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.

Reducing False-Positives Due to Urinary Stagnation in the Prostatic Urethra on 18F-DCFPyL PSMA PET/CT With MRI

PURPOSE

Prostate-specific membrane antigen (PSMA)-targeting PET radiotracers reveal physiologic uptake in the urinary system, potentially misrepresenting activity in the prostatic urethra as an intraprostatic lesion. This study examined the correlation between midline 18F-DCFPyL activity in the prostate and hyperintensity on T2-weighted (T2W) MRI as an indication of retained urine in the prostatic urethra.

PATIENTS AND METHODS

Eighty-five patients who underwent both 18F-DCFPyL PSMA PET/CT and prostate MRI between July 2017 and September 2023 were retrospectively analyzed for midline radiotracer activity and retained urine on postvoid T2W MRIs. Fisher's exact tests and unpaired t tests were used to compare residual urine presence and prostatic urethra measurements between patients with and without midline radiotracer activity. The influence of anatomical factors including prostate volume and urethral curvature on urinary stagnation was also explored.

RESULTS

Midline activity on PSMA PET imaging was seen in 14 patients included in the case group, whereas the remaining 71 with no midline activity constituted the control group. A total of 71.4% (10/14) and 29.6% (21/71) of patients in the case and control groups had urethral hyperintensity on T2W MRI, respectively (P < 0.01). Patients in the case group had significantly larger mean urethral dimensions, larger prostate volumes, and higher incidence of severe urethral curvature compared with the controls.

CONCLUSIONS

Stagnated urine within the prostatic urethra is a potential confounding factor on PSMA PET scans. Integrating PET imaging with T2W MRI can mitigate false-positive calls, especially as PSMA PET/CT continues to gain traction in diagnosing localized prostate cancer.

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.

Evaluating Diagnostic Accuracy and Inter-reader Agreement of the Prostate Imaging After Focal Ablation Scoring System

Background and objective

Focal therapy (FT) is increasingly recognized as a promising approach for managing localized prostate cancer (PCa), notably reducing treatment-related morbidities. However, post-treatment anatomical changes present significant challenges for surveillance using current imaging techniques. This study aimed to evaluate the inter-reader agreement and efficacy of the Prostate Imaging after Focal Ablation (PI-FAB) scoring system in detecting clinically significant prostate cancer (csPCa) on post-FT multiparametric magnetic resonance imaging (mpMRI).

Methods

A retrospective cohort study was conducted involving patients who underwent primary FT for localized csPCa between 2013 and 2023, followed by post-FT mpMRI and a prostate biopsy. Two expert genitourinary radiologists retrospectively evaluated post-FT mpMRI using PI-FAB. The key measures included inter-reader agreement of PI-FAB scores, assessed by quadratic weighted Cohen's kappa (κ), and the system's efficacy in predicting in-field recurrence of csPCa, with a PI-FAB score cutoff of 3. Additional diagnostic metrics including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy were also evaluated.

Key findings and limitations

Scans from 38 patients were analyzed, revealing a moderate level of agreement in PI-FAB scoring (κ = 0.56). Both radiologists achieved sensitivity of 93% in detecting csPCa, although specificity, PPVs, NPVs, and accuracy varied.

Conclusions and clinical implications

The PI-FAB scoring system exhibited high sensitivity with moderate inter-reader agreement in detecting in-field recurrence of csPCa. Despite promising results, its low specificity and PPV necessitate further refinement. These findings underscore the need for larger studies to validate the clinical utility of PI-FAB, potentially aiding in standardizing post-treatment surveillance.

Patient summary

Focal therapy has emerged as a promising approach for managing localized prostate cancer, but limitations in current imaging techniques present significant challenges for post-treatment surveillance. The Prostate Imaging after Focal Ablation (PI-FAB) scoring system showed high sensitivity for detecting in-field recurrence of clinically significant prostate cancer. However, its low specificity and positive predictive value necessitate further refinement. Larger, more comprehensive studies are needed to fully validate its clinical utility.

Predicting 18F-DCFPyL-PET/CT Scan Positivity in Prostate Cancer Patients with Biochemical Recurrence

RATIONALE AND OBJECTIVES

To analyze variables that can predict the positivity of 18F-DCFPyL- positron emission tomography/computed tomography (PET/CT) and extent of disease in patients with biochemically recurrent (BCR) prostate cancer after primary local therapy with either radical prostatectomy or radiation therapy.

MATERIALS AND METHODS

This is a retrospective analysis of a prospective single institutional review board-approved study. We included 199 patients with biochemical recurrence and negative conventional imaging after primary local therapies (radical prostatectomy n = 127, radiation therapy n = 72). All patients underwent 18F-DCFPyL-PET/CT. Univariate and multivariate logistic regression analyses were used to determine predictors of a positive scan for both cohort of patients. Regression-based coefficients were used to develop nomograms predicting scan positivity and extra-pelvic disease. Decision curve analysis (DCA) was implemented to quantify nomogram's clinical benefit.

RESULTS

Of the 127 (63%) post-radical prostatectomy patients, 91 patients had positive scans - 61 of those with intrapelvic lesions and 30 with extra-pelvic lesions (i.e., retroperitoneal or distant nodes and/or bone/organ lesions). Of the 72 post-radiation therapy patients, 65 patients had positive scans - 39 of them had intrapelvic lesions and 26 extra-pelvic lesions. In the radical prostatectomy cohort, multivariate regression analysis revealed original International Society of Urological Pathology category, prostate-specific antigen (PSA), prostate-specific antigen doubling time (PSAdt), and time from BCR (mo) to scan were predictors for scan positivity and presence of extra-pelvic disease, with an area under the curve of 80% and 78%, respectively. Positive versus negative tumor margin after radical prostatectomy was not related to scan positivity or to the presence of positive extra-pelvic foci. In the radiation therapy cohort, multivariate regression analysis revealed that PSA, PSAdt, and time to BCR (mo) were predictors of extra-pelvic disease, with area under the curve of 82%. Because only seven patients in the radiation therapy cohort had negative scans, a prediction model for scan positivity could not be analyzed and only the presence of extra-pelvic disease was evaluated.

CONCLUSION

PSA and PSAdt are consistently significant predictors of 18F-DCFPyL PET/CT positivity and extra-pelvic disease in BCR prostate cancer patients. Stratifying the patient population into primary local treatment group enables the use of other variables as predictors, such as time since BCR. This nomogram may guide selection of the most suitable candidates for 18F-DCFPyL-PET/CT imaging.

PRECISE Version 2: Updated Recommendations for Reporting Prostate Magnetic Resonance Imaging in Patients on Active Surveillance for Prostate Cancer

BACKGROUND AND OBJECTIVE

The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) recommendations standardise the reporting of prostate magnetic resonance imaging (MRI) in patients on active surveillance (AS) for prostate cancer. An international consensus group recently updated these recommendations and identified the areas of uncertainty.

METHODS

A panel of 38 experts used the formal RAND/UCLA Appropriateness Method consensus methodology. Panellists scored 193 statements using a 1-9 agreement scale, where 9 means full agreement. A summary of agreement, uncertainty, or disagreement (derived from the group median score) and consensus (determined using the Interpercentile Range Adjusted for Symmetry method) was calculated for each statement and presented for discussion before individual rescoring.

KEY FINDINGS AND LIMITATIONS

Participants agreed that MRI scans must meet a minimum image quality standard (median 9) or be given a score of 'X' for insufficient quality. The current scan should be compared with both baseline and previous scans (median 9), with the PRECISE score being the maximum from any lesion (median 8). PRECISE 3 (stable MRI) was subdivided into 3-V (visible) and 3-NonV (nonvisible) disease (median 9). Prostate Imaging Reporting and Data System/Likert ≥3 lesions should be measured on T2-weighted imaging, using other sequences to aid in the identification (median 8), and whenever possible, reported pictorially (diagrams, screenshots, or contours; median 9). There was no consensus on how to measure tumour size. More research is needed to determine a significant size increase (median 9). PRECISE 5 was clarified as progression to stage ≥T3a (median 9).

CONCLUSIONS AND CLINICAL IMPLICATIONS

The updated PRECISE recommendations reflect expert consensus opinion on minimal standards and reporting criteria for prostate MRI in AS.

PATIENT SUMMARY

The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) recommendations are used in clinical practice and research to guide the interpretation and reporting of magnetic resonance imaging for patients on active surveillance for prostate cancer. An international panel has updated these recommendations, clarified the areas of uncertainty, and highlighted the areas for further research.

Automated prostate gland segmentation in challenging clinical cases: comparison of three artificial intelligence methods

OBJECTIVE

Automated methods for prostate segmentation on MRI are typically developed under ideal scanning and anatomical conditions. This study evaluates three different prostate segmentation AI algorithms in a challenging population of patients with prior treatments, variable anatomic characteristics, complex clinical history, or atypical MRI acquisition parameters.

MATERIALS AND METHODS

A single institution retrospective database was queried for the following conditions at prostate MRI: prior prostate-specific oncologic treatment, transurethral resection of the prostate (TURP), abdominal perineal resection (APR), hip prosthesis (HP), diversity of prostate volumes (large ≥ 150 cc, small ≤ 25 cc), whole gland tumor burden, magnet strength, noted poor quality, and various scanners (outside/vendors). Final inclusion criteria required availability of axial T2-weighted (T2W) sequence and corresponding prostate organ segmentation from an expert radiologist. Three previously developed algorithms were evaluated: (1) deep learning (DL)-based model, (2) commercially available shape-based model, and (3) federated DL-based model. Dice Similarity Coefficient (DSC) was calculated compared to expert. DSC by model and scan factors were evaluated with Wilcox signed-rank test and linear mixed effects (LMER) model.

RESULTS

683 scans (651 patients) met inclusion criteria (mean prostate volume 60.1 cc [9.05-329 cc]). Overall DSC scores for models 1, 2, and 3 were 0.916 (0.707-0.971), 0.873 (0-0.997), and 0.894 (0.025-0.961), respectively, with DL-based models demonstrating significantly higher performance (p < 0.01). In sub-group analysis by factors, Model 1 outperformed Model 2 (all p < 0.05) and Model 3 (all p < 0.001). Performance of all models was negatively impacted by prostate volume and poor signal quality (p < 0.01). Shape-based factors influenced DL models (p < 0.001) while signal factors influenced all (p < 0.001).

CONCLUSION

Factors affecting anatomical and signal conditions of the prostate gland can adversely impact both DL and non-deep learning-based segmentation models.

Localized high-risk prostate cancer harbors an androgen receptor low subpopulation susceptible to HER2 inhibition

Patients diagnosed with localized high-risk prostate cancer have higher rates of recurrence, and the introduction of neoadjuvant intensive hormonal therapies seeks to treat occult micrometastatic disease by their addition to definitive treatment. Sufficient profiling of baseline disease has remained a challenge in enabling the in-depth assessment of phenotypes associated with exceptional vs. poor pathologic responses after treatment. In this study, we report comprehensive and integrative gene expression profiling of 37 locally advanced prostate tumors prior to six months of androgen deprivation therapy (ADT) plus the androgen receptor (AR) inhibitor enzalutamide prior to radical prostatectomy. A robust transcriptional program associated with HER2 activity was positively associated with poor outcome and opposed AR activity, even after adjusting for common genomic alterations in prostate cancer including PTEN loss and expression of the TMPRSS2:ERG fusion. Patients experiencing exceptional pathologic responses demonstrated lower levels of HER2 and phospho-HER2 by immunohistochemistry of biopsy tissues. The inverse correlation of AR and HER2 activity was found to be a universal feature of all aggressive prostate tumors, validated by transcriptional profiling an external cohort of 121 patients and immunostaining of tumors from 84 additional patients. Importantly, the AR activity-low, HER2 activity-high cells that resist ADT are a pre-existing subset of cells that can be targeted by HER2 inhibition alone or in combination with enzalutamide. In summary, we show that prostate tumors adopt an AR activity-low prior to antiandrogen exposure that can be exploited by treatment with HER2 inhibitors.

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.

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.

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.

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.

Is prostatic adenocarcinoma with cribriform architecture more difficult to detect on prostate MRI?

BACKGROUND

Cribriform (CBFM) pattern on prostate biopsy has been implicated as a predictor for high-risk features, potentially leading to adverse outcomes after definitive treatment. This study aims to investigate whether the CBFM pattern containing prostate cancers (PCa) were associated with false negative magnetic resonance imaging (MRI) and determine the association between MRI and histopathological disease burden.

METHODS

Patients who underwent multiparametric magnetic resonance imaging (mpMRI), combined 12-core transrectal ultrasound (TRUS) guided systematic (SB) and MRI/US fusion-guided biopsy were retrospectively queried for the presence of CBFM pattern at biopsy. Biopsy cores and lesions were categorized as follows: C0 = benign, C1 = PCa with no CBFM pattern, C2 = PCa with CBFM pattern. Correlation between cancer core length (CCL) and measured MRI lesion dimension were assessed using a modified Pearson correlation test for clustered data. Differences between the biopsy core groups were assessed with the Wilcoxon-signed rank test with clustering.

RESULTS

Between 2015 and 2022, a total of 131 consecutive patients with CBFM pattern on prostate biopsy and pre-biopsy mpMRI were included. Clinical feature analysis included 1572 systematic biopsy cores (1149 C0, 272 C1, 151 C2) and 736 MRI-targeted biopsy cores (253 C0, 272 C1, 211 C2). Of the 131 patients with confirmed CBFM pathology, targeted biopsy (TBx) alone identified CBFM in 76.3% (100/131) of patients and detected PCa in 97.7% (128/131) patients. SBx biopsy alone detected CBFM in 61.1% (80/131) of patients and PCa in 90.8% (119/131) patients. TBx and SBx had equivalent detection in patients with smaller prostates (p = 0.045). For both PCa lesion groups there was a positive and significant correlation between maximum MRI lesion dimension and CCL (C1 lesions: p < 0.01, C2 lesions: p < 0.001). There was a significant difference in CCL between C1 and C2 lesions for T2 scores of 3 and 5 (p ≤ 0.01, p ≤ 0.01, respectively) and PI-RADS 5 lesions (p ≤ 0.01), with C2 lesions having larger CCL, despite no significant difference in MRI lesion dimension.

CONCLUSIONS

The extent of disease for CBFM-containing tumors is difficult to capture on mpMRI. When comparing MRI lesions of similar dimensions and PIRADS scores, CBFM-containing tumors appear to have larger cancer yield on biopsy. Proper staging and planning of therapeutic interventions is reliant on accurate mpMRI estimation. Special considerations should be taken for patients with CBFM pattern on prostate biopsy.

A Phase 1 Trial of Focal Salvage Stereotactic Body Radiation Therapy for Radiorecurrent Prostate Cancer

PURPOSE

NCT03253744 was a phase 1 trial to identify the maximum tolerated dose (MTD) of image-guided, focal, salvage stereotactic body radiation therapy (SBRT) for patients with locally radiorecurrent prostate cancer. Additional objectives included biochemical control and imaging response.

METHODS AND MATERIALS

The trial design included 3 dose levels (DLs): 40 Gy (DL1), 42.5 Gy (DL2), and 45 Gy (DL3) in 5 fractions delivered ≥48 hours apart. The prescription dose was delivered to the magnetic resonance- and prostate-specific membrane antigen imaging-defined tumor volume. Dose escalation followed a 3+3 design with a 3-patient expansion at the MTD. Toxicities were scored until 2 years after completion of SBRT using Common Terminology Criteria for Adverse Events, version 5.0, criteria. Escalation was halted if 2 dose-limiting toxicities occurred, defined as any persistent (>4 days) grade 3 toxicity occurring within the first 3 weeks after SBRT and any grade 3 genitourinary (GU) or grade 4 gastrointestinal (GI) toxicity thereafter.

RESULTS

Between August 2018 and May 2022, 8 patients underwent salvage focal SBRT, with a median follow-up of 35 months. No dose-limiting toxic effects were observed on DL1. Two patients were enrolled in DL2 and experienced grade 3 GU toxicities, prompting de-escalation and expansion (n = 6) at the MTD (DL1). The most common toxicities observed were grade ≥2 GU toxicities, with only a single grade 2 GI toxicity and no grade ≥3 GI toxicities. One patient experienced biochemical failure (prostate-specific antigen nadir + 2.0) at 33 months.

CONCLUSIONS

The MTD for focal salvage SBRT for isolated intraprostatic radiorecurrence was 40 Gy in 5 fractions, producing a 100% 24-month biochemical progression free survival, with 1 poststudy failure at 33 months. The most frequent clinically significant toxicity was late grade ≥2 GU toxicity.

A Novel Magnetic Resonance Imaging/Ultrasound Fusion Prostate Biopsy Technique Using Transperineal Ultrasound: An Initial Experience

OBJECTIVE

To report an initial experience with a novel, "fully" transperineal (TP) prostate fusion biopsy using an unconstrained ultrasound transducer placed on the perineal skin to guide biopsy needles inserted via a TP approach.

METHODS

Conventional TP prostate biopsies for detection of prostate cancer have been performed with transrectal ultrasound, requiring specialized hardware, imposing limitations on needle trajectory, and contributing to patient discomfort. Seventy-six patients with known or suspected prostate cancer underwent 78 TP biopsy sessions in an academic center between June 2018 and April 2022 and were included in this study. These patients underwent TP prostate fusion biopsy using a grid or freehand device with transrectal ultrasound as well as TP prostate fusion biopsy using TP ultrasound in the same session. Per-session and per-lesion cancer detection rates were compared for conventional and fully TP biopsies using Fisher exact and McNemar's tests.

RESULTS

After a refinement period in 30 patients, 92 MRI-visible prostate lesions were sampled in 46 subsequent patients, along with repeat biopsies in 2 of the 30 patients from the refinement period. Grade group ≥2 cancer was diagnosed in 24/92 lesions (26%) on conventional TP biopsy (17 lesions with grid, 7 with freehand device), and in 25/92 lesions (27%) on fully TP biopsy (P = 1.00), with a 73/92 (79%) rate of agreement for grade group ≥2 cancer between the two methods.

CONCLUSION

Fully TP biopsy is feasible and may detect prostate cancer with detection rates comparable to conventional TP biopsy.

Evaluation of a Deep Learning-based Algorithm for Post-Radiotherapy Prostate Cancer Local Recurrence Detection Using Biparametric MRI

OBJECTIVE

To evaluate a biparametric MRI (bpMRI)-based artificial intelligence (AI) model for the detection of local prostate cancer (PCa) recurrence in patients with radiotherapy history.

MATERIALS AND METHODS

This study included post-radiotherapy patients undergoing multiparametric MRI and subsequent MRI/US fusion-guided and/or systematic biopsy. Histopathology results were used as ground truth. The recurrent cancer detection sensitivity of a bpMRI-based AI model, which was developed on a large dataset to primarily identify lesions in treatment-naïve patients, was compared to a prospective radiologist assessment using the Wald test. Subanalysis was conducted on patients stratified by the treatment modality (external beam radiation treatment [EBRT] and brachytherapy) and the prostate volume quartiles.

RESULTS

Of the 62 patients included (median age = 70 years; median PSA = 3.51 ng/ml; median prostate volume = 27.55 ml), 56 recurrent PCa foci were identified within 46 patients. The AI model detected 40 lesions in 35 patients. The AI model performance was lower than the prospective radiology interpretation (Rad) on a patient-(AI: 76.1% vs. Rad: 91.3%, p = 0.02) and lesion-level (AI: 71.4% vs. Rad: 87.5%, p = 0.01). The mean number of false positives per patient was 0.35 (range: 0-2). The AI model performance was higher in EBRT group both on patient-level (EBRT: 81.5% [22/27] vs. brachytherapy: 68.4% [13/19]) and lesion-level (EBRT: 79.4% [27/34] vs. brachytherapy: 59.1% [13/22]). In patients with gland volumes >34 ml (n = 25), detection sensitivities were 100% (11/11) and 94.1% (16/17) on patient- and lesion-level, respectively.

CONCLUSION

The reported bpMRI-based AI model detected the majority of locally recurrent prostate cancer after radiotherapy. Further testing including external validation of this model is warranted prior to clinical implementation.

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.

A Phase I Trial of Focal Salvage Stereotactic Body Radiation Therapy for Radiorecurrent Prostate Cancer

PURPOSE/OBJECTIVE(S)

Locally recurrent prostate cancer after radiotherapy (RT) is an increasingly recognized entity with no standard management. NCT03253744 was a phase I trial with a primary objective of identifying the maximally tolerated dose (MTD) of a course of image-guided, focal, salvage stereotactic body radiotherapy (SBRT) for patients with local recurrence after prior definitive RT. Additional objectives included biochemical control and imaging response on mpMRI and 18F-DCFPyL (PSMA) PET/CT.

MATERIALS/METHODS

SBRT was prescribed to three dose levels (DLs): 40Gy (DL1), 42.5Gy (DL2), and 45Gy (DL3) in 5 fractions. The prescription dose was delivered to a PTV defined by mpMRI and PSMA imaging and biopsy confirmed tumor volume. Dose escalation followed a 3+3 design with a 3-patient expansion at the MTD. Toxicities above baseline were scored using CTCAE v5.0 criteria for two years after completion of SBRT. Escalation was halted if 2 dose limiting toxicities (DLTs) were observed. DLTs were defined as any persistent (>4 days) grade 3 toxicity occurring within the first 3 weeks after SBRT, and any grade 3 GU or grade 4 GI toxicity thereafter. Imaging response was compared between baseline and 6-months by the Wilcoxon signed rank test.

RESULTS

Between 08/2018 and 05/2022, 8 patients underwent salvage SBRT to 11 intraprostatic lesions with a median follow-up of 27 months. No DLTs were observed on DL1. Two patients were enrolled on DL2 and both experienced grade 3 GU toxicities, prompting de-escalation and expansion (n = 6) on DL1, the MTD. The most common toxicities were grade 2 GU toxicities: acute urinary urgency/frequency, acute weak urinary stream, and noninfective cystitis. One patient at DL1 had a self-limited episode of grade 2 GI toxicity (proctitis). No grade 3 GI toxicities were observed. All but two patients achieved an undetectable PSA nadir. Only one of these experienced biochemical failure (nadir + 2.0) at 33 months with suspicion of distant metastatic failure on restaging PET/CT. Imaging response was demonstrated by MRI in all lesions with heterogeneity in volumetric response (6% to 100%). A significant (p<0.01) response on PSMA PET/CT was observed for all measured parameters (SUVMax, SUVMean, GTVPSMA, Total Lesion PSMA [SUVMean × GTVPSMA]). Of the 11 lesions, 1 (9%) demonstrated a complete response (CR) by MRI and 9 (82%) by PSMA PET/CT. A single lesion increased in volume by 0.06 cc (16%) at 6-month PSMA PET/CT compared to baseline in the only patient who did not achieve an undetectable PSA nadir and did not have imaging suggestive of distant failure.

CONCLUSION

On this phase I dose escalation study of salvage SBRT for isolated intraprostatic local failure after definitive RT, the MTD was 40Gy in 5 fractions. producing a 100% 24-month bPFS, with one late failure at 33 months occurring after the 24-month study period. The most frequent clinically significant toxicity was late grade 2 GU toxicity. Imaging response was demonstrated in all lesions on MRI and PSMA PET/CT with exception of a single lesion.

Radiologic reporting of MRI-proven thoracolumbar epidural metastases on body CT: 12-Year single-institution experience

RATIONALE AND OBJECTIVES

Metastatic epidural masses are an important radiological finding. The purpose of this study is to determine factors associated with non-reporting of thoracolumbar epidural metastases on body CT.

MATERIALS AND METHODS

In a study population of 166 patients from a single institution over a 12-year period, 293 body CT examinations were identified which were performed within 30 days before or after a spine MRI diagnosis of epidural metastasis. Associations were sought between patient diagnosis, CT examination characteristics, reporting radiologist (n = 17), and lesion characteristics with respect to whether an epidural metastasis was reported on CT.

RESULTS

In retrospective consensus review comprised of 3 radiologists, epidural metastases reported on spine MRI were clearly visible in 80.5% (236/293) of body CT examinations, however 65.3% (154/236) of the body CT reports omitted reporting their presence, even in cases where there was a preceding MRI diagnosis within 30 days (65.4%, 74/113). The identity of the reporting radiologist was statistically significantly associated with the accurate diagnostic reporting of epidural metastasis on body CT (p = 0.04). The only lesion features which were statistically significantly associated with CT reporting were lesion volume (p = 0.03) on noncontrast CT, and lesion volume (p = 0.006) and percentage of spinal canal stenosis (p = 0.001) on intravenous contrast-enhanced CT. The presence or absence of intravenous contrast was not significantly associated with CT reporting (p = 1.0).

CONCLUSION

Using spine MRI as the reference standard for the presence of epidural tumor, the majority of body CT reports omit describing thoracolumbar epidural metastases which are clearly visible in retrospect.

Ultrasound Frame-to-Volume Registration via Deep Learning for Interventional Guidance

Fusing intraoperative 2-D ultrasound (US) frames with preoperative 3-D magnetic resonance (MR) images for guiding interventions has become the clinical gold standard in image-guided prostate cancer biopsy. However, developing an automatic image registration system for this application is challenging because of the modality gap between US/MR and the dimensionality gap between 2-D/3-D data. To overcome these challenges, we propose a novel US frame-to-volume registration (FVReg) pipeline to bridge the dimensionality gap between 2-D US frames and 3-D US volume. The developed pipeline is implemented using deep neural networks, which are fully automatic without requiring external tracking devices. The framework consists of three major components, including one) a frame-to-frame registration network (Frame2Frame) that estimates the current frame's 3-D spatial position based on previous video context, two) a frame-to-slice correction network (Frame2Slice) adjusting the estimated frame position using the 3-D US volumetric information, and three) a similarity filtering (SF) mechanism selecting the frame with the highest image similarity with the query frame. We validated our method on a clinical dataset with 618 subjects and tested its potential on real-time 2-D-US to 3-D-MR fusion navigation tasks. The proposed FVReg achieved an average target navigation error of 1.93 mm at 5-14 fps. Our source code is publicly available at https://github.com/DIAL-RPI/Frame-to-Volume-Registration.

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

PURPOSE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Early Detection of Prostate Cancer: AUA/SUO Guideline Part I: Prostate Cancer Screening

PURPOSE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

Prostate-specific antigen (PSA)-based prostate cancer screening in combination with shared decision-making (SDM) is recommended. Current data regarding risk from population-based cohorts provide a basis for longer screening intervals and tailored screening, and the use of available online risk calculators is encouraged.

Prospective Evaluation of PI-RADS Version 2.1 for Prostate Cancer Detection and Investigation of Multiparametric MRI-derived Markers

Background Data regarding the prospective performance of Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 alone and in combination with quantitative MRI features for prostate cancer detection is limited. Purpose To assess lesion-based clinically significant prostate cancer (csPCa) rates in different PI-RADS version 2.1 categories and to identify MRI features that could improve csPCa detection. Materials and Methods This single-center prospective study included men with suspected or known prostate cancer who underwent multiparametric MRI and MRI/US-guided biopsy from April 2019 to December 2021. MRI scans were prospectively evaluated using PI-RADS version 2.1. Atypical transition zone (TZ) nodules were upgraded to category 3 if marked diffusion restriction was present. Lesions with an International Society of Urological Pathology (ISUP) grade of 2 or higher (range, 1-5) were considered csPCa. MRI features, including three-dimensional diameter, relative lesion volume (lesion volume divided by prostate volume), sphericity, and surface to volume ratio (SVR), were obtained from lesion contours delineated by the radiologist. Univariable and multivariable analyses were conducted at the lesion and participant levels to determine features associated with csPCa. Results In total, 454 men (median age, 67 years [IQR, 62-73 years]) with 838 lesions were included. The csPCa rates for lesions categorized as PI-RADS 1 (n = 3), 2 (n = 170), 3 (n = 197), 4 (n = 319), and 5 (n = 149) were 0%, 9%, 14%, 37%, and 77%, respectively. csPCa rates of PI-RADS 4 lesions were lower than PI-RADS 5 lesions (P < .001) but higher than PI-RADS 3 lesions (P < .001). Upgraded PI-RADS 3 TZ lesions were less likely to harbor csPCa compared with their nonupgraded counterparts (4% [one of 26] vs 20% [20 of 99], P = .02). Predictors of csPCa included relative lesion volume (odds ratio [OR], 1.6; P < .001), SVR (OR, 6.2; P = .02), and extraprostatic extension (EPE) scores of 2 (OR, 9.3; P < .001) and 3 (OR, 4.1; P = .02). Conclusion The rates of csPCa differed between consecutive PI-RADS categories of 3 and higher. MRI features, including lesion volume, shape, and EPE scores of 2 and 3, predicted csPCa. Upgrading of PI-RADS category 3 TZ lesions may result in unnecessary biopsies. ClinicalTrials.gov registration no. NCT03354416 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Goh in this issue.

Radiogenomic profiling of prostate tumors prior to external beam radiotherapy converges on a transcriptomic signature of TGF-β activity driving tumor recurrence

Background

Patients with localized prostate cancer have historically been assigned to clinical risk groups based on local disease extent, serum prostate specific antigen (PSA), and tumor grade. Clinical risk grouping is used to determine the intensity of treatment with external beam radiotherapy (EBRT) and androgen deprivation therapy (ADT), yet a substantial proportion of patients with intermediate and high risk localized prostate cancer will develop biochemical recurrence (BCR) and require salvage therapy. Prospective identification of patients destined to experience BCR would allow treatment intensification or selection of alternative therapeutic strategies.

Methods

Twenty-nine individuals with intermediate or high risk prostate cancer were prospectively recruited to a clinical trial designed to profile the molecular and imaging features of prostate cancer in patients undergoing EBRT and ADT. Whole transcriptome cDNA microarray and whole exome sequencing were performed on pretreatment targeted biopsy of prostate tumors (n=60). All patients underwent pretreatment and 6-month post EBRT multiparametric MRI (mpMRI), and were followed with serial PSA to assess presence or absence of BCR. Genes differentially expressed in the tumor of patients with and without BCR were investigated using pathways analysis tools and were similarly explored in alternative datasets. Differential gene expression and predicted pathway activation were evaluated in relation to tumor response on mpMRI and tumor genomic profile. A novel TGF-β gene signature was developed in the discovery dataset and applied to a validation dataset.

Findings

Baseline MRI lesion volume and PTEN/TP53 status in prostate tumor biopsies correlated with the activation state of TGF-β signaling measured using pathway analysis. All three measures correlated with the risk of BCR after definitive RT. A prostate cancer-specific TGF-β signature discriminated between patients that experienced BCR vs. those that did not. The signature retained prognostic utility in an independent cohort.

Interpretation

TGF-β activity is a dominant feature of intermediate-to-unfavorable risk prostate tumors prone to biochemical failure after EBRT with ADT. TGF-β activity may serve as a prognostic biomarker independent of existing risk factors and clinical decision-making criteria.

Funding

This research was supported by the Prostate Cancer Foundation, the Department of Defense Congressionally Directed Medical Research Program, National Cancer Institute, and the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research.

HoloLens augmented reality system for transperineal free-hand prostate procedures

Purpose

An augmented reality (AR) system was developed to facilitate free-hand real-time needle guidance for transperineal prostate (TP) procedures and to overcome the limitations of a traditional guidance grid.

Approach

The HoloLens AR system enables the superimposition of annotated anatomy derived from preprocedural volumetric images onto a patient and addresses the most challenging part of free-hand TP procedures by providing real-time needle tip localization and needle depth visualization during insertion. The AR system accuracy, or the image overlay accuracy ( n = 56 ), and needle targeting accuracy ( n = 24 ) were evaluated within a 3D-printed phantom. Three operators each used a planned-path guidance method ( n = 4 ) and free-hand guidance ( n = 4 ) to guide needles into targets in a gel phantom. Placement error was recorded. The feasibility of the system was further evaluated by delivering soft tissue markers into tumors of an anthropomorphic pelvic phantom via the perineum.

Results

The image overlay error was 1.29 ± 0.57    mm , and needle targeting error was 2.13 ± 0.52    mm . The planned-path guidance placements showed similar error compared to the free-hand guidance ( 4.14 ± 1.08    mm versus 4.20 ± 1.08    mm , p = 0.90 ). The markers were successfully implanted either into or in close proximity to the target lesion.

Conclusions

The HoloLens AR system can provide accurate needle guidance for TP interventions. AR support for free-hand lesion targeting is feasible and may provide more flexibility than grid-based methods, due to the real-time 3D and immersive experience during free-hand TP procedures.

Predicting Outcomes of Indeterminate Bone Lesions on 18F-DCFPyL PSMA PET/CT Scans in the Setting of High-Risk Primary or Recurrent Prostate Cancer

Indeterminate bone lesions (IBLs) on prostate-specific membrane antigen (PSMA) PET/CT are common. This study aimed to define variables that predict whether such lesions are likely malignant or benign using features on PSMA PET/CT. Methods: ¹⁸F-DCFPyL PET/CT imaging was performed on 243 consecutive patients with high-risk primary or biochemically recurrent prostate cancer. IBLs identified on PSMA PET/CT could not definitively be interpreted as benign or malignant. Medical records of patients with IBLs were reviewed to determine the ultimate status of each lesion. IBLs were deemed malignant or benign on the basis of evidence of progression or stability at follow-up, respectively, or by biopsy results; IBLs were deemed equivocal when insufficient or unclear evidence existed. Post hoc patient, lesion, and scan variables accounting for clustered data were evaluated using Wilcoxon rank-sum and χ² tests to determine features that favored benign or malignant interpretation. Results: Overall, 98 IBLs within 267 bone lesions (36.7%) were identified in 48 of 243 patients (19.8%). Thirty-seven of 98 IBLs were deemed benign, and 42 were deemed malignant, of which 8 had histologic verification; 19 remained equivocal. Location and SUV_max categorical variables were predictive of IBL interpretation (P = 0.0201 and P = 0.0230, respectively). For IBLs with new interpretations, 34 of 37 (91.9%) considered benign showed an SUV_max of less than 5 or exhibited focal uptake without coexisting bone metastases; 37 of 42 (88.1%) deemed malignant demonstrated an SUV_max of at least 5 or were present with coexisting bone metastases. Logistic regression predicted IBLs with a high SUV_max (univariable: odds ratio [OR], 9.29 [P = 0.0016]; multivariable: OR, 13.87 [P = 0.0089]) or present with other bone metastases (univariable: OR, 9.87 [P = 0.0112]; multivariable: OR, 11.35 [P = 0.003]) to be malignant. Conclusion: IBLs on PSMA PET/CT are concerning; however, characterizing their location, SUV, and additional scan findings can aid interpretation. IBLs displaying an SUV_max of at least 5 or present with other bone metastases favor malignancy. IBLs without accompanying bone metastases that exhibit an SUV_max of less than 5 and are observed only in atypical locations favor benign processes. These guidelines may assist in the interpretation of IBLs on PSMA PET/CT.

Ipsilateral hemigland prostate biopsy may underestimate cancer burden in patients with unilateral mpMRI-visible lesions

PURPOSE

To evaluate the cancer detection rates of reduced-core biopsy schemes in patients with unilateral mpMRI-visible intraprostatic lesions and to analyze the contribution of systematic biopsy cores in clinically significant prostate cancer (csPCa) detection.

METHODS

212 patients with mpMRI-visible unilateral intraprostatic lesions undergoing MRI/TRUS fusion-guided targeted biopsy (TBx) and systematic biopsy (SBx) were included. Cancer detection rates of TBx + SBx, as determined by highest Gleason Grade Group (GG), were compared to 3 reduced-core biopsy schemes: TBx alone, TBx + ipsilateral systematic biopsy (IBx; MRI-positive hemigland), and TBx + contralateral systematic biopsy (CBx; MRI-negative hemigland). Patient-level and biopsy core-level data were analyzed using descriptive statistics with confidence intervals. Univariable and multivariable logistic regression analysis was conducted to identify predictors of csPCa (≥ GG2) detected in MRI-negative hemiglands at p < 0.05.

RESULTS

Overall, 43.4% (92/212) of patients had csPCa and 66.0% (140/212) of patients had any PCa detected by TBx + SBx. Of patients with csPCa, 81.5% had exclusively ipsilateral involvement (MRI-positive), 7.6% had only contralateral involvement (MRI-negative), and 10.9% had bilateral involvement. The csPCa detection rates of reduced-core biopsy schemes were 35.4% (75/212), 40.1% (85/212), and 39.6% (84/212) for TBx alone, TBx + IBx, and TBx + CBx, respectively, with detection sensitivities of 81.5%, 92.4%, and 91.3% compared to TBx + SBx.

CONCLUSION

Reduced-core prostate biopsy strategies confined to the ipsilateral hemigland underestimate csPCa burden by at least 8% in patients with unilateral mpMRI-visible intraprostatic lesions. The combined TBx + SBx strategy maximizes csPCa detection.

Inherited risk for prostate cancer (PCa): Following the natural history of men with high-risk genetics using multiparametric MRI (mpMRI)

390

Background: PCa has inherited risk factors including high genetic risk variants such as BRCA1/2, HOXB13, and DNA mismatch repair genes. mpMRI has been shown to be effective for detection and staging of localized PCa. This study follows participants (prts), born biologically male, without a diagnosis of PCa with known germline pathogenic or likely pathogenic variants (PV) in BRCA1/2, MLH1, MSH2, MSH6, PMS2, EPCAM, HOXB13, ATM, NBN, TP53, CHEK2, PALB2, RAD51C/D, BRIP1, or FANCA-FANCM (NCT03805919). Methods: Up to 500 eligible prts 30-75 years old (yo) with a documented germline PV will enroll. Prts undergo biennial clinical exam and mpMRI, and annual PSA monitoring and are followed at 12-month intervals to determine PSA, prostate cancer diagnosis, and/or disease/survival status until death. Indication for prostate biopsy includes clinical or imaging findings. Biopsy specimens undergo molecular analyses. Results: To date, 175 prts have been enrolled: 169 (97%) White, 3 Hispanic (2%), 1 African American (1%), 1 Asian (1%), and 1 biethnic (1%). Median age is 47 yo. The most common monoallelic PV are: 48.6% BRCA2, 25.1% BRCA1, 6.3% CHEK2 and 5.7% MSH2. PVs in ATM, PALB2, HOXB13, PMS2, MLH1, MSH6, BRIP1, EPCAM and RAD51D are ≤4%. One subject carries three distinct PVs ( BRCA2, CHEK2, BRIP1). Indication for biopsy was found in 26.3% of prts with 22/46 (47.8%) with a PIRADS 4 lesion, 6/46 (13.0%) PIRADS 3 lesion, 12/46 (26.1%) elevated PSA (median=2.8 ng/mL) or 6/46 (13.0%) due to clinical discretion. Adenocarcinoma was diagnosed on 13/39 (33.3%) biopsies with median age at diagnosis=59 yo. 9/13 (69%) prts had a PSA <3 ng/ml at diagnosis. Nine prts were diagnosed with ISUP Grade Group (GG) 1, 3 with GG2, and 1 with GG3. Eight prts opted for active surveillance (AS), 2 for radiation therapy (RT), and 3 for prostatectomy (RP). Two prts on AS converted to definitive treatment (one RP and one RT) due to progression in GG on the year 1 AS biopsy. Conclusions: mpMRI screening in men with germline PV can be used for diagnosis and monitoring of PCa and facilitates detection below conventional PSA thresholds in a high genetic risk setting. Access to genetic testing and other variables need to be addressed in underrepresented minorities. Correlative studies, including cfDNA and PBMCs, are ongoing. Clinical trial information: NCT03805919 . [Table: see text]

Impact of Prostate Urethral Lift Device on Prostate Magnetic Resonance Image Quality

PURPOSE

Prostatic urethral lift with UroLift is a minimally invasive approach to treat symptomatic benign prostatic hypertrophy. This device causes artifacts on prostate magnetic resonance images. Our aim was to evaluate the impact of artifact on prostate magnetic resonance image quality.

MATERIALS AND METHODS

This was a single-center retrospective review of patients with UroLift who subsequently had prostate magnetic resonance imaging. Two readers graded UroLift artifact on each pulse sequence using a 5-point scale (1-nondiagnostic; 5-no artifact). Prostate Imaging Quality scores were assigned for the whole data set. The volume of gland obscured by artifact was measured. Linear and logistic regression models were used to identify predictors of poor image quality.

RESULTS

Thirty-seven patients were included. Poor image quality occurs more in the transition zone than the peripheral zone (15% vs 3%), at base/mid regions vs the apex (13%, 9%, and 5%, respectively) and on diffusion-weighted images vs T2-weighted and dynamic contrast-enhanced sequences (27%, 0.3%, 0%, respectively; P < .001). Suboptimal image quality (ie, Prostate Imaging Quality score <2) was found in 16%-24% of exams. The percentage of gland obscured by the UroLift artifact was higher on diffusion-weighted images and dynamic contrast-enhanced sequences than T2-weighted (32%, 9%, and 6%, respectively; P < .001).

CONCLUSIONS

UroLift artifact negatively affects prostate magnetic resonance image quality with greater impact in the mid-basal transition zone, obscuring a third of the gland on diffusion-weighted images. Patients considering this procedure should be counseled on the impact of this device on image quality and its potential implications for any image-guided prostate cancer workup.

Development of a 3D CNN-based AI Model for Automated Segmentation of the Prostatic Urethra

RATIONALE AND OBJECTIVE

The combined use of prostate cancer radiotherapy and MRI planning is increasingly being used in the treatment of clinically significant prostate cancers. The radiotherapy dosage quantity is limited by toxicity in organs with de-novo genitourinary toxicity occurrence remaining unperturbed. Estimation of the urethral radiation dose via anatomical contouring may improve our understanding of genitourinary toxicity and its related symptoms. Yet, urethral delineation remains an expert-dependent and time-consuming procedure. In this study, we aim to develop a fully automated segmentation tool for the prostatic urethra.

MATERIALS AND METHODS

This study incorporated 939 patients' T2-weighted MRI scans (train/validation/test/excluded: 657/141/140/1 patients), including in-house and public PROSTATE-x datasets, and their corresponding ground truth urethral contours from an expert genitourinary radiologist. The AI model was developed using MONAI framework and was based on a 3D-UNet. AI model performance was determined by Dice score (volume-based) and the Centerline Distance (CLD) between the prediction and ground truth centers (slice-based). All predictions were compared to ground truth in a systematic failure analysis to elucidate the model's strengths and weaknesses. The Wilcoxon-rank sum test was used for pair-wise comparison of group differences.

RESULTS

The overall organ-adjusted Dice score for this model was 0.61 and overall CLD was 2.56 mm. When comparing prostates with symmetrical (n = 117) and asymmetrical (n = 23) benign prostate hyperplasia (BPH), the AI model performed better on symmetrical prostates compared to asymmetrical in both Dice score (0.64 vs. 0.51 respectively, p < 0.05) and mean CLD (2.3 mm vs. 3.8 mm respectively, p < 0.05). When calculating location-specific performance, the performance was highest at the apex and lowest at the base location of the prostate for Dice and CLD. Dice location dependence: symmetrical (Apex, Mid, Base: 0.69 vs. 0.67 vs. 0.54 respectively, p < 0.05) and asymmetrical (Apex, Mid, Base: 0.68 vs. 0.52 vs. 0.39 respectively, p < 0.05). CLD location dependence: symmetrical (Apex, Mid, Base: 1.43 mm vs. 2.15 mm vs. 3.28 mm, p < 0.05) and asymmetrical (Apex, Mid, Base: 1.83 mm vs. 3.1 mm vs. 6.24 mm, p < 0.05).

CONCLUSION

We developed a fully automated prostatic urethra segmentation AI tool yielding its best performance in prostate glands with symmetric BPH features. This system can potentially be used to assist treatment planning in patients who can undergo whole gland radiation therapy or ablative focal therapy.

Predictors of 18F-DCFPyL PET/CT Positivity in Patients with Biochemical Recurrence of Prostate Cancer After Local Therapy

Our objective was to investigate the factors predicting scan positivity and disease location in patients with biochemical recurrence (BCR) of prostate cancer (PCa) after primary local therapy using prostate-specific membrane antigen-targeted ¹⁸F-DCFPyL PET/CT. Methods: This was a 2-institution study including 245 BCR PCa patients after primary local therapy and negative results on conventional imaging. The patients underwent ¹⁸F-DCFPyL PET/CT. We tested for correlations of lesion detection rate and disease location with tumor characteristics, time from initial therapy, prostate-specific antigen (PSA) level, and PSA doubling time (PSAdt). Multivariate logistic regression analyses were used to determine predictors of a positive scan. Regression-based coefficients were used to develop nomograms predicting scan positivity and extrapelvic disease. Results: Overall, 79.2% (194/245) of patients had a positive ¹⁸F-DCFPyL PET/CT result, with detection rates of 48.2% (27/56), 74.3% (26/35), 84% (37/44), 96.7% (59/61), and 91.8% (45/49) for PSAs of <0.5, 0.5 to <1.0, 1.0 to <2.0, 2.0 to <5.0, and ≥5.0 ng/mL, respectively. Patients with lesions confined to the pelvis had lower PSAs than those with distant sites (1.6 ± 3.5 vs. 3.0 ± 6.3 ng/mL, P < 0.001). In patients treated with prostatectomy (n = 195), 24.1% (47/195) had a negative scan result, 46.1% (90/195) showed intrapelvic disease, and 29.7% (58/195) showed extrapelvic disease. In the postradiation subgroup (n = 50), ¹⁸F-DCFPyL PET/CT was always negative at a PSA lower than 1.0 ng/mL and extrapelvic disease was seen only when PSA was greater than 2.0 ng/mL. At multivariate analysis, PSA and PSAdt were independent predictive factors of scan positivity and the presence of extrapelvic disease in postsurgical patients, with area under the curve of 78% and 76%, respectively. PSA and PSAdt were independent predictors of the presence of extrapelvic disease in the postradiation cohort, with area under the curve of 85%. Time from treatment to scan was significantly longer for prostatectomy-bed-only recurrences than for those with bone or visceral disease (6.2 ± 6.4 vs. 2.4 ± 1.3 y, P < 0.001). Conclusion:¹⁸F-DCFPyL PET/CT offers high detection rates in BCR PCa patients. PSA and PSAdt are able to predict scan positivity and disease location. Furthermore, the presence of bone or visceral lesions is associated with shorter intervals from treatment than are prostate-bed-only recurrences. These tools might guide clinicians to select the most suitable candidates for ¹⁸F-DCFPyL PET/CT imaging.

A Cascaded Deep Learning-Based Artificial Intelligence Algorithm for Automated Lesion Detection and Classification on Biparametric Prostate Magnetic Resonance Imaging

RATIONALE AND OBJECTIVES

Prostate MRI improves detection of clinically significant prostate cancer; however, its diagnostic performance has wide variation. Artificial intelligence (AI) has the potential to assist radiologists in the detection and classification of prostatic lesions. Herein, we aimed to develop and test a cascaded deep learning detection and classification system trained on biparametric prostate MRI using PI-RADS for assisting radiologists during prostate MRI read out.

MATERIALS AND METHODS

T2-weighted, diffusion-weighted (ADC maps, high b value DWI) MRI scans obtained at 3 Tesla from two institutions (n = 1043 in-house and n = 347 Prostate-X, respectively) acquired between 2015 to 2019 were used for model training, validation, testing. All scans were retrospectively reevaluated by one radiologist. Suspicious lesions were contoured and assigned a PI-RADS category. A 3D U-Net-based deep neural network was used to train an algorithm for automated detection and segmentation of prostate MRI lesions. Two 3D residual neural network were used for a 4-class classification task to predict PI-RADS categories 2 to 5 and BPH. Training and validation used 89% (n = 1290 scans) of the data using 5 fold cross-validation, the remaining 11% (n = 150 scans) were used for independent testing. Algorithm performance at lesion level was assessed using sensitivities, positive predictive values (PPV), false discovery rates (FDR), classification accuracy, Dice similarity coefficient (DSC). Additional analysis was conducted to compare AI algorithm's lesion detection performance with targeted biopsy results.

RESULTS

Median age was 66 years (IQR = 60-71), PSA 6.7 ng/ml (IQR = 4.7-9.9) from in-house cohort. In the independent test set, algorithm correctly detected 111 of 198 lesions leading to 56.1% (49.3%-62.6%) sensitivity. PPV was 62.7% (95% CI 54.7%-70.7%) with FDR of 37.3% (95% CI 29.3%-45.3%). Of 79 true positive lesions, 82.3% were tumor positive at targeted biopsy, whereas of 57 false negative lesions, 50.9% were benign at targeted biopsy. Median DSC for lesion segmentation was 0.359. Overall PI-RADS classification accuracy was 30.8% (95% CI 24.6%-37.8%).

CONCLUSION

Our cascaded U-Net, residual network architecture can detect, classify cancer suspicious lesions at prostate MRI with good detection, reasonable classification performance metrics.

Near-Infrared Photoimmunotherapy (NIR-PIT) in Urologic Cancers

Near-infrared photoimmunotherapy (NIR-PIT) is a novel molecularly-targeted therapy that selectively kills cancer cells by systemically injecting an antibody-photoabsorber conjugate (APC) that binds to cancer cells, followed by the application of NIR light that drives photochemical transformations of the APC. APCs are synthesized by selecting a monoclonal antibody that binds to a receptor on a cancer cell and conjugating it to IRDye700DX silica-phthalocyanine dye. Approximately 24 h after APC administration, NIR light is delivered to the tumor, resulting in nearly-immediate necrotic cell death of cancer cells while causing no harm to normal tissues. In addition, NIR-PIT induces a strong immunologic effect, activating anti-cancer immunity that can be further boosted when combined with either immune checkpoint inhibitors or immune suppressive cell-targeted (e.g., regulatory T cells) NIR-PIT. Currently, a global phase III study of NIR-PIT in recurrent head and neck squamous cell carcinoma is ongoing. The first APC and NIR laser systems were approved for clinical use in September 2020 in Japan. In the near future, the clinical applications of NIR-PIT will expand to other cancers, including urologic cancers. In this review, we provide an overview of NIR-PIT and its possible applications in urologic cancers.

Minority Enrollment in Phase II and III Clinical Trials in Urologic Oncology

PURPOSE

Proportionate minority representation in clinical trials is an important step toward addressing health care inequities. Given the paucity of data on this topic in urologic oncology, we sought to quantify the enrollment of minority patients in clinical trials studying prostate, kidney, and bladder/urothelial cancers.

METHODS

The ClincialTrials.gov database was queried for completed phase II and III interventional trials in prostate, kidney, and bladder cancers. The SEER database was used to calculate the US prevalence of these genitourinary cancers. Representation quotients (RQ) were calculated to describe the relative proportion of each racial/ethnic group enrolled in clinical trials over the proportion of persons from each group among national cancer cases by cancer type.

RESULTS

Of 341 trials that met initial eligibility criteria, only 169 (49.7%) reported data on race or ethnicity. Aggregate RQs from 2000 to 2017 showed that White patients were continually over-represented in trials for all cancer types. Black and Asian patients were poorly represented across all cancer types. When stratified by 3-year increments, the RQs remained stable for all races, from 2000 to 2017. When stratified by ethnicity, Hispanic patients were under-represented across all cancer types in the study period. When examining representation by funding source, we found that US government-funded clinical trials proportionally enroll the most diverse patient populations over those funded by academic institutions and industry. Interestingly, more than 50% of the trials examined did not report race nor ethnicity, highlighting a crucial flaw in investigator compliance with federal clinical trial mandates.

CONCLUSION

Clinical trials targeting prostate, kidney, and bladder cancers continue to under-represent racial/ethnic minority patients. On the basis of the incidence of these cancers within minority populations, efforts should focus on creating racially and ethnically inclusive cancer research.

Detection of failure patterns using advanced imaging in patients with biochemical recurrence following low-dose-rate brachytherapy for prostate cancer

PURPOSE/OBJECTIVE(S)

This study describes the pattern of failure in patients with biochemical (BCR) recurrence after low-dose-rate (LDR) brachytherapy as a component of definitive treatment for prostate cancer.

METHODS

Patients with BCR after LDR brachytherapy ± external beam radiation therapy (EBRT) were enrolled on prospective IRB approved advanced imaging protocols. Patients underwent 3T multiparametric MRI (mpMRI); a subset underwent prostate specific membrane antigen (PSMA)-based PET/CT. Pathologic confirmation was obtained unless contraindicated.

RESULTS

Between January 2011 and April 2021, 51 patients with BCR after brachytherapy (n = 36) or brachytherapy + EBRT (n = 15) underwent mpMRI and were included in this analysis. Of 38 patients with available dosimetry, only two had D90<90%. The prostate and seminal vesicles were a site of failure in 66.7% (n = 34) and 39.2% (n = 20), respectively. PET/CT (n = 32 patients) more often identified lesions pelvic lymph nodes (50%; n = 16) and distant metastases (18.8%; n = 6), than mpMRI. Isolated nodal disease (9.8%; n = 5) and distant metastases (n = 1) without local recurrence were uncommon. Recurrence within the prostate was located in the transition zone in 48.5%, central or midline in 45.5%, and anterior in 36.4% of patients.

CONCLUSION

In this cohort of patients with BCR after LDR brachytherapy ± EBRT, the predominant recurrence pattern was local (prostate ± seminal vesicles) with frequent occurrence in the anterior prostate and transition zone. mpMRI and PSMA PET/CT provided complementary information to localize sites of recurrence, with PSMA PET/CT often confirming mpMRI findings and identifying occult nodal or distant metastases.

The role of novel imaging in prostate cancer focal therapy: treatment and follow-up

PURPOSE OF REVIEW

Multiparametric magnetic resonance imaging (mpMRI) has fundamentally changed how intraprostatic lesions are visualized, serving as a highly sensitive means for detecting clinically significant prostate cancer (csPCa) via image-targeted biopsy. However, limitations associated with mpMRI have led to the development of new imaging technologies with the goal of better characterizing intraprostatic disease burden to more accurately guide treatment planning and surveillance for prostate cancer focal therapy. Herein, we review several novel imaging modalities with an emphasis on clinical data reported within the past two years.

RECENT FINDINGS

7T MRI, artificial intelligence applied to mpMRI, positron emission tomography combined with either computerized tomography or MRI, contrast-enhanced ultrasound, and micro-ultrasound are novel imaging modalities with the potential to further improve intraprostatic lesion localization for applications in focal therapy for prostate cancer. Many of these technologies have demonstrated equivalent or favorable diagnostic accuracy compared to contemporary mpMRI for identifying csPCa and some have even shown improved capabilities to define lesion borders, to provide volumetric estimates of lesions, and to assess the adequacy of focal ablation of planned treatment zones.

SUMMARY

Novel imaging modalities with capabilities to better characterize intraprostatic lesions have the potential to improve accuracy in treatment planning, real-time assessment of the ablation zone, and posttreatment surveillance; however, many of these technologies require further validation to determine their clinical utility.

Differential VHL mutation patterns in bilateral clear cell RCC distinguishes between independent primary tumors and contralateral metastatic disease

OBJECTIVE

To evaluate whether bilateral, multifocal clear cell renal cell carcinoma (ccRCC) patients can be differentiated by VHL mutation analysis into cases that represent either multiple independently arising primary tumors, or a single primary tumor which has spread ipsilaterally as well as to the contralateral kidney. The nature of kidney cancer multifocality outside of known hereditary syndromes is as yet poorly understood.

MATERIALS AND METHODS

DNA from multiple tumors per patient were evaluated for somatic VHL gene mutation and hypermethylation. A subset of tumors with shared VHL mutations were analyzed with targeted, next-generation sequencing assays.

RESULTS

This cohort contained 5 patients with multiple tumors that demonstrated a shared somatic VHL mutation consistent with metastatic spread including to the contralateral kidney. In several cases this was substantiated by additional shared somatic mutations in ccRCC-associated genes. In contrast, the remaining 14 patients with multiple tumors demonstrated unique, unshared VHL alterations in every analyzed tumor, consistent with independently arising kidney tumors. None of these latter patients showed any evidence of local spread or distant metastasis.

CONCLUSION

The spectrum of VHL alterations within evaluated bilateral, multifocal ccRCC tumors from a single patient can distinguish between multiple independent tumor growth and metastasis. This can be performed using currently available clinical genetic tests and will improve the accuracy of patient diagnosis and prognosis, as well as informing appropriate management.

Deep learning-based artificial intelligence for prostate cancer detection at biparametric MRI

PURPOSE

To present fully automated DL-based prostate cancer detection system for prostate MRI.

METHODS

MRI scans from two institutions, were used for algorithm training, validation, testing. MRI-visible lesions were contoured by an experienced radiologist. All lesions were biopsied using MRI-TRUS-guidance. Lesions masks, histopathological results were used as ground truth labels to train UNet, AH-Net architectures for prostate cancer lesion detection, segmentation. Algorithm was trained to detect any prostate cancer ≥ ISUP1. Detection sensitivity, positive predictive values, mean number of false positive lesions per patient were used as performance metrics.

RESULTS

525 patients were included for training, validation, testing of the algorithm. Dataset was split into training (n = 368, 70%), validation (n = 79, 15%), test (n = 78, 15%) cohorts. Dice coefficients in training, validation sets were 0.403, 0.307, respectively, for AHNet model compared to 0.372, 0.287, respectively, for UNet model. In validation set, detection sensitivity was 70.9%, PPV was 35.5%, mean number of false positive lesions/patient was 1.41 (range 0-6) for UNet model compared to 74.4% detection sensitivity, 47.8% PPV, mean number of false positive lesions/patient was 0.87 (range 0-5) for AHNet model. In test set, detection sensitivity for UNet was 72.8% compared to 63.0% for AHNet, mean number of false positive lesions/patient was 1.90 (range 0-7), 1.40 (range 0-6) in UNet, AHNet models, respectively.

CONCLUSION

We developed a DL-based AI approach which predicts prostate cancer lesions at biparametric MRI with reasonable performance metrics. While false positive lesion calls remain as a challenge of AI-assisted detection algorithms, this system can be utilized as an adjunct tool by radiologists.

Magnetic Resonance Imaging-Guided Biopsy in Active Surveillance of Prostate Cancer

PURPOSE

The underlying premise of prostate cancer active surveillance (AS) is that cancers likely to metastasize will be recognized and eliminated before cancer-related disease can ensue. Our study was designed to determine the prostate cancer upgrading rate when biopsy guided by magnetic resonance imaging (MRGBx) is used before entry and during AS.

MATERIALS AND METHODS

The cohort included 519 men with low- or intermediate-risk prostate cancer who enrolled in prospective studies (NCT00949819 and NCT00102544) between February 2008 and February 2020. Subjects were preliminarily diagnosed with Gleason Grade Group (GG) 1 cancer; AS began when subsequent MRGBx confirmed GG1 or GG2. Participants underwent confirmatory MRGBx (targeted and systematic) followed by surveillance MRGBx approximately every 12 to 24 months. The primary outcome was tumor upgrading to ≥GG3.

RESULTS

Upgrading to ≥GG3 was found in 92 men after a median followup of 4.8 years (IQR 3.1-6.5) after confirmatory MRGBx. Upgrade-free probability after 5 years was 0.85 (95% CI 0.81-0.88). Cancer detected in a magnetic resonance imaging lesion at confirmatory MRGBx increased risk of subsequent upgrading during AS (HR 2.8; 95% CI 1.3-6.0), as did presence of GG2 (HR 2.9; 95% CI 1.1-8.2) In men who upgraded ≥GG3 during AS, upgrading was detected by targeted cores only in 27%, systematic cores only in 25% and both in 47%. In 63 men undergoing prostatectomy, upgrading from MRGBx was found in only 5 (8%).

CONCLUSIONS

When AS begins and follows with MRGBx (targeted and systematic), upgrading rate (≥GG3) is greater when tumor is initially present within a magnetic resonance imaging lesion or when pathology is GG2 than when these features are absent.

Immunotherapy to prevent progression on active surveillance study (IPASS): A phase II, randomized, double-blind, controlled trial of PROSTVAC in prostate cancer patients who are candidates for active surveillance

249

Background: Immunotherapy could potentially prevent disease progression for early-stage prostate cancer. In this randomized Phase 2 clinical trial, we evaluated the clinical effects of PROSTVAC, a vaccinia/fowlpox viral vector-based immunotherapy that contains PSA and three T-cell costimulatory molecules, in patients with localized prostate cancer. Methods:154 patients with clinically localized, low- or favorable intermediate-risk prostate cancer active surveillance were randomized (2:1) to receive 7 doses of subcutaneous PROSTVAC or placebo (empty fowlpox vector) over 140 days. Post-intervention prostate biopsy was performed 7-14 days after the last dose. Participants were followed for 6 months post-treatment. The primary outcome was change from baseline to post-vaccination in CD4 and CD8 T cell infiltration in biopsy tumor tissue. Secondary outcomes included changes in prostate biopsy Gleason grade (Grade Group) and serum PSA. Results: There were no differences in CD4 and CD8 densities (count of cells/mm2) in post-treatment biopsy tumor tissue between groups ( p = 0.63 and p = 0.75, respectively). Compared to placebo, patients who received PROSTVAC were less likely to demonstrate upgrading at follow-up biopsy, but this difference did not attain significance (22% vs. 40%, p= 0.08). There was no difference in the change of PSA from baseline to 6 months post-treatment between arms ( p= 0.30). Conclusions: In this first-of-kind trial of immunotherapy for localized prostate cancer, PROSTVAC was well tolerated but did not elicit significant prostate tissue T-cell responses compared to placebo. The favorable post-treatment biopsy grade findings in PROSTVAC patients merit further evaluation and longer-term clinical follow-up. Clinical trial information: NCT02326805.

