Inflammatory marker levels in children with tobacco smoke exposure.

BACKGROUND: Tobacco smoke exposure (TSE) has inflammatory and immunosuppressive effects which may be associated with altered levels of inflammatory markers and pediatric illnesses. OBJECTIVE: The primary objective was to examine the associations of cotinine-confirmed and parent-reported child TSE patterns and discharge diagnoses with C-reactive protein (CRP), IL-8, and IL-10 in 0-11-year-old pediatric emergency department (PED) patients who lived with ≥ 1 smoker. METHODS: Saliva samples were obtained from 115 children with a mean (SD) age of 3.5 (3.1) years during the PED visit (T0). Saliva was analyzed for cotinine, CRP, IL-8, and IL-10. Parents self-reported their children's TSE patterns; children's medical records were reviewed to identify and categorize discharge diagnoses. Linear regression models were utilized to find T0 associations of cotinine-confirmed and parent-reported child TSE patterns, and PED diagnoses with each inflammatory marker. All models were adjusted for child race/ethnicity, child sex, annual household income, and housing type. The TSE models also adjusted for child discharge diagnosis. RESULTS: At T0, the geometric mean (GeoM) of cotinine was 4.1 ng/ml [95 %CI = 3.2-5.2]; the GeoMs of CRP, IL-8, and IL-10 were 3,326 pg/ml [95 %CI = 2,696-4,105], 474 pg/ml [95 %CI = 386-583], and 1.1 pg/ml [95 %CI = 0.9-1.3], respectively. Parent-reported child TSE patterns were positively associated with ln-transformed CRP levels, while adjusting for the covariates (ß^ = 0.012 [95 %CI:0.004-0.020], p = 0.037). In the parent-reported child TSE pattern model, there were significant positive associations between the covariate of child age with CRP and IL-8 levels (p = 0.028 and p < 0.001, respