The University of California, San Francisco Cancer of the Prostate Risk Assessment score: a straightforward and reliable preoperative predictor of disease recurrence after radical prostatectomy

Prostate Cancer Risk Stratification in NRG Oncology Phase III Randomized Trials Using Multimodal Deep Learning With Digital Histopathology

PURPOSE

Current clinical risk stratification methods for localized prostate cancer are suboptimal, leading to over- and undertreatment. Recently, machine learning approaches using digital histopathology have shown superior prognostic ability in phase III trials. This study aims to develop a clinically usable risk grouping system using multimodal artificial intelligence (MMAI) models that outperform current National Comprehensive Cancer Network (NCCN) risk groups.

MATERIALS AND METHODS

The cohort comprised 9,787 patients with localized prostate cancer from eight NRG Oncology randomized phase III trials, treated with radiation therapy, androgen deprivation therapy, and/or chemotherapy. Locked MMAI models, which used digital histopathology images and clinical data, were applied to each patient. Expert consensus on cut points defined low-, intermediate-, and high-risk groups on the basis of 10-year distant metastasis rates of 3% and 10%, respectively. The MMAI's reclassification and prognostic performance were compared with the three-tier NCCN risk groups.

RESULTS

The median follow-up for censored patients was 7.9 years. According to NCCN risk categories, 30.4% of patients were low-risk, 25.5% intermediate-risk, and 44.1% high-risk. The MMAI risk classification identified 43.5% of patients as low-risk, 34.6% as intermediate-risk, and 21.8% as high-risk. MMAI reclassified 1,039 (42.0%) patients initially categorized by NCCN. Despite the MMAI low-risk group being larger than the NCCN low-risk group, the 10-year metastasis risks were comparable: 1.7% (95% CI, 0.2 to 3.2) for NCCN and 3.2% (95% CI, 1.7 to 4.7) for MMAI. The overall 10-year metastasis risk for NCCN high-risk patients was 16.6%, with MMAI further stratifying this group into low-, intermediate-, and high-risk, showing metastasis rates of 3.4%, 8.2%, and 26.3%, respectively.

CONCLUSION

The MMAI risk grouping system expands the population of men identified as having low metastatic risk and accurately pinpoints a high-risk subset with elevated metastasis rates. This approach aims to prevent both overtreatment and undertreatment in localized prostate cancer, facilitating shared decision making.

Determining Long-term Prostate Cancer Outcomes for Active Surveillance Patients Without Early Disease Progression: Implications for Slowing or Stopping Surveillance

BACKGROUND AND OBJECTIVE

Active surveillance (AS) of prostate cancer (PCa) is the standard of care for low-grade disease, but there is limited guidance on tailoring protocols for stable patients. We investigated long-term outcomes for patients without initial progression and risk factors for upgrade.

METHODS

Men on AS with Gleason grade group (GG) 1 PCa on three serial biopsies, ≥5 yr without progression, and ≥10 yr of follow-up were included. Outcomes were upgrade (GG ≥2), major upgrade (GG ≥3), progression to treatment, metastasis, PCa-specific survival, and overall survival. Cox proportional hazards regression models were used to estimate the associations between patient characteristics and risk of upgrade.

KEY FINDINGS AND LIMITATIONS

A total of 774 men met the inclusion criteria. At 10, 12, and 15 yr, upgrade-free survival rates were 56%, 45%, and 21%; major upgrade-free survival rates were 88%, 83%, and 61%; treatment-free survival rates were 86%, 83%, and 73%; metastasis-free survival rates were 99%, 99%, and 98%; and overall survival rates were 98%, 96%, and 95%, respectively. PCa-specific survival was 100% at 15 yr. On a multivariable analysis, year of diagnosis, age, body mass index (BMI), and biopsy core positivity were associated with upgrade (all p < 0.01), whereas age and prostate-specific antigen (PSA) density were associated with major upgrade.

CONCLUSIONS AND CLINICAL IMPLICATIONS

Patients without progression for 5 yr on AS had modest rates of upgrade and low rates of metastasis, and mortality at 15 yr of follow-up. Year of diagnosis, older age, increased BMI, and increased biopsy core positivity were associated with upgrade, whereas older age and greater PSA density were associated with an increased risk of major upgrade. A subset of these patients may benefit from deintensification of AS protocols.

PATIENT SUMMARY

There are little reported data or clinical guidelines for patients with PCa who are stable for many years on active surveillance (AS). We show, in a large cohort, that PCa patients without progression for 5 yr on AS have modest rates of upgrade and very low rates of metastasis, and mortality rates at 15 yr of follow-up, and that older age, increased body mass index, and increased PCa volume are associated with an increased likelihood of future upgrade. This study supports continued AS in this patient population and deintensification in select patients.

Preoperative multiparametric magnetic resonance imaging based risk stratification system for predicting biochemical recurrence after radical prostatectomy

BACKGROUND

Multiparametric magnetic resonance imaging (mpMRI) is used as a current marker in preoperative staging and surgical decision-making, but current evidence on predicting post-surgical oncological outcomes based on preoperative mpMRI findings is limited. In this study We aimed to develop a risk classification based on mpMRI and mpMRI-derived biopsy findings to predict early biochemical recurrence (BCR) after radical prostatectomy.

METHODS

Between January 2017 and January 2023, the data of 289 patients who underwent mpMRI, transrectal ultrasound-guided cognitive and fusion targeted biopsies, and subsequent radical prostatectomy (RP) with or without pelvic lymph node dissection in a single center were retrospectively re-evaluated. BCR was defined as a prostate specific-antigen (PSA) ≥ 0.2 ng/mL at least twice after RP. Multivariate logistic regression models tested the predictors of BCR. The regression tree analysis stratified patients into risk groups based on preoperative mpMRI characteristics. Receiver operating characteristic (ROC)-derived area under the curve (AUC) estimates were used to test the accuracy of the regression tree-derived risk stratification tool.

RESULTS

BCR was detected in 47 patients (16.2 %) at a median follow-up of 24 months. In mpMRI based multivariate analyses, the maximum diameter of the index lesion (HR 1.081, 95%Cl 1.015-1.151, p = 0.015) the presence of PI-RADS 5 lesions (HR 2.604, 95%Cl 1.043-6.493, p = 0.04), ≥iT3a stage (HR 2.403, 95%Cl 1.013-5.714, p = 0.046) and ISUP grade ≥4 on biopsy (HR 2.440, 95%Cl 1.123-5.301, p = 0.024) were independent predictors of BCR. In regression tree analysis, patients were stratified into three risk groups: maximum diameter of index lesion, biopsy ISUP grade, and clinical stage on mpMRI. The regression tree-derived risk stratification model had moderate-good accuracy in predicting early BCR (AUC 77 %) CONCLUSION: Straightforward mpMRI and mpMRI-derived biopsy-based risk stratification for BCR prediction provide an additional clinical predictive model to the currently available pathological risk tools.

MRI characteristics predict risk of pathological upgrade in patients with ISUP grade group 1 prostate cancer

OBJECTIVE

This study aims to analyse multiparametric MRI (mpMRI) characteristics of patients diagnosed with ISUP grade group (GG) 1 prostate cancer (PC) on initial target plus systematic MRI/TRUS fusion-guided biopsy and investigate histopathological progression during follow-up.

METHODS

A retrospective single-centre cohort analysis was conducted on consecutive patients with mpMRI visible lesions (PI-RADS ≥ 3) and detection of ISUP-1-PC at the time of initial biopsy. The study assessed clinical, mpMRI, and histopathological parameters. Subcohorts were analysed with (1) patients who had confirmed ISUP-1-PC and (2) patients who experienced histopathological upgrading to ISUP ≥ 2 PC during follow-up either at re-biopsy or radical prostatectomy (RP).

RESULTS

A total of 156 patients (median age 65 years) between March 2014 and August 2021 were included. Histopathological upgrading to ISUP ≥ 2 was detected in 55% of patients during a median follow-up of 9.5 months (IQR 2.2-16.4). When comparing subgroups with an ISUP upgrade and sustained ISUP 1 PC, they differed significantly in contact length of the index lesion to the pseudocapsule, ADC value, PI-RADS category, and the MRI grading group (mGG) (p < 0.05). In the ISUP GG ≥ 2 subgroup, 91% of men had PI-RADS category 4 or 5 and 82% exhibited the highest mGG (mGG3). In multivariate analysis, mGG was the only independent parameter for predicting ISUP ≥ 2-PC in these patients.

CONCLUSIONS

MRI reveals important information about PC aggressiveness and should be incorporated into clinical decision-making when ISUP-1-PC is diagnosed. In cases of specific MRI characteristics adverse to the histopathology, early re-biopsy might be considered.

CLINICAL RELEVANCE STATEMENT

In cases with clear MRI characteristics for clinically significant prostate cancer (e.g., mGG 3 and/or PI-RADS 5, cT3, or clear focal PI-RADS 4 lesions on MRI) and ISUP GG 1 PC diagnosed on initial prostate biopsy, MRI findings should be incorporated into clinical decision-making and early re-biopsy (e.g., within 6 months) might be considered.

KEY POINTS

MRI reveals important information about prostate cancer (PC) aggressiveness. MRI should be incorporated into clinical decision-making when ISUP GG 1 PC is diagnosed on initial prostate biopsy. In cases of specific MRI characteristics adverse to the histopathology, early re-biopsy might be considered.

Evolving Paradigms in Prostate Cancer: The Integral Role of Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography in Primary Staging and Therapeutic Decision-Making

Prostate-specific membrane antigen (PSMA) positron emission tomography or computed tomography (PET/CT) has emerged as a superior imaging option to conventional imaging for prostate cancer. The majority of early evidence and prospective trials evaluated PSMA PET/CT in the biochemical recurrence or metastatic setting. However, there has been an increasing number of prospective trials in the primary setting. The purpose of this narrative review was to describe the role of PSMA PET/CT in localized primary prostate cancer. This narrative review focuses on the prospective evidence available in this setting. We detail the current practice and future potential for PSMA PET/CT to be used in multiple stages of localized primary prostate cancer. The most common practice currently for PSMA PET/CT is in the primary nodal and metastatic staging of high-risk prostate cancer. We describe other roles of PSMA PET/CT, including in intermediate-risk prostate cancer as well as local staging and the impact on radiation therapy and surgical management. We also discuss the potential future roles of PSMA PET/CT in prediagnosis such as risk stratification for biopsy, prognosis, and specific surgical roles. Potential pitfalls of PSMA PET/CT are also addressed. PSMA PET/CT has already had a significant influence on prostate cancer, and there will continue to be a greater role for this imaging modality in localized primary prostate cancer.

Active surveillance selection and 3-year durability in intermediate-risk prostate cancer following genomic testing

BACKGROUND

Genomic testing can add risk stratification information to clinicopathological features in prostate cancer, aiding in shared medical decision-making between the clinician and patient regarding whether active surveillance (AS) or definitive treatment (DT) is most appropriate. Here we examined initial AS selection and 3-year AS durability in patients diagnosed with localized intermediate-risk prostate cancer who underwent Prolaris testing before treatment decision-making.

METHODS

This retrospective observational cohort study included 3208 patients from 10 study sites who underwent Prolaris testing at diagnosis from September 2015 to December 2018. Prolaris utilizes a combined clinical cell cycle risk score calculated at diagnostic biopsy to stratify patients by the Prolaris AS threshold (below threshold, patient recommended to AS or above threshold, patient recommended to DT). AS selection rates and 3-year AS durability were compared in patients recommended to AS or DT by Prolaris testing. Univariable and multivariable logistic regression models and Cox proportional hazard models were used with molecular and clinical variables as predictors of initial treatment decision and AS durability, respectively.

RESULTS

AS selection was ~2 times higher in patients recommended to AS by Prolaris testing than in those recommended to DT (p < 0.0001). Three-year AS durability was ~1.5 times higher in patients recommended to AS by Prolaris testing than in those recommended to DT (p < 0.0001). Prolaris treatment recommendation remained a statistically significant predictor of initial AS selection and AS durability after accounting for CAPRA or Gleason scores.

CONCLUSIONS

Prolaris added significant information to clinical risk stratification to aid in treatment decision making. Intermediate-risk prostate cancer patients who were recommended to AS by Prolaris were more likely to initially pursue AS and were more likely to remain on AS at 3 years post-diagnosis than patients recommended to DT.

Deep Learning Features Can Improve Radiomics-Based Prostate Cancer Aggressiveness Prediction

PURPOSE

Emerging evidence suggests that the use of artificial intelligence can assist in the timely detection and optimization of therapeutic approach in patients with prostate cancer. The conventional perspective on radiomics encompassing segmentation and the extraction of radiomic features considers it as an independent and sequential process. However, it is not necessary to adhere to this viewpoint. In this study, we show that besides generating masks from which radiomic features can be extracted, prostate segmentation and reconstruction models provide valuable information in their feature space, which can improve the quality of radiomic signatures models for disease aggressiveness classification.

MATERIALS AND METHODS

We perform 2,244 experiments with deep learning features extracted from 13 different models trained using different anatomic zones and characterize how modeling decisions, such as deep feature aggregation and dimensionality reduction, affect performance.

RESULTS

While models using deep features from full gland and radiomic features consistently lead to improved disease aggressiveness prediction performance, others are detrimental. Our results suggest that the use of deep features can be beneficial, but an appropriate and comprehensive assessment is necessary to ensure that their inclusion does not harm predictive performance.

CONCLUSION

The study findings reveal that incorporating deep features derived from autoencoder models trained to reconstruct the full prostate gland (both zonal models show worse performance than radiomics only models), combined with radiomic features, often lead to a statistically significant increase in model performance for disease aggressiveness classification. Additionally, the results also demonstrate that the choice of feature selection is key to achieving good performance, with principal component analysis (PCA) and PCA + relief being the best approaches and that there is no clear difference between the three proposed latent representation extraction techniques.

Association Between the Decipher Genomic Classifier and Prostate Cancer Outcome in the Real-world Setting

BACKGROUND AND OBJECTIVE

Although the prognostic significance of the Decipher prostate cancer genomic classifier (GC) has been established largely from analyses of archival tissue, less is known about the associations between the results of Decipher testing and oncologic outcomes among patients receiving contemporaneous testing and treatment in the real-world practice setting. Our objective was to assess the associations between the Decipher GC and risks of metastasis and biochemical recurrence (BCR) following prostate biopsy and radical prostatectomy (RP) among patients tested and treated in the real-world setting.

METHODS

A retrospective cohort study was conducted using a novel longitudinal linkage of transcriptomic data from the Decipher GC and real-world clinical data (RWD) aggregated from insurance claims, pharmacy records, and electronic health record data across payors and sites of care. Kaplan-Meier and Cox proportional hazards regressions were used to examine the associations between the GC and study outcomes, adjusting for clinical and pathologic factors.

KEY FINDINGS AND LIMITATIONS

Metastasis from prostate cancer and BCR after radical prostatectomy, Decipher GC continuous score, and risk categories were evaluated. We identified 58 935 participants who underwent Decipher testing, including 33 379 on a biopsy specimen and 25 556 on an RP specimen. The median age was 67 yr (interquartile range [IQR] 62-72) at biopsy testing and 65 yr (IQR 59-69) at RP. The median GC score was 0.43 (IQR 0.27-0.66) among biopsy-tested patients and 0.54 (0.32-0.79) among RP-tested patients. The GC was independently associated with the risk of metastasis among biopsy-tested (hazard ratio [HR] per 0.1 unit increase in GC 1.21 [95% confidence interval {CI} 1.16-1.27], p < 0.001) and RP-tested (HR 1.20 [95% CI 1.17-1.24], p < 0.001) patients after adjusting for baseline clinical and pathologic risk factors. In addition, the GC was associated with the risk of BCR among RP-tested patients (HR 1.12 [95% CI 1.10-1.14], p < 0.001) in models adjusted for age and Cancer of the Prostate Risk Assessment postsurgical score.

CONCLUSIONS AND CLINICAL IMPLICATIONS

This real-world study of a novel transcriptomic linkage conducted at a national scale supports the external prognostic validity of the Decipher GC among patients managed in contemporary practice.

PATIENT SUMMARY

This study looked at the use of the Decipher genomic classifier, a test used to help understand the aggressiveness of a patient's prostate cancer. Looking at the results of 58 935 participants who underwent testing, we found that the Decipher test helped estimate the risk of cancer recurrence and metastasis.

The Impact of Delayed Radical Prostatectomy on Recurrence Outcomes After Initial Active Surveillance: Results from a Large Institutional Cohort

BACKGROUND AND OBJECTIVE

Active surveillance (AS) of prostate cancer (PCa) involves regular monitoring for disease progression. The aim is to avoid unnecessary treatment while ensuring appropriate and timely treatment for those whose disease progresses. AS has emerged as the standard of care for low-grade (Gleason grade 1, GG 1) PCa. Opponents are concerned that initial undersampling and delay of definitive management for patients with GG 2 disease may lead to adverse outcomes. We sought to determine whether the timing for definitive management of GG 2 PCa, either upfront or after initial AS, affects recurrence outcomes after radical prostatectomy (RP).

METHODS

Participants were diagnosed with cT1-2N0/xM0/x, prostate-specific antigen (PSA) <20 ng/ml, and GG 1-2 PCa between 2000 and 2020 and underwent immediate RP for GG 2 or AS followed by delayed RP on upgrading to GG 2. The outcome was recurrence-free survival (RFS) after surgery, with recurrence defined as either biochemical failure (2 PSA measurements ≥0.2 ng/ml) or a second treatment. Multivariable Cox proportional-hazards regression models were used to calculate associations between the timing for definitive RP and the risk of recurrence, adjusted for age at diagnosis, percentage of positive biopsy cores (PPC), PSA density, PSA before RP, year of diagnosis, surgical margins, genomic risk score, and prostate MRI findings.

KEY FINDINGS

Of the 1259 men who met the inclusion criteria, 979 underwent immediate RP after diagnosis of GG 2, 190 underwent RP within 12 mo of upgrading to GG 2 on AS, and 90 men underwent RP >12 mo after upgrading to GG 2. The 5-yr RFS rates were 81% for the immediate RP group, 80% for the delayed RP ≤12 mo, and 70% for the delayed RP >12 mo group (univariate log-rank p = 0.03). Cox multivariable regression demonstrated no difference in RFS outcomes between immediate RP for GG 2 disease and delayed RP after upgrading on AS. PPC (hazard ratio [HR] per 10% increment 1.08, 95% confidence interval [CI] 1.02-1.15; p = 0.01) and PSA before RP (HR 1.06, 95% CI 1.03-1.09; p < 0.01) were significantly associated with the risk of recurrence.

CONCLUSIONS AND CLINICAL IMPLICATIONS

PPC and PSA before RP, but not the timing of definitive surgery after upgrade to GG 2, were associated with the risk of PCa recurrence after RP on multivariable analysis. These findings support the safety of AS and delayed definitive therapy for a subset of patients with GG 2 disease.

PATIENT SUMMARY

In a large group of 1259 patients with low-grade prostate cancer, we found that delaying surgical treatment after an initial period of active surveillance resulted in no differences in prostate cancer recurrence. Our results support the safety of active surveillance for low-grade prostate cancer.

Which men benefit from prostate cancer screening? Prostate cancer mortality by subgroup in the European Randomised Study of Screening for Prostate Cancer

OBJECTIVE

To evaluate whether a subgroup of men can be identified that would benefit more from screening than others.

MATERIALS AND METHODS

This retrospective cohort study was based on three European Randomised Study of Screening for Prostate Cancer (ERSPC) centres, Finland, the Netherlands and Sweden. We identified 126 827 men aged 55-69 years in the study who were followed for maximum of 16 years after randomisation. The primary outcome was prostate cancer (PCa) mortality. We analysed three age groups 55-59, 60-64 and 65-69 years and PCa cases within four European Association of Urology (EAU) risk groups: low, intermediate, high risk, and advanced disease.

RESULTS

The hazard ratio (HR) for PCa mortality in the screening arm relative to the control arm for men aged 55-59 years was 0.96 (95% confidence interval [CI] 0.75-1.24) in Finland, 0.70 (95% CI 0.44-1.12) in the Netherlands and 0.42 (95% CI 0.24-0.73) in Sweden. The HR for men aged 60-64 years was 1.03 (95% CI 0.77-1.37) in Finland, 0.76 (95% CI 0.50-1.16) in the Netherlands and 0.97 (95% CI 0.64-1.48) in Sweden. The HR for men aged 65-69 years was 0.80 (95% CI 0.62-1.03) in Finland and 0.57 (95% CI 0.38-0.83) in the Netherlands, and this age group was absent in Sweden. In the EAU risk group analysis, PCa mortality rates were materially lower for men with advanced disease at diagnosis in all three countries: 0.67 (95% CI 0.56-0.82) in Finland, 0.28 (95% CI 0.18-0.44) in the Netherlands, and 0.48 (95% CI 0.30-0.78) in Sweden.

CONCLUSION

We were unable to unequivocally identify the optimal age group for screening, as mortality reduction differed among centres and age groups. Instead, the screening effect appears to depend on screening duration, and the number and frequency of screening rounds. PCa mortality reduction by screening is largely attributable to stage shift.

Molecular analysis of archival diagnostic prostate cancer biopsies identifies genomic similarities in cases with progression post-radiotherapy, and those with de novo metastatic disease

BACKGROUND

It is important to identify molecular features that improve prostate cancer (PCa) risk stratification before radical treatment with curative intent. Molecular analysis of historical diagnostic formalin-fixed paraffin-embedded (FFPE) prostate biopsies from cohorts with post-radiotherapy (RT) long-term clinical follow-up has been limited. Utilizing parallel sequencing modalities, we performed a proof-of-principle sequencing analysis of historical diagnostic FFPE prostate biopsies. We compared patients with (i) stable PCa (sPCa) postprimary or salvage RT, (ii) progressing PCa (pPCa) post-RT, and (iii) de novo metastatic PCa (mPCa).

METHODS

A cohort of 19 patients with diagnostic prostate biopsies (n = 6 sPCa, n = 5 pPCa, n = 8 mPCa) and mean 4 years 10 months follow-up (diagnosed 2009-2016) underwent nucleic acid extraction from demarcated malignancy. Samples underwent 3'RNA sequencing (3'RNAseq) (n = 19), nanoString analysis (n = 12), and Illumina 850k methylation (n = 8) sequencing. Bioinformatic analysis was performed to coherently identify differentially expressed genes and methylated genomic regions (MGRs).

RESULTS

Eighteen of 19 samples provided useable 3'RNAseq data. Principal component analysis (PCA) demonstrated similar expression profiles between pPCa and mPCa cases, versus sPCa. Coherently differentially methylated probes between these groups identified ~600 differentially MGRs. The top 50 genes with increased expression in pPCa patients were associated with reduced progression-free survival post-RT (p < 0.0001) in an external cohort.

CONCLUSIONS

3'RNAseq, nanoString and 850k-methylation analyses are each achievable from historical FFPE diagnostic pretreatment prostate biopsies, unlocking the potential to utilize large cohorts of historic clinical samples. Profiling similarities between individuals with pPCa and mPCa suggests biological similarities and historical radiological staging limitations, which warrant further investigation.

What the urologist needs to know before radical prostatectomy: MRI effective support to pre-surgery planning

BACKGROUND

Radical prostatectomy (RP) is recommended in case of localized or locally advanced prostate cancer (PCa), but it can lead to side effects, including urinary incontinence (UI) and erectile dysfunction (ED). Magnetic resonance imaging (MRI) is recommended for PCa diagnosis and staging, but it can also improve preoperative risk-stratification.

PURPOSE

This nonsystematic review aims to provide an overview on factors involved in RP side effects, highlighting anatomical and pathological aspects that could be included in a structured report.

EVIDENCE SYNTHESIS

Considering UI evaluation, MR can investigate membranous urethra length (MUL), prostate volume, the urethral sphincter complex, and the presence of prostate median lobe. Longer MUL measurement based on MRI is linked to a higher likelihood of achieving continence restoration. For ED assessment, MRI and diffusion tensor imaging identify the neurovascular bundle and they can aid in surgery planning. Finally, MRI can precisely describe extra-prostatic extension, prostate apex characteristics and lymph-node involvement, providing valuable preoperative information for PCa treatment.

CONCLUSIONS

Anatomical principals structures involved in RP side effects can be assessed with MR. A standardized MR report detailing these structures could assist urologists in planning optimal and tailored surgical techniques, reducing complications, and improving patients' care.

Clinical Tools for Optimizing Therapeutic Decision-Making in Prostate Cancer: A Five-Year Retrospective Analysis

The effective staging of prostate cancer is essential for optimizing treatment and predicting outcomes. This study assessed the correlation between detailed preoperative diagnostic scores and postoperative outcomes to evaluate the accuracy of cancer restaging and its impact on treatment decisions and prognosis after prostatectomy. This retrospective study analyzed 133 prostate cancer patients who underwent prostatectomies at "Pius Brinzeu" Clinical Emergency Hospital in Timisoara over five years. Preoperative Gleason scores increased significantly across risk categories, from an average of 6.21 in low-risk patients to 7.57 in high-risk patients. This trend continued postoperatively, with scores rising from 7.04 to 8.33, respectively. The average increase in Gleason scores from preoperative to postoperative assessments was most pronounced in high-risk patients, at 0.76. Significant changes in clinical staging included increases in NCCN risk, where high-risk patients showed a 30% increase, and ISUP grade, with a 26.7% increase in the high-risk category. Notably, nodal status changes were also significant in high-risk patients, showing a 23.3% increase. The incidence of MRI-detected adenopathy was notably higher in the high-risk group (50%). Furthermore, there were significant correlations between the preoperative CAPRA score and postoperative ISUP grade (r = 0.261) and the preoperative PIRADS score and postoperative ISUP grade (r = 0.306). Similar observations were made between the preoperative and postoperative Gleason scores (r = 0.286) and the number of positive fragments (r = 0.227) with the postoperative ISUP grading. Furthermore, the preoperative CAPRA score was significantly correlated (r = 0.261) with the postoperative ISUP grading. Preoperative MRI findings, which included assessments of adenopathy and seminal vesicle invasion, were also significantly correlated (r = 0.218) with the postoperative pathological findings. Additionally, a significant correlation was found between the preoperative PIRADS score and postoperative ISUP grade (r = 0.306). In forecasting the aggressiveness and staging of prostate cancer following surgery, preoperative PSA levels showed an AUC of 0.631; the preoperative Gleason score had an AUC adjusted to 0.582, and the number of positive biopsy fragments indicated an AUC of 0.566. These results highlight the necessity of accurate and comprehensive preoperative assessments to better predict disease progression and refine treatment strategies.

Impact of Family History and Germline Genetic Risk Single Nucleotide Polymorphisms on Long-Term Outcomes of Favorable-Risk Prostate Cancer

PURPOSE

Family history and germline genetic risk single nucleotide polymorphisms (SNPs) have been separately shown to stratify lifetime risk of prostate cancer. Here, we evaluate the combined prognostic value of family history of prostate and other related cancers and germline risk SNPs among patients with favorable-risk prostate cancer.

MATERIALS AND METHODS

A total of 1367 participants from the prospective Health Professionals Follow-up Study diagnosed with low- or favorable intermediate-risk prostate cancer from 1986 to 2017 underwent genome-wide SNP genotyping. Multivariable Cox regression was used to estimate the association between family history, specific germline risk variants, and a 269 SNP polygenic risk score with prostate cancer‒specific death.

RESULTS

Family history of prostate, breast, and/or pancreatic cancer was observed in 489 (36%) participants. With median follow-up from diagnosis of 14.9 years, participants with favorable-risk prostate cancer with a positive family history had a significantly higher risk of prostate cancer‒specific death (HR 1.95, 95% CI 1.15-3.32, P = .014) compared to those without any family history. The rs2735839 (19q13) risk allele was associated with prostate cancer‒specific death (HR 1.81 per risk allele, 95% CI 1.04-3.17, P = .037), whereas the polygenic risk score was not. Combined family history and rs2735839 risk allele were each associated with an additive risk of prostate cancer‒specific death (HR 1.78 per risk factor, 95% CI 1.25-2.53, P = .001).

CONCLUSIONS

Family history of prostate, breast, or pancreatic cancer and/or a 19q13 germline risk allele are associated with an elevated risk of prostate cancer‒specific death among favorable-risk patients. These findings have implications for how family history and germline genetic risk SNPs should be factored into clinical decision-making around favorable-risk prostate cancer.

Model risk scores may underestimate rate of biochemical recurrence in African American men with localized prostate cancer: a cohort analysis of over 3000 men

INTRODUCTION

This study aims to determine if there is a difference in prostate cancer nomogram-adjusted risk of biochemical recurrence (BCR) and/or adverse pathology (AP) between African American (AAM) and Caucasian men (CM) undergoing radical prostatectomy (RP).

METHODS

A retrospective review was performed of men undergoing RP in the Pennsylvania Urologic Regional Collaborative between 2015 and 2021. Cox proportional hazard regression models were used to compare the rate of BCR after RP, and logistic regression models were used to compare rates of AP after RP between CM and AAM, adjusting for the CAPRA, CAPRA-S, and MSKCC pre- and post-operative nomogram scores.

RESULTS

Rates of BCR and AP after RP were analyzed from 3190 and 5029 men meeting inclusion criteria, respectively. The 2-year BCR-free survival was lower in AAM (72.5%) compared to CM (79.0%), with a hazard ratio (HR) of 1.38 (95% CI 1.16-1.63, p < 0.001). The rate of BCR was significantly greater in AAM compared to CM after adjustment for MSKCC pre-op (HR 1.29; 95% CI 1.08-1.53; p = 0.004), and post-op nomograms (HR 1.26; 95% CI 1.05-1.49; p < 0.001). There was a trend toward higher BCR rates among AAM after adjustment for CAPRA (HR 1.13; 95% CI 0.95-1.35; p = 0.17) and CAPRA-S nomograms (HR 1.11; 95% 0.93-1.32; p = 0.25), which did not reach statistical significance. The rate of AP was significantly greater in AAM compared to CM after adjusting for CAPRA (OR 1.28; 95% CI 1.10-1.50; p = 0.001) and MSKCC nomograms (OR 1.23; 95% CI 1.06-1.43; p = 0.007).

CONCLUSION

This analysis of a large multicenter cohort provides further evidence that AAM may have higher rates of BCR and AP after RP than is predicted by CAPRA and MSKCC nomograms. Accordingly, AAM may benefit with closer post-operative surveillance and may be more likely to require salvage therapies.

Prostate Cancer Risk Stratification by Digital Histopathology and Deep Learning

PURPOSE

Prostate cancer (PCa) represents a highly heterogeneous disease that requires tools to assess oncologic risk and guide patient management and treatment planning. Current models are based on various clinical and pathologic parameters including Gleason grading, which suffers from a high interobserver variability. In this study, we determine whether objective machine learning (ML)-driven histopathology image analysis would aid us in better risk stratification of PCa.

MATERIALS AND METHODS

We propose a deep learning, histopathology image-based risk stratification model that combines clinicopathologic data along with hematoxylin and eosin- and Ki-67-stained histopathology images. We train and test our model, using a five-fold cross-validation strategy, on a data set from 502 treatment-naïve PCa patients who underwent radical prostatectomy (RP) between 2000 and 2012.

RESULTS

We used the concordance index as a measure to evaluate the performance of various risk stratification models. Our risk stratification model on the basis of convolutional neural networks demonstrated superior performance compared with Gleason grading and the Cancer of the Prostate Risk Assessment Post-Surgical risk stratification models. Using our model, 3.9% of the low-risk patients were correctly reclassified to be high-risk and 21.3% of the high-risk patients were correctly reclassified as low-risk.

CONCLUSION

These findings highlight the importance of ML as an objective tool for histopathology image assessment and patient risk stratification. With further validation on large cohorts, the digital pathology risk classification we propose may be helpful in guiding administration of adjuvant therapy including radiotherapy after RP.

Refining Risk Stratification of High-risk and Locoregional Prostate Cancer: A Pooled Analysis of Randomized Trials

BACKGROUND AND OBJECTIVE

Radiotherapy (RT) and long-term androgen deprivation therapy (ltADT; 18-36 mo) is a standard of care in the treatment of high-risk localized/locoregional prostate cancer (HRLPC). We evaluated the outcomes in patients treated with RT + ltADT to identify which patients have poorer prognosis with standard therapy.

METHODS

Individual patient data from patients with HRLPC (as defined by any of the following three risk factors [RFs] in the context of cN0 disease-Gleason score ≥8, cT3-4, and prostate-specific antigen [PSA] >20 ng/ml, or cN1 disease) treated with RT and ltADT in randomized controlled trials collated by the Intermediate Clinical Endpoints in Cancer of the Prostate group. The outcome measures of interest were metastasis-free survival (MFS), overall survival (OS), time to metastasis, and prostate cancer-specific mortality. Multivariable Cox and Fine-Gray regression estimated hazard ratios (HRs) for the three RFs and cN1 disease.

KEY FINDINGS AND LIMITATIONS

A total of 3604 patients from ten trials were evaluated, with a median PSA value of 24 ng/ml. Gleason score ≥8 (MFS HR = 1.45; OS HR = 1.42), cN1 disease (MFS HR = 1.86; OS HR = 1.77), cT3-4 disease (MFS HR = 1.28; OS HR = 1.22), and PSA >20 ng/ml (MFS HR = 1.30; OS HR = 1.21) were associated with poorer outcomes. Adjusted 5-yr MFS rates were 83% and 78%, and 10-yr MFS rates were 63% and 53% for patients with one and two to three RFs, respectively; corresponding 10-yr adjusted OS rates were 67% and 60%, respectively. In cN1 patients, adjusted 5- and 10-yr MFS rates were 67% and 36%, respectively, and 10-yr OS was 47%.

CONCLUSIONS AND CLINICAL IMPLICATIONS

HRLPC patients with two to three RFs (and cN0) or cN1 disease had the poorest outcomes on RT and ltADT. This will help in counseling patients treated in routine practice and in guiding adjuvant trials in HRLPC.

PATIENT SUMMARY

Radiotherapy and long-term hormone therapy are standard treatments for high-risk and locoregional prostate cancer. In this report, we defined prognostic groups within high-risk/locoregional prostate cancer and showed that outcomes to standard therapy are poorest in those with two or more "high-risk" factors or evidence of lymph node involvement. Such patients may therefore be the best candidates for intensification of treatment.

Predictors of Metastasis in 68GA-Prostate Specific Membrane Antigen Pet-CT in the Primary Staging of Prostate Cancer

Background: The objective of this study was to investigate factors influencing Gallium 68 Prostate Specific Membrane Antigen Positron Emission Tomography (Ga68 PSMA PET-CT) uptake for primary staging in prostate cancer. Methods: Retrospective analysis was conducted on 499 non-metastatic and 243 de novo metastatic prostate cancer cases undergoing Ga68 PSMA PET-CT. Demographic, clinical, and imaging data were collected and analyzed. Multivariate logistic regression determined independent risk factors for metastasis detection on Ga68 PSMA PET-CT. Results: Metastatic cases showed higher levels of total PSA, PSA density (dPSA) and biopsy ISUP grade group compared to non-metastatic cases. Multivariate analysis identified cT2 stage and dPSA as independent predictors of metastasis detection on Ga68 PSMA PET-CT. Conclusions: Ga68 PSMA PET-CT plays a crucial role in prostate cancer staging, with identified factors such as clinical T stage and dPSA significantly impacting its diagnostic accuracy. These findings underscore the importance of Ga68 PSMA PET-CT in refining clinical staging and guiding treatment decisions for prostate cancer patients.

Shedding light on the shadows: oxidative stress and its pivotal role in prostate cancer progression

Objectives

Data on oxidative protein damage, total antioxidant capacity (TAC) and lipid peroxidation in progression of prostate cancer remain elusive. So far, the influence of the presence of perineural invasion on the level of oxidative stress has not been described. Additionally, there is limited data on the level of oxidative stress in patients' urine.

Methods

We compared the levels of oxidative stress markers in serum and urine in 50 patients with prostate cancer depending on the tumor stage and histological grade, the Gleason score, and the presence of perineural invasion.

Results

We found a significantly de-creased level of serum thiol groups and TAC in participants with prostate cancer. Similarly, serum Amadori products and malondialdehyde (MDA) were higher in patients than in healthy men. There was a significantly decrease in TAC and a significantly increased MDA in the urine of prostate cancer patients. As the stage of cancer increased, a decrease in the thiol group concentration and TAC as well as an increase in the concentration of lipid peroxidation products in the serum was observed. The serum level of advanced oxidation protein products (AOPP) increased in the group with Gleason scores greater than 7. Furthermore, serum thiol groups and TAC were reduced in the group with Gleason >7 as compared to Gleason <7. The presence of perineural invasion significantly reduced serum and urinary TAC and increased urinary AOPP concentration.

Conclusions

These results indicate a significant role for oxidative damage in prostate carcinogenesis and its progression. Characterizing oxidative and nitrosative damage to proteins may be useful in designing targeted therapies for prostate cancer patients.

A prospective cohort of men with localized prostate cancer on active surveillance protocol in Hong Kong, China: what did we learn?

This study aimed to report the outcomes of active surveillance (AS) in the management of low-risk prostate cancer (PCa). It recruited 87 men who were prospectively followed up according to the Prostate Cancer Research International Active Surveillance (PRIAS) protocol with local adaptation at SH Ho Urology Centre, Prince of Wales Hospital, Hong Kong, China. We investigated the predictors of disease progression and found that baseline prostate-specific antigen density (PSAD) and the presence of the highest Prostate Imaging-Reporting and Data System (PI-RADS) score 5 lesion on magnetic resonance imaging (MRI) are significantly correlated with disease progression. Moreover, men with PSAD >0.2 ng ml -2 or PI-RADS 4 or 5 lesions had significantly worse upgrading-free survival compared to those with PSAD ≤0.2 ng ml -2 and PI-RADS 2 or 3 lesions. The study concludes that AS is a safe and effective management strategy for selected patients to defer radical treatment and that most disease progression can be detected after the first repeated biopsy. The combination of PSAD >0.2 ng ml -2 and PI-RADS 4 or 5 lesions may serve as a useful predictor of early disease progression and provide a guide to optimize follow-up protocols for men in different risk groups.

An integrated radiology-pathology machine learning classifier for outcome prediction following radical prostatectomy: Preliminary findings

Objectives

To evaluate the added benefit of integrating features from pre-treatment MRI (radiomics) and digitized post-surgical pathology slides (pathomics) in prostate cancer (PCa) patients for prognosticating outcomes post radical-prostatectomy (RP) including a) rising prostate specific antigen (PSA), and b) extraprostatic-extension (EPE).

Methods

Multi-institutional data (N = 58) of PCa patients who underwent pre-treatment 3-T MRI prior to RP were included in this retrospective study. Radiomic and pathomic features were extracted from PCa regions on MRI and RP specimens delineated by expert clinicians. On training set (D1, N = 44), Cox Proportional-Hazards models MR, MP and MRaP were trained using radiomics, pathomics, and their combination, respectively, to prognosticate rising PSA (PSA > 0.03 ng/mL). Top features from MRaP were used to train a model to predict EPE on D1 and test on external dataset (D2, N = 14). C-index, Kalplan-Meier curves were used for survival analysis, and area under ROC (AUC) was used for EPE. MRaP was compared with the existing post-treatment risk-calculator, CAPRA (MC).

Results

Patients had median follow-up of 34 months. MRaP (c-index = 0.685 ± 0.05) significantly outperformed MR (c-index = 0.646 ± 0.05), MP (c-index = 0.631 ± 0.06) and MC (c-index = 0.601 ± 0.071) (p < 0.0001). Cross-validated Kaplan-Meier curves showed significant separation among risk groups for rising PSA for MRaP (p < 0.005, Hazard Ratio (HR) = 11.36) as compared to MR (p = 0.64, HR = 1.33), MP (p = 0.19, HR = 2.82) and MC (p = 0.10, HR = 3.05). Integrated radio-pathomic model MRaP (AUC = 0.80) outperformed MR (AUC = 0.57) and MP (AUC = 0.76) in predicting EPE on external-data (D2).

Conclusions

Results from this preliminary study suggest that a combination of radiomic and pathomic features can better predict post-surgical outcomes (rising PSA and EPE) compared to either of them individually as well as extant prognostic nomogram (CAPRA).

A Comparison of Globally Applied Prognostic Risk Groups and the Prevalence of Metastatic Disease on Prostate-specific Membrane Antigen Positron Emission Tomography in Patients with Newly Diagnosed Prostate Cancer

BACKGROUND

Various risk classification systems (RCSs) are used globally to stratify newly diagnosed patients with prostate cancer (PCa) into prognostic groups.

OBJECTIVE

To compare the predictive value of different prognostic subgroups (low-, intermediate-, and high-risk disease) within the RCSs for detecting metastatic disease on prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/computed tomography (CT) for primary staging, and to assess whether further subdivision of subgroups would be beneficial.

DESIGN, SETTING, AND PARTICIPANTS

Patients with newly diagnosed PCa, in whom PSMA-PET/CT was performed between 2017 and 2022, were studied retrospectively. Patients were stratified into risk groups based on four RCSs: European Association of Urology, National Comprehensive Cancer Network (NCCN), Cambridge Prognostic Group (CPG), and Cancer of the Prostate Risk Assessment.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The prevalence of metastatic disease on PSMA-PET/CT was compared among the subgroups within the four RCSs.

RESULTS AND LIMITATIONS

In total, 2630 men with newly diagnosed PCa were studied. Any metastatic disease was observed in 35% (931/2630) of patients. Among patients classified as having intermediate- and high-risk disease, the prevalence of metastases ranged from approximately 12% to 46%. Two RCSs further subdivided these groups. According to the NCCN, metastatic disease was observed in 5.8%, 13%, 22%, and 62% for favorable intermediate-, unfavorable intermediate-, high-, and very-high-risk PCa, respectively. Regarding the CPG, these values were 6.9%, 13%, 21%, and 60% for the corresponding risk groups.

CONCLUSIONS

This study underlines the importance of nuanced risk stratification, recommending the further subdivision of intermediate- and high-risk disease given the notable variation in the prevalence of metastatic disease. PSMA-PET/CT for primary staging should be reserved for patients with unfavorable intermediate- or higher-risk disease.

PATIENT SUMMARY

The use of various risk classification systems in patients with prostate cancer helps identify those at a higher risk of having metastatic disease on prostate-specific membrane antigen positron emission tomography/computed tomography for primary staging.