Atherosclerotic Plaque Burden on Abdominal CT: Automated Assessment With Deep Learning on Noncontrast and Contrast-enhanced Scans

BACKGROUND

Abdominal aortic atherosclerotic plaque burden may have clinical significance but manual measurement is time-consuming and impractical.

PURPOSE

To perform external validation on an automated atherosclerotic plaque detector for noncontrast and postcontrast abdominal CT.

MATERIALS AND METHODS

The training data consisted of 114 noncontrast CT scans and 23 postcontrast CT urography scans. The testing data set consisted of 922 CT colonography (CTC) scans, and 1207 paired noncontrast and postcontrast CT scans from renal donors from a second institution. Reference standard data included manual plaque segmentations in the 137 training scans and manual plaque burden measurements in the 922 CTC scans. The total Agatston score and group (0-3) was determined using fully-automated deep learning software. Performance was assessed by measures of agreement, linear regression, and paired evaluations.

RESULTS

On CTC scans, automated Agatston scoring correlated highly with manual assessment (R² = 0.94). On paired renal donor CT scans, automated Agatston scoring on postcontrast CT correlated highly with noncontrast CT (R² = 0.95). When plaque burden was expressed as a group score, there was excellent agreement for both the CTC (weighted kappa 0.80 ± 0.01 [95% confidence interval: 0.78-0.83]) and renal donor (0.83 ± 0.02 [0.79-0.86]) assessments.

CONCLUSION

Fully automated detection, segmentation, and scoring of abdominal aortic atherosclerotic plaques on both pre- and post-contrast CT was validated and may have application for population-based studies.

Association Between Multiparametric Magnetic Resonance Imaging of the Prostate and Oncological Outcomes after Primary Treatment for Prostate Cancer: A Systematic Review and Meta-analysis

CONTEXT

The diagnostic accuracy of multiparametric magnetic resonance imaging (mpMRI) for prostate cancer (PCa) diagnosis has been extensively explored. Little is known about the prognostic value of mpMRI suspicion scores and other quantitative mpMRI information.

OBJECTIVE

To systematically review the current literature assessing the relationship between pretreatment mpMRI and oncological outcomes after primary treatment for PCa to assess the role of mpMRI as a prognostic tool.

EVIDENCE ACQUISITION

A computerized bibliographic search of MEDLINE/PubMed, EMBASE, Scopus, and the Cochrane Library CENTRAL databases was performed for all studies assessing the relationship between mpMRI and oncological outcomes after primary treatment for PCa. The review protocol is registered in the PROSPERO database (CRD42020209899).

EVIDENCE SYNTHESIS

A total of six studies were included. Reliable evidence is still limited in this field. The Prostate Imaging-Reporting and Data System (PI-RADS) score was an independent predictor of biochemical recurrence (BCR) after radical prostatectomy (RP) in the majority of the studies included. The tumor volume at mpMRI was not significantly associated with BCR after RP for PCa. Data on disease progression and PCa-specific mortality are limited. Heterogeneity among the studies was substantial.

CONCLUSIONS

The review shows that PI-RADS scores provide information on the future likelihood of cancer recurrence or progression, at least for men undergoing RP. We are of the view that this information should be taken into account to identify men at higher risk of unfavorable outcomes.

PATIENT SUMMARY

A higher Prostate Imaging-Reporting and Data System score for magnetic resonance imaging of the prostate seems to be positively associated with oncological failure in prostate cancer and should be incorporated into future risk models.

Emerging role for local therapy in oligometastatic prostate cancer

Oligometastatic prostate cancer is a subtype of metastatic disease that generally is defined by the presence of 5 or fewer metastatic lesions. Metastatic prostate cancer currently is treated with androgen deprivation therapy and additional systemic therapy, such as novel antiandrogen medications or chemotherapy. The management of metastatic prostate cancer is evolving, however, with the notion that some patients with low-burden metastatic disease may benefit from both local and systemic therapy. Local therapy of the prostate in the setting of oligometastatic prostate cancer is a new concept. Evidence from retrospective studies suggests that cytoreductive therapy, including radical prostatectomy, can improve overall survival in these patients. Ongoing randomized trials are comparing cytoreductive therapy with standard-of-care treatment options. Local therapy in the form of radiation has also been investigated in phase 2 randomized trials. In this review, we discuss the biological and clinical rationales for local therapy, review the current evidence for local therapy, and compare the clinical designs of various ongoing trials.

Neoadjuvant PROSTVAC prior to radical prostatectomy enhances T-cell infiltration into the tumor immune microenvironment in men with prostate cancer

Background

Clinical trials have shown the ability of therapeutic vaccines to generate immune responses to tumor-associated antigens (TAAs). What is relatively less known is if this translates into immune-cell (IC) infiltration into the tumor microenvironment. This study examined whether neoadjuvant prostate-specific antigen (PSA)-targeted vaccination with PROSTVAC could induce T-cell immunity, particularly at the tumor site.

Methods

An open-label, phase II study of neoadjuvant PROSTVAC vaccine enrolled 27 patients with localized prostate cancer awaiting radical prostatectomy (RP). We evaluated increases in CD4 and CD8 T-cell infiltrates (RP tissue vs baseline biopsies) using a six-color multiplex immunofluorescence Opal method. Antigen-specific responses were assessed by intracellular cytokine staining after in vitro stimulation of peripheral blood mononuclear cells with overlapping 15-mer peptide pools encoding the TAAs PSA, brachyury and MUC-1.

Results

Of 27 vaccinated patients, 26 had matched prevaccination (biopsy) and postvaccination (RP) prostate samples available for non-compartmentalized analysis (NCA) and compartmentalized analysis (CA). Tumor CD4 T-cell infiltrates were significantly increased in postvaccination RP specimens compared with baseline biopsies by NCA (median 176/mm² vs 152/mm²; IQR 136–317/mm² vs 69–284/mm²; p=0.0249; median ratio 1.20; IQR 0.64–2.25). By CA, an increase in both CD4 T-cell infiltrates at the tumor infiltrative margin (median 198/mm² vs 151/mm²; IQR 123–500/mm² vs 85–256/mm²; p=0.042; median ratio 1.44; IQR 0.59–4.17) and in CD8 T-cell infiltrates at the tumor core (median 140/mm² vs 105/mm²; IQR 91–175/mm² vs 83–163/mm²; p=0.036; median ratio 1.25; IQR 0.88–2.09) were noted in postvaccination RP specimens compared with baseline biopsies. A total of 13/25 patients (52%) developed peripheral T-cell responses to any of the three tested TAAs (non-neoantigens); five of these had responses to more than one antigen of the three evaluated.

Conclusion

Neoadjuvant PROSTVAC can induce both tumor immune response and peripheral immune response.

Trial registration number

NCT02153918.

MRI-guided focal laser ablation of prostate cancer: a prospective single-arm, single-center trial with 3 years of follow-up

PURPOSE

We aimed to assess post-interventional and 36-month follow-up results of a single-center, single-arm, in-bore phase I trial of focal laser ablation (FLA) guided by multiparametric magnetic resonance imaging (mpMRI).

METHODS

FLA procedures were done in-bore MRI using a transperineal approach. Primary endpoints were feasibility and safety expressed as lack of grade 3 complications. Secondary endpoints were changes in international prostate symptom score (IPSS), sexual health inventory for men (SHIM), quality of life (QoL) scores, and serum prostate specific antigen (PSA) levels. Treatment outcomes were assessed by combined mpMRI-ultrasound fusion-guided and extended sextant systematic biopsy after 12, 24, and optionally after 36 months.

RESULTS

Fifteen participants were included. Seven patients (46.67%) had Gleason 3+3 and 8 patients (53.33%) had Gleason 3+4 cancer. All patients tolerated the procedure well, and no grade 3/4 complications occurred. All grade 1 and 2 complications were transient and resolved completely. There was no significant change in mean IPSS from baseline (-1, p = 0.460) and QoL (0, p = 0.441) scores following FLA but there was a significant drop in mean SHIM scores (-2, p = 0.010) compared to pretreatment baselines. Mean PSA significantly decreased after FLA (-2.5, p < 0.001). Seven out of 15 patients (46.67%) had residual cancer in, adjacent, or in close proximity to the treatment area (1 × 4+3=7, 1 × 3+4=7, and 5 × 3+3=6). Four out of 15 patients (26.67%) underwent salvage therapy (2 repeat FLA, 2 radical prostatectomy).

CONCLUSION

After 3 years of follow-up we conclude focal laser ablation is safe and feasible without significant complications.