Living Donor Liver Transplant.

BACKGROUND: Reconstructing the hepatic artery in living donor liver transplantation presents the challenges of a short and small donor vessel stump, which is compounded by poor surgical access for microsurgical anastomosis. Arterial interpositional grafts (eg, the radial artery) have been used to overcome these problems. The purpose of this presentation is to describe the use of the descending branch of the lateral circumflex femoral artery (DLCFA) as an alternative when the patient has had an abnormal Allen's test precluding the use of the radial artery or if a Y-graft is needed. METHODS: The DLCFA resides in the septum between the rectus femoris and vastus lateralis muscles. A linear incision made over the proximal third of this septum exposed the avascular plane in which the vessel resides. A graft exceeding 10 cm could be harvested with diameters ranging between ≤2 and 7 mm. There were several muscular branches emanating from the profunda femoris artery system that could be dissected to the required length for a Y-shaped graft. Three cases of living donor liver transplantation using the DLCFA (straight and Y grafts) are described. RESULTS: After DLCFA interpositional grafting, all patients had normal resistive indices on duplex ultrasonography of the intrahepatic arterial system. Follow-up of the 3 patients was between 2 and 6 months. There was no donor site morbidity. CONCLUSIONS: The DLCFA graft was a useful arterial graft for hepatic artery reconstruction. It was easily harvested with minimal donor site morbidity.

Periprostatic schwannoma mimicking metastatic lymphadenopathy in a case of multifocal prostate adenocarcinoma.

Schwannomas of the prostate are a rare entity and usually diagnosed incidentally following surgical management of presumed benign prostate hyperplasia or prostate adenocarcinoma. We present a case of sporadic periprostatic schwannoma diagnosed in conjunction with multifocal prostate adenocarcinoma on pre-operative multiparametric magnetic resonance imaging.