Differential outcomes of re-stratified high-risk prostate cancer patients treated with external beam radiation therapy plus high-dose-rate brachytherapy boost

Purpose

We report outcomes of high-risk prostate cancer (PCa) patients, initially classified according to a 3-tier NCCN classification system, treated with external beam radiation therapy (EBRT) and high-dose-rate brachytherapy boost (HDR-BT). Patients were analyzed based on a re-stratification of their risk grouping using CAPRA score and a newer 5-tier NCCN classification.

Material and methods

471 high-risk PCa patients treated with EBRT, HDR-BT, and androgen deprivation therapy (ADT) between 1999 and 2018 were included. Competing risk survival analyses to compare individuals with CAPRA scores < 6 vs. ≥ 6 for biochemical relapse (BCR) and metastasis incidence were conducted. Also, overall survival (OS) for both groups using Kaplan-Meier analysis was assessed. The same analyses were repeated using a 5-tier NCCN stratification comparing those classified as high-risk vs. very high-risk patients.

Results

The median age was 71 years, and the median follow-up period was 72 months. The whole cohort received an EQD2 of 74 Gy or greater, with a median EQD2 of 106.89 Gy. Both a CAPRA score ≥ 6 and belonging to the NCCN very high-risk group were associated with BCR, with subdistribution hazard ratios (sHRs) of 3.04 (p = 0.015) and 2.53 (p = 0.013), respectively. For metastasis incidence, both the CAPRA and NCCN groups had similar sHRs of 2.60 (p = 0.094) and 2.71 (p = 0.037), respectively. For 10-year OS, patients with CAPRA score ≥ 6 and belonging to the NCCN very high-risk group presented similar HRs of 2.11 (p = 0.005) and 2.10 (p = 0.002).

Conclusions

We showed that high-risk PCa patients classified according to the 3-tier NCCN system benefit from further stratification using the CAPRA score or the 5-tier NCCN stratification method. Patients with a CAPRA score ≥ 6 or classified as very high-risk demonstrate a higher hazard of BCR, metastasis, and death. These patients might benefit from further intensification of their investigations and treatment, based on ongoing research.

Biomarkers for Pre-Treatment Risk Stratification of Prostate Cancer Patients: A Systematic Review

BACKGROUND

Prostate cancer (PCa) is one of the most frequently occurring malignancies. Although most cases are not life-threatening, approximately 20% endure an unfavorable outcome. PSA-based screening reduced mortality but at the cost of an increased overdiagnosis/overtreatment of low-risk (lrPCa) and favorable intermediate-risk (firPCa) PCa. PCa risk-groups are usually identified based on serum Prostate-Specific Antigen (PSA), the Gleason score, and clinical T stage, which have consistent although variable specificity or subjectivity. Thus, more effective and specific tools for risk assessment are needed, ideally making use of minimally invasive methods such as liquid biopsies. In this systematic review we assessed the clinical potential and analytical performance of liquid biopsy-based biomarkers for pre-treatment risk stratification of PCa patients.

METHODS

Studies that assessed PCa pre-treatment risk were retrieved from PubMed, Scopus, and MedLine. PCa risk biomarkers were analyzed, and the studies' quality was assessed using the QUADAS-2 tool.

RESULTS

The final analysis comprised 24 full-text articles, in which case-control studies predominated, mostly reporting urine-based biomarkers (54.2%) and biomarker quantification by qPCR (41.7%). Categorization into risk groups was heterogeneous, predominantly making use of the Gleason score.

CONCLUSION

This systematic review unveils the substantial clinical promise of using circulating biomarkers in assessing the risk for prostate cancer patients. However, the standardization of groups, categories, and biomarker validation are mandatory before this technique can be implemented. Circulating biomarkers might represent a viable alternative to currently available tools, obviating the need for tissue biopsies, and allowing for faster and more cost-effective testing, with superior analytical performance, specificity, and reproducibility.

Prostate cancer risk stratification via eNose urine odor analysis: a preliminary report

Introduction

Prostate cancer (PCa) is known for its highly diverse clinical behavior, ranging from low-risk, slow-growing tumors to aggressive and life-threatening forms. To avoid over-treatment of low-risk PCa patients, it would be very important prior to any therapeutic intervention to appropriately classify subjects based on tumor aggressiveness. Unfortunately, there is currently no reliable test available for this purpose. The aim of the present study was to evaluate the ability of risk stratification of PCa subjects using an electronic nose (eNose) detecting PCa-specific volatile organic compounds (VOCs) in urine samples.

Methods

The study involved 120 participants who underwent diagnostic prostate biopsy followed by robot assisted radical prostatectomy (RARP). PCa risk was categorized as low, intermediate, or high based on the D'Amico risk classification and the pathological grade (PG) assessed after RARP. The eNose's ability to categorize subjects for PCa risk stratification was evaluated based on accuracy and recall metrics.

Results

The study population comprised 120 participants. When comparing eNose predictions with PG an accuracy of 79.2% (95%CI 70.8 - 86%) was found, while an accuracy of 74.2% (95%CI 65.4 - 81.7%) was found when compared to D'Amico risk classification system. Additionally, if compared low- versus -intermediate-/high-risk PCa, the eNose achieved an accuracy of 87.5% (95%CI 80.2-92.8%) based on PG or 90.8% (95%CI 84.2-95.3%) based on D'Amico risk classification. However, when using low-/-intermediate versus -high-risk PCa for PG, the accuracy was found to be 91.7% (95%CI 85.2-95.9%). Finally, an accuracy of 80.8% (95%CI72.6-87.4%) was found when compared with D'Amico risk classification.

Discussion

The findings of this study indicate that eNose may represent a valid alternative not only for early and non-invasive diagnosis of PCa, but also to categorize patients based on tumor aggressiveness. Further studies including a wider sample population will be necessary to confirm the potential clinical impact of this new technology.

Growth Patterns of Prostate Cancers Undetected by Prostate 3T Multiparametric MRI

BACKGROUND/AIM

The multiparametric magnetic resonance imaging (mpMRI)-ultrasound (US) fusion targeted biopsy (TB) is a useful diagnostic device for men with suspected prostate cancer (PC) and can increase the detection rate for clinically significant PCs (csPC). However, few studies have shown pathological findings of undetectable csPCs on the prostate mpMRI.

PATIENTS AND METHODS

This study investigated the growth patterns of csPC undetected in prostate mpMRI. The study enrolled 248 patients with suspected PCs and ≥PI-RADS 2 lesions, who then underwent mpMRI-US fusion TB and nearly prostate-mapping systematic biopsies (SB). A total 248 biopsies included 404 regions of interest in TB and 2976 mapping-regions in SB.

RESULTS

The detection rates of csPC, defined as PC grade group (GG) ≥2, were 42% in TB and 44% in SB, and the highest detection rate was 50%, using both TB and SB. Approximately 79% of PI-RADS 3/4/5 with any PC showed csPC. A total 201 PI-RADS 3/4/5 lesions showed benign prostatic hyperplasia, lymphocytic prostatitis, or fibromuscular stroma only in the core tissues. Notably, 22 csPCs detected in SB but undetected in prostate mpMRI preferentially showed a pattern of mixed well-formed and fused PC glands. The other patterns including cribriform glands and poorly formed glands with intracytoplasmic vacuoles were also seen. Approximately 85% of the 22 csPCs showed tumor volume less than 50% of core tissues.

CONCLUSION

Changes in prostatic stroma amounts, inflammation severity, tumor volume and growth patterns of PC glands affected the detectability of prostate mpMRI.

Large cribriform glands (> 0.25 mm diameter) as a predictor of adverse pathology in men with Grade Group 2 prostate cancer

AIMS

A recent outcome-based, radical prostatectomy study defined > 0.25 mm diameter to distinguish large versus small cribriform glands, with > 0.25 mm associated with worse recurrence-free survival. This study evaluates whether identification of > 0.25 mm cribriform glands in Grade Group 2 patients at biopsy is associated with adverse pathology at radical prostatectomy.

METHODS AND RESULTS

Tumours containing biopsy slides for 133 patients with Grade Group 2 prostate cancer with subsequent radical prostatectomy were re-reviewed for large cribriform glands (diameter > 0.25 mm). The primary outcome was adverse pathology (Grade Groups 3-5; stage pT3a or greater, or pN1). The secondary outcome was recurrence-free survival. Cribriform pattern was present in 52 of 133 (39%) patients; of these, 16 of 52 (31%) had large cribriform glands and 36 of 52 (69%) had only small cribriform glands. Patients with large cribriform glands had significantly more adverse pathology at radical prostatectomy compared to patients with small cribriform glands and no cribriform glands (large = 11 of 16, 69%; small = 12 of 36, 33%; no cribriform = 25 of 81, 31%; χ2 P-value 0.01). On multivariate analysis, large cribriform glands were also associated with adverse pathology, independent of age, prostate-specific antigen (PSA)/PSA density at diagnosis, year of diagnosis and biopsy cores percentage positive (global P-value 0.02). Large cribriform glands were also associated with increased CAPRA-S surgical risk score (Kruskal-Wallis P-value 0.02).

CONCLUSIONS

Large cribriform glands using a diameter > 0.25 mm definition in Grade Group 2 patients on biopsy are associated with increased risk of adverse pathology at radical prostatectomy. The presence of large cribriform histology should be considered when offering active surveillance for those with Grade Group 2 disease.

Prostate cancer nomograms and their application in Asian men: a review

Nomograms help to predict outcomes in individual patients rather than whole populations and are an important part of evaluation and treatment decision making. Various nomograms have been developed in malignancies to predict and prognosticate clinical outcomes such as severity of disease, overall survival, and recurrence-free survival. In prostate cancer, nomograms were developed for determining need for biopsy, disease course, need for adjuvant therapy, and outcomes. Most of these predictive nomograms were based on Caucasian populations. Prostate cancer is significantly affected by race, and Asian men have a significantly different racial and genetic susceptibility compared to Caucasians, raising the concern in generalizability of these nomograms. We reviewed the existing literature for nomograms in prostate cancer and their application in Asian men. There are very few studies that have evaluated the applicability and validity of the existing nomograms in these men. Most have found significant differences in the performance in this population. Thus, more studies evaluating the existing nomograms in Asian men or suggesting modifications for this population are required.

Deep learning-based radiomics model from pretreatment ADC to predict biochemical recurrence in advanced prostate cancer

Purpose

To develop deep-learning radiomics model for predicting biochemical recurrence (BCR) of advanced prostate cancer (PCa) based on pretreatment apparent diffusion coefficient (ADC) maps.

Methods

Data were collected retrospectively from 131 patients diagnosed with advanced PCa, randomly divided into training (n = 93) and test (n = 38) datasets. Pre-treatment ADC images were segmented using a pre-trained artificial intelligence (AI) model to identify suspicious PCa areas. Three models were constructed, including a clinical model, a conventional radiomics model and a deep-radiomics model. The receiver operating characteristic (ROC), precision-recall (PR) curve and decision curve analysis (DCA) were used to assess predictive performance in test dataset. The net reclassification index (NRI) and integrated discrimination improvement (IDI) were employed to compare the performance enhancement of the deep-radiomics model in relation to the other two models.

Results

The deep-radiomics model exhibited a significantly higher area under the curve (AUC) of ROC than the other two (P = 0.033, 0.026), as well as PR curve (AUC difference 0.420, 0.432). The DCA curve demonstrated superior performance for the deep-radiomics model across all risk thresholds than the other two. Taking the clinical model as reference, the NRI and IDI was 0.508 and 0.679 for the deep-radiomics model with significant difference. Compared with the conventional radiomics model, the NRI and IDI was 0.149 and 0.164 for the deep-radiomics model without significant difference.

Conclusion

The deep-radiomics model exhibits promising potential in predicting BCR in advanced PCa, compared to both the clinical model and the conventional radiomics model.

The Current Landscape of Prostate-Specific Membrane Antigen (PSMA) Imaging Biomarkers for Aggressive Prostate Cancer

The review examines the vital role of prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) in the diagnosis, staging, and treatment of prostate cancer (PCa). It focuses on the superior diagnostic abilities of PSMA PET/CT for identifying both nodal and distant PCa, and its potential as a prognostic indicator for biochemical recurrence and overall survival. Additionally, we focused on the variability of PSMA's expression and its impact on personalised treatment, particularly the use of [177Lu] Lu-PSMA-617 radioligand therapy. This review emphasises the essential role of PSMA PET/CT in enhancing treatment approaches, improving patient outcomes, and reducing unnecessary interventions, positioning it as a key element in personalised PCa management.

MRI characteristics and oncological follow-up of patients with ISUP grade group 4 or 5 prostate cancer

OBJECTIVES

To analyze multiparametric MRI (mpMRI) characteristics of patients with International Society of Urological Pathology (ISUP) grade group (GG) 4 or 5 prostate cancer (PC) and to correlate MRI parameters with the occurrence of biochemical recurrence (BCR) after radical prostatectomy (RPE).

METHODS

In this single-center cohort study consecutive patients with mpMRI and ISUP GG 4 or 5 PC were retrospectively analyzed. Clinical, MR-guided biopsy, and diagnostic mpMRI parameter were assessed. A subcohort of patients with RPE and follow-up was analyzed separately. A univariate and multivariate analyses were performed to determine parameters that are associated to patients with BCR after RPE.

RESULTS

145 patients (mean age 70y, median PSA 10.9 ng/ml) were analyzed. 99% had a PI-RADS classification of 4 or 5, 48% revealed MRI T3 stage, and median diameter of the MRI index lesion (IL) was 15 mm. IL showed a median ADC value of 668 ×10-6 mm2/s and exhibited contrast enhancement in 94% of the cases. For patients with follow-up after RPE (n = 82; mean follow-up time 68 ± 27 m), MRI parameters were significantly different for contact length of the IL to the pseudocapsule (LCC), MRI T3 stage, and IL localization (p < 0.05). Higher PSAD and MRI T3 stage were independent parameters for the risk of BCR when incorporating clinical, biopsy, and MRI parameters.

CONCLUSION

ISUP GG 4 or 5 PC has distinctive characteristics on mpMRI and were detected on MRI in all cases. In addition, higher PSAD and MRI T3 stage were significant predictors for BCR after RPE.

A matched-analysis on short-term and long-term (up to 5 years of follow-up) urinary incontinence outcomes after robot-assisted radical prostatectomy with and without anterior and posterior reconstruction: data on 1358 patients

PURPOSE

We report a comparative monocentric study with a short and long-term follow-up with the aim to assess differences about urinary continence outcomes in patients treated with Robot-Assisted Radical Prostatectomy (RARP) with two different techniques: with anterior and posterior reconstruction and without any kind of reconstruction.

MATERIALS AND METHODS

From January 2016 to September 2021, at the Department of Urology of the "F. Miulli" Hospital of Acquaviva delle Fonti, in Italy, 850 eligible patients underwent extraperitoneal RARP with anterior and posterior reconstruction and 508 without reconstructions.

RESULTS

In patients undergoing RARP with reconstructions 1 month after surgery the urinary continence was preserved in 287/850 patients (33.8%), 3 months after surgery in 688/850 (81%), 6 months in 721/850 (84.8%), 12 months in 734/850 (86.3%), 18 months in 671/754 (89%), 24 months in 696/754 (92.3%), 36 months in 596/662 (90%), 48 months in 394/421 (93.6%), 60 months in 207/212 (97.6%). In patients undergoing RARP without reconstruction 1 month after surgery urinary continence was preserved in 99/508 (19.4%), after 3 months in 276/508 (54.3%), 6 months in 305/508 (60%), 12 months in 329/508 (64.7%), 18 months in 300/456 (65.7%), 24 months in 295/456 (64.7%), 36 months in 268/371 (72.3%), 48 months in 181/224 (81%), 60 months in 93/103 (90.3%).

CONCLUSION

In our case study, the RARP with anterior and posterior reconstruction technique is associated with a statistically significant higher rate (up to 48 months of follow-up) and a faster recovery of urinary continence compared to the technique without reconstructions.

Presurgical 68Ga-PSMA-11 Positron Emission Tomography for Biochemical Recurrence Risk Assessment: A Follow-up Analysis of a Multicenter Prospective Phase 3 Imaging Trial

BACKGROUND

In the initial staging of patients with high-risk prostate cancer (PCa), prostate-specific membrane antigen positron emission tomography (PSMA-PET) has been established as a front-line imaging modality. The increasing number of PSMA-PET scans performed in the primary staging setting might be associated with decreases in biochemical recurrence (BCR)-free survival (BCR-FS).

OBJECTIVE

To assess the added prognostic value of presurgical PSMA-PET for BCR-FS compared with the presurgical Cancer of the Prostate Risk Assessment (CAPRA) and postsurgical CAPRA-Surgery (CAPRA-S) scores in patients with intermediate- to high-risk PCa treated with radical prostatectomy (RP) and pelvic lymph node dissection.

DESIGN, SETTING, AND PARTICIPANTS

This is a follow-up study of the surgical cohort evaluated in the multicenter prospective phase 3 imaging trial (n = 277; NCT03368547, NCT02611882, and NCT02919111).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Each 68Ga-PSMA-11-PET scan was read by three blinded independent readers. PSMA-PET prostate uptake (low vs high), PSMA-PET extraprostatic disease (N1/M1), and CAPRA and CAPRA-S scores were used to assess the risk of BCR. Patients were followed after RP by local investigators using electronic medical records. BCR was defined by a prostate-specific antigen (PSA) level increasing to ≥0.2 ng/ml after RP or initiation of PCa-specific secondary treatment (>6 mo after surgery). Univariate and multivariable Cox models, and c-statistic index were performed to assess the prognostic value of PSMA-PET and for a comparison with the CAPRA and CAPRA-S scores.

RESULTS AND LIMITATIONS

From December 2015 to December 2019, 277 patients underwent surgery after PSMA-PET. Clinical follow-up was obtained in 240/277 (87%) patients. The median follow-up after surgery was 32.4 (interquartile range 23.3-42.9) mo. Of 240 BCR events, 91 (38%) were observed. PSMA-PET N1/M1 was found in 41/240 (17%) patients. PSMA-PET prostate uptake, PSMA-PET N1/M1, and CAPRA and CAPRA-S scores were significant univariate predictors of BCR. The addition of PSMA-PET N1/M1 status to the presurgical CAPRA score improved the risk assessment for BCR significantly in comparison with the presurgical CAPRA score alone (c-statistic 0.70 [0.64-0.75] vs 0.63 [0.57-0.69]; p < 0.001). The C-index of the postsurgical model utilizing the postsurgical CAPRA-S score alone was not significantly different from the presurgical model combining the presurgical CAPRA score and PSMA-PET N1/M1 status (p = 0.19).

CONCLUSIONS

Presurgical PSMA-PET was a strong prognostic biomarker improving BCR-FS risk assessment. Its implementation in the presurgical risk assessment with the CAPRA score improved the performance and reduced the difference with the reference standard (postsurgical CAPRA-S score).

PATIENT SUMMARY

The use prostate-specific membrane antigen positron emission tomography improved the assessment of biochemical recurrence risk in patients with intermediate- and high-risk prostate cancer who were treated with radical prostatectomy and pelvic lymph node dissection.

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.

Quantitative Histomorphometric Features of Prostate Cancer Predict Patients Who Biochemically Recur Following Prostatectomy

Prostate cancer is the most commonly diagnosed cancer in men, accounting for 27% of the new male cancer diagnoses in 2022. If organ-confined, removal of the prostate through radical prostatectomy is considered curative; however, distant metastases may occur, resulting in a poor patient prognosis. This study sought to determine whether quantitative pathomic features of prostate cancer differ in patients who biochemically experience biological recurrence after surgery. Whole-mount prostate histology from 78 patients was analyzed for this study. In total, 614 slides were hematoxylin and eosin stained and digitized to produce whole slide images (WSI). Regions of differing Gleason patterns were digitally annotated by a genitourinary fellowship-trained pathologist, and high-resolution tiles were extracted from each annotated region of interest for further analysis. Individual glands within the prostate were identified using automated image processing algorithms, and histomorphometric features were calculated on a per-tile basis and across WSI and averaged by patients. Tiles were organized into cancer and benign tissues. Logistic regression models were fit to assess the predictive value of the calculated pathomic features across tile groups and WSI; additionally, models using clinical information were used for comparisons. Logistic regression classified each pathomic feature model at accuracies >80% with areas under the curve of 0.82, 0.76, 0.75, and 0.72 for all tiles, cancer only, noncancer only, and across WSI. This was comparable with standard clinical information, Gleason Grade Groups, and CAPRA score, which achieved similar accuracies but areas under the curve of 0.80, 0.77, and 0.70, respectively. This study demonstrates that the use of quantitative pathomic features calculated from digital histology of prostate cancer may provide clinicians with additional information beyond the traditional qualitative pathologist assessment. Further research is warranted to determine possible inclusion in treatment guidance.

Development, Validation, and Clinical Utility of a Six-gene Signature to Predict Aggressive Prostate Cancer

BACKGROUND

Molecular signatures in prostate cancer (PCa) tissue can provide useful prognostic information to improve the understanding of a patient's risk of harbouring aggressive disease.

OBJECTIVE

To develop and validate a gene signature that adds independent prognostic information to clinical parameters for better treatment decisions and patient management.

DESIGN, SETTING, AND PARTICIPANTS

Expression of 14 genes was evaluated in radical prostatectomy (RP) tissue from an Irish cohort of PCa patients (n = 426). A six-gene molecular risk score (MRS) was identified with strong prognostic performance to predict adverse pathology (AP) at RP or biochemical recurrence (BCR). The MRS was combined with the Cancer of the Prostate Risk Assessment (CAPRA) score, to create a molecular and clinical risk score (MCRS), and validated in a Swedish cohort (n = 203).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The primary AP outcome was assessed by the likelihood ratio statistics and area under the receiver operating characteristics curves (AUC) from logistic regression models. The secondary time to BCR outcome was assessed by likelihood ratio statistics and C-indexes from Cox proportional hazard regression models.

RESULTS AND LIMITATIONS

The six-gene signature was significantly (p < 0.0001) prognostic and added significant prognostic value to clinicopathological features for AP and BCR outcomes. For both outcomes, both the MRS and the MCRS increased the AUC/C-index when added to European Association of Urology (EAU) and CAPRA scores. Limitations include the retrospective nature of this study.

CONCLUSIONS

The six-gene signature has strong performance for the prediction of AP and BCR in an independent clinical validation study. MCRS improves prognostic evaluation and can optimise patient management after RP.

PATIENT SUMMARY

We found that the expression panel of six genes can help predict whether a patient is likely to have a disease recurrence after radical prostatectomy surgery.

Biopsy-free AI-aided precision MRI assessment in prediction of prostate cancer biochemical recurrence

BACKGROUND

To investigate the predictive ability of high-throughput MRI with deep survival networks for biochemical recurrence (BCR) of prostate cancer (PCa) after prostatectomy.

METHODS

Clinical-MRI and histopathologic data of 579 (train/test, 463/116) PCa patients were retrospectively collected. The deep survival network (iBCR-Net) is based on stepwise processing operations, which first built an MRI radiomics signature (RadS) for BCR, and predicted the T3 stage and lymph node metastasis (LN+) of tumour using two predefined AI models. Subsequently, clinical, imaging and histopathological variables were integrated into iBCR-Net for BCR prediction.

RESULTS

RadS, derived from 2554 MRI features, was identified as an independent predictor of BCR. Two predefined AI models achieved an accuracy of 82.6% and 78.4% in staging T3 and LN+. The iBCR-Net, when expressed as a presurgical model by integrating RadS, AI-diagnosed T3 stage and PSA, can match a state-of-the-art histopathological model (C-index, 0.81 to 0.83 vs 0.79 to 0.81, p > 0.05); and has maximally 5.16-fold, 12.8-fold, and 2.09-fold (p < 0.05) benefit to conventional D'Amico score, the Cancer of the Prostate Risk Assessment (CAPRA) score and the CAPRA Postsurgical score.

CONCLUSIONS

AI-aided iBCR-Net using high-throughput MRI can predict PCa BCR accurately and thus may provide an alternative to the conventional method for PCa risk stratification.

Prostate-derived circulating microRNAs add prognostic value to prostate cancer risk calculators

Prostate cancer is the second leading cause of malignancy-related deaths among American men. Active surveillance is a safe option for many men with less aggressive disease, yet definitively determining low-risk cancer is challenging with biopsy alone. Herein, we sought to identify prostate-derived microRNAs in patient sera and serum extracellular vesicles, and determine if those microRNAs improve upon the current clinical risk calculators for prostate cancer prognosis before and after biopsy. Prostate-derived intracellular and extracellular vesicle-contained microRNAs were identified by small RNA sequencing of prostate cancer patient explants and primary cells. Abundant microRNAs were included in a custom microRNA PCR panel that was queried in whole serum and serum extracellular vesicles from a diverse cohort of men diagnosed with prostate cancer. The levels of these circulating microRNAs significantly differed between indolent and aggressive disease and improved the area under the curve for pretreatment nomograms of prostate cancer disease risk. The microRNAs within the extracellular vesicles were the most informative and improved the AUC to 0.739 compared to the existing nomogram alone, which has an AUC of 0.561. The microRNAs in the whole serum improved it to AUC 0.675. In summary, quantifying microRNAs circulating in extracellular vesicles is a clinically feasible assay that may provide additional information for assessing prostate cancer risk stratification.

Multiparametric MRI-based 5-year Risk Prediction Model for Biochemical Recurrence of Prostate Cancer after Radical Prostatectomy

Background Current predictive tools to estimate the risk of biochemical recurrence (BCR) after treatment of prostate cancer do not consider multiparametric MRI (mpMRI) information. Purpose To develop a risk prediction tool that considers mpMRI findings to assess the risk of 5-year BCR after radical prostatectomy. Materials and Methods In this retrospective single-center analysis in 1459 patients with prostate cancer who underwent mpMRI before radical prostatectomy (in 2012-2015), the outcome of interest was 5-year BCR (two consecutive prostate-specific antigen [PSA] levels > 0.2 ng/mL [0.2 µg/L]). Patients were randomly divided into training (70%) and test (30%) sets. Kaplan-Meier plots were applied to the training set to estimate survival probabilities. Multivariable Cox regression models were used to test the relationship between BCR and different sets of exploratory variables. The C-index of the final model was calculated for the training and test sets and was compared with European Association of Urology, University of California San Francisco Cancer of the Prostate Risk Assessment, Memorial Sloan-Kettering Cancer Center, and Partin risk tools using the partial likelihood ratio test. Five risk categories were created. Results The median duration of follow-up in the whole cohort was 59 months (IQR, 32-81 months); 376 of 1459 (25.8%) patients had BCR. A multivariable Cox regression model (referred to as PIPEN, and composed of PSA density, International Society of Urological Pathology grade group, Prostate Imaging Reporting and Data System category, European Society of Urogenital Radiology extraprostatic extension score, nodes) fitted to the training data yielded a C-index of 0.74, superior to that of other predictive tools (C-index 0.70 for all models; P ≤ .01) and a median higher C-index on 500 test set replications (C-index, 0.73). Five PIPEN risk categories were identified with 5-year BCR-free survival rates of 92%, 84%, 71%, 56%, and 26% in very low-, low-, intermediate-, high-, and very high-risk patients, respectively (all P < .001). Conclusion A five-item model for predicting the risk of 5-year BCR after radical prostatectomy for prostate cancer was developed and internally verified, and five risk categories were identified. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Aguirre and Ortegón in this issue.

Cohort profile: the Turin prostate cancer prognostication (TPCP) cohort

Introduction

Prostate cancer (PCa) is the most frequent tumor among men in Europe and has both indolent and aggressive forms. There are several treatment options, the choice of which depends on multiple factors. To further improve current prognostication models, we established the Turin Prostate Cancer Prognostication (TPCP) cohort, an Italian retrospective biopsy cohort of patients with PCa and long-term follow-up. This work presents this new cohort with its main characteristics and the distributions of some of its core variables, along with its potential contributions to PCa research.

Methods

The TPCP cohort includes consecutive non-metastatic patients with first positive biopsy for PCa performed between 2008 and 2013 at the main hospital in Turin, Italy. The follow-up ended on December 31st 2021. The primary outcome is the occurrence of metastasis; death from PCa and overall mortality are the secondary outcomes. In addition to numerous clinical variables, the study's prognostic variables include histopathologic information assigned by a centralized uropathology review using a digital pathology software system specialized for the study of PCa, tumor DNA methylation in candidate genes, and features extracted from digitized slide images via Deep Neural Networks.

Results

The cohort includes 891 patients followed-up for a median time of 10 years. During this period, 97 patients had progression to metastatic disease and 301 died; of these, 56 died from PCa. In total, 65.3% of the cohort has a Gleason score less than or equal to 3 + 4, and 44.5% has a clinical stage cT1. Consistent with previous studies, age and clinical stage at diagnosis are important prognostic factors: the crude cumulative incidence of metastatic disease during the 14-years of follow-up increases from 9.1% among patients younger than 64 to 16.2% for patients in the age group of 75-84, and from 6.1% for cT1 stage to 27.9% in cT3 stage.

Discussion

This study stands to be an important resource for updating existing prognostic models for PCa on an Italian cohort. In addition, the integrated collection of multi-modal data will allow development and/or validation of new models including new histopathological, digital, and molecular markers, with the goal of better directing clinical decisions to manage patients with PCa.

Grossing and reporting of radical prostatectomy specimens: An evidence-based approach

Radical prostatectomy (RP) constitutes the primary treatment option for patients with clinically localized, biopsy-proven prostate cancer that requires local treatment with curative intent. Accurate reporting of radical prostatectomy specimens is required to guide further risk stratification and management of patients. Hence, for the handling and reporting of RP specimens, a standardized protocol should be followed. Many general pathologists may not be well-versed with the guidelines for the handling of radical prostatectomy specimens. This article discusses a detailed approach to grossing techniques, including specimen description, fixation requirements, gross cut-up, and reporting of the grade and stage of RP specimens. This will enable the pathologist to aid in multidisciplinary management.

Long-term complications and health-related quality of life outcomes after radical prostatectomy with or without subsequent radiation treatment for prostate cancer

BACKGROUND

To report objective long-term complications and health related quality of life (HRQOL) outcomes after radical prostatectomy (RP) with and without radiation therapy (RT) for prostate cancer (CaP).

METHODS

We analyzed patients diagnosed with CaP who underwent RP from the UCSF Cancer of the Prostate Strategic Urologic Research Endeavor (CaPSURE) registry between 1995 and 2020. Cox proportional hazards were used to assess risk of postoperative complications which included cystitis, gastrointestinal (GI) toxicity, incontinence requiring a surgical procedure, ureteral injury and urinary stricture. Repeated measures mixed models were used to assess the effects of radiation and complications on patient-reported urinary, bowel, and sexual function after surgery.

RESULTS

Of 6,258 men who underwent RP, cumulative incidence of EBRT was 9.1% at 5 years after surgery. Patients who received postoperative radiation were at increased risk for onset of cystitis (HR 5.60, 95% CI 3.40-9.22, P < 0.01). Receipt of RT was not associated with other complications. In repeated measures analysis, postoperative RT was associated with worsening general health scores, adjusting for complications of incontinence, urinary stricture, GI toxicity or ureteral injury, independent of whether patients had those complications.

CONCLUSIONS

RT after RP was associated with an increase in the risk of cystitis and worse general health in the long term. Other complications and HRQOL outcomes did not demonstrate differences by whether patients had RT or not. While post-operative RT is the only curative option for CaP after RP, patients and providers should be aware of the increased risks when making treatment decisions.

Role of radiomic analysis of [18F]fluoromethylcholine PET/CT in predicting biochemical recurrence in a cohort of intermediate and high risk prostate cancer patients at initial staging

AIM

To study the feasibility of radiomic analysis of baseline [18F]fluoromethylcholine positron emission tomography/computed tomography (PET/CT) for the prediction of biochemical recurrence (BCR) in a cohort of intermediate and high-risk prostate cancer (PCa) patients.

MATERIAL AND METHODS

Seventy-four patients were prospectively collected. We analyzed three prostate gland (PG) segmentations (i.e., PGwhole: whole PG; PG41%: prostate having standardized uptake value - SUV > 0.41*SUVmax; PG2.5: prostate having SUV > 2.5) together with three SUV discretization steps (i.e., 0.2, 0.4, and 0.6). For each segmentation/discretization step, we trained a logistic regression model to predict BCR using radiomic and/or clinical features.

RESULTS

The median baseline prostate-specific antigen was 11 ng/mL, the Gleason score was > 7 for 54% of patients, and the clinical stage was T1/T2 for 89% and T3 for 9% of patients. The baseline clinical model achieved an area under the receiver operating characteristic curve (AUC) of 0.73. Performances improved when clinical data were combined with radiomic features, in particular for PG2.5 and 0.4 discretization, for which the median test AUC was 0.78.

CONCLUSION

Radiomics reinforces clinical parameters in predicting BCR in intermediate and high-risk PCa patients. These first data strongly encourage further investigations on the use of radiomic analysis to identify patients at risk of BCR.

CLINICAL RELEVANCE STATEMENT

The application of AI combined with radiomic analysis of [18F]fluoromethylcholine PET/CT images has proven to be a promising tool to stratify patients with intermediate or high-risk PCa in order to predict biochemical recurrence and tailor the best treatment options.

KEY POINTS

• Stratification of patients with intermediate and high-risk prostate cancer at risk of biochemical recurrence before initial treatment would help determine the optimal curative strategy. • Artificial intelligence combined with radiomic analysis of [18F]fluorocholine PET/CT images allows prediction of biochemical recurrence, especially when radiomic features are complemented with patients' clinical information (highest median AUC of 0.78). • Radiomics reinforces the information of conventional clinical parameters (i.e., Gleason score and initial prostate-specific antigen level) in predicting biochemical recurrence.

Emerging Role of Nuclear Medicine in Prostate Cancer: Current State and Future Perspectives

Prostate cancer is the most frequent epithelial neoplasia after skin cancer in men starting from 50 years and prostate-specific antigen (PSA) dosage can be used as an early screening tool. Prostate cancer imaging includes several radiological modalities, ranging from ultrasonography, computed tomography (CT), and magnetic resonance to nuclear medicine hybrid techniques such as single-photon emission computed tomography (SPECT)/CT and positron emission tomography (PET)/CT. Innovation in radiopharmaceutical compounds has introduced specific tracers with diagnostic and therapeutic indications, opening the horizons to targeted and very effective clinical care for patients with prostate cancer. The aim of the present review is to illustrate the current knowledge and future perspectives of nuclear medicine, including stand-alone diagnostic techniques and theragnostic approaches, in the clinical management of patients with prostate cancer from initial staging to advanced disease.

Ten-year work burden after prostate cancer treatment

INTRODUCTION

We aim to characterize the magnitude of the work burden (weeks off from work) associated with prostate cancer (PCa) treatment over a 10-year period after PCa diagnosis and identify those at greatest risk.

MATERIALS AND METHODS

We identified men diagnosed with PCa treated with radical prostatectomy, radiation therapy, or active surveillance/watchful waiting within CaPSURE. Patients self-reported work burden and SF36 general health scores via surveys before and 1,3,5, and 10 years after treatment. Using multivariate repeated measures generalized estimating equation modeling we examined the association between primary treatment with risk of any work weeks lost due to care.

RESULTS

In total, 6693 men were included. The majority were White (81%, 5% Black, and 14% Other) with CAPRA low- (60%) or intermediate-risk (32%) disease and underwent surgery (62%) compared to 29% radiation and 9% active surveillance. Compared to other treatments, surgical patients were more likely to report greater than 7 days off work in the first year, with relatively less time off over time. Black men (RR 0.64, 95% CI 0.54-0.77) and those undergoing radiation (vs. surgery, RR 0.46, 95% CI 0.41-0.51) were less likely to report time off from work over time. Mean baseline GH score (73 [SD 18]) was similar between race and treatment groups, and stable over time.

CONCLUSIONS

The work burden of cancer care continued up to 10 years after treatment and varied across racial groups and primary treatment groups, highlighting the multifactorial nature of this issue and the call to leverage greater resources for those at greatest risk.

Diagnostic performance of prediction models for extraprostatic extension in prostate cancer: a systematic review and meta-analysis

PURPOSE

In recent decades, diverse nomograms have been proposed to predict extraprostatic extension (EPE) in prostate cancer (PCa). We aimed to systematically evaluate the accuracy of MRI-inclusive nomograms and traditional clinical nomograms in predicting EPE in PCa. The purpose of this meta-analysis is to provide baseline summative and comparative estimates for future study designs.

MATERIALS AND METHODS

The PubMed, Embase, and Cochrane databases were searched up to May 17, 2023, to identify studies on prediction nomograms for EPE of PCa. The risk of bias in studies was assessed by using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Summary estimates of sensitivity and specificity were obtained with bivariate random-effects model. Heterogeneity was investigated through meta-regression and subgroup analysis.

RESULTS

Forty-eight studies with a total of 57 contingency tables and 20,395 patients were included. No significant publication bias was observed for either the MRI-inclusive nomograms or clinical nomograms. For MRI-inclusive nomograms predicting EPE, the pooled AUC of validation cohorts was 0.80 (95% CI: 0.76, 0.83). For traditional clinical nomograms predicting EPE, the pooled AUCs of the Partin table and Memorial Sloan Kettering Cancer Center (MSKCC) nomogram were 0.72 (95% CI: 0.68, 0.76) and 0.79 (95% CI: 0.75, 0.82), respectively.

CONCLUSION

Preoperative risk stratification is essential for PCa patients; both MRI-inclusive nomograms and traditional clinical nomograms had moderate diagnostic performance for predicting EPE in PCa. This study provides baseline comparative values for EPE prediction for future studies which is useful for evaluating preoperative risk stratification in PCa patients.

CRITICAL RELEVANCE STATEMENT

This meta-analysis firstly evaluated the diagnostic performance of preoperative MRI-inclusive nomograms and clinical nomograms for predicting extraprostatic extension (EPE) in prostate cancer (PCa) (moderate AUCs: 0.72-0.80). We provide baseline estimates for EPE prediction, these findings will be useful in assessing preoperative risk stratification of PCa patients.

KEY POINTS

• MRI-inclusive nomograms and traditional clinical nomograms had moderate AUCs (0.72-0.80) for predicting EPE. • MRI combined clinical nomogram may improve diagnostic accuracy of MRI alone for EPE prediction. • MSKCC nomogram had a higher specificity than Partin table for predicting EPE. • This meta-analysis provided baseline and comparative estimates of nomograms for EPE prediction for future studies.

MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer

Prostate cancer (PCa) is a pernicious tumor with high heterogeneity, which creates a conundrum for making a precise diagnosis and choosing an optimal treatment approach. Multiparametric magnetic resonance imaging (mp-MRI) with anatomical and functional sequences has evolved as a routine and significant paradigm for the detection and characterization of PCa. Moreover, using radiomics to extract quantitative data has emerged as a promising field due to the rapid growth of artificial intelligence (AI) and image data processing. Radiomics acquires novel imaging biomarkers by extracting imaging signatures and establishes models for precise evaluation. Radiomics models provide a reliable and noninvasive alternative to aid in precision medicine, demonstrating advantages over traditional models based on clinicopathological parameters. The purpose of this review is to provide an overview of related studies of radiomics in PCa, specifically around the development and validation of radiomics models using MRI-derived image features. The current landscape of the literature, focusing mainly on PCa detection, aggressiveness, and prognosis evaluation, is reviewed and summarized. Rather than studies that exclusively focus on image biomarker identification and method optimization, models with high potential for universal clinical implementation are identified. Furthermore, we delve deeper into the critical concerns that can be addressed by different models and the obstacles that may arise in a clinical scenario. This review will encourage researchers to design models based on actual clinical needs, as well as assist urologists in gaining a better understanding of the promising results yielded by radiomics.

Can We Predict Prostate Cancer Metastasis Based on Biomarkers? Where Are We Now?

The incidence of prostate cancer (PC) has risen annually. PC mortality is explained by the metastatic disease (mPC). There is an intermediate scenario in which patients have non-mPC but have initiated a metastatic cascade through epithelial-mesenchymal transition. There is indeed a need for more and better tools to predict which patients will progress in the future to non-localized clinical disease or already have micrometastatic disease and, therefore, will clinically progress after primary treatment. Biomarkers for the prediction of mPC are still under development; there are few studies and not much evidence of their usefulness. This review is focused on tissue-based genomic biomarkers (TBGB) for the prediction of metastatic disease. We develop four main research questions that we attempt to answer according to the current evidence. Why is it important to predict metastatic disease? Which tests are available to predict metastatic disease? What impact should there be on clinical guidelines and clinical practice in predicting metastatic disease? What are the current prostate cancer treatments? The importance of predicting metastasis is fundamental given that, once metastasis is diagnosed, quality of life (QoL) and survival drop dramatically. There is still a need and space for more cost-effective TBGB tests that predict mPC disease.

Artificial Intelligence Predictive Model for Hormone Therapy Use in Prostate Cancer

BACKGROUND: Androgen deprivation therapy (ADT) with radiotherapy can benefit patients with localized prostate cancer. However, ADT can negatively impact quality of life, and there remain no validated predictive models to guide its use. METHODS: We used digital pathology images from pretreatment prostate tissue and clinical data from 5727 patients enrolled in five phase 3 randomized trials, in which treatment was radiotherapy with or without ADT, as our data source to develop and validate an artificial intelligence (AI)–derived predictive patient-specific model that would determine which patients would develop the primary end point of distant metastasis. The model used baseline data to provide a binary output that a given patient will likely benefit from ADT or not. After the model was locked, validation was performed using data from NRG Oncology/Radiation Therapy Oncology Group (RTOG) 9408 (n=1594), a trial that randomly assigned men to radiotherapy plus or minus 4 months of ADT. Fine–Gray regression and restricted mean survival times were used to assess the interaction between treatment and the predictive model and within predictive model–positive, i.e., benefited from ADT, and –negative subgroup treatment effects. RESULTS: Overall, in the NRG/RTOG 9408 validation cohort (14.9 years of median follow-up), ADT significantly improved time to distant metastasis. Of these enrolled patients, 543 (34%) were model positive, and ADT significantly reduced the risk of distant metastasis compared with radiotherapy alone. Of 1051 patients who were model negative, ADT did not provide benefit. CONCLUSIONS: Our AI-based predictive model was able to identify patients with a predominantly intermediate risk for prostate cancer likely to benefit from short-term ADT. (Supported by a grant [U10CA180822] from NRG Oncology Statistical and Data Management Center, a grant [UG1CA189867] from NCI Community Oncology Research Program, a grant [U10CA180868] from NRG Oncology Operations, and a grant [U24CA196067] from NRG Specimen Bank from the National Cancer Institute and by Artera, Inc. ClinicalTrials.gov numbers NCT00767286, NCT00002597, NCT00769548, NCT00005044, and NCT00033631.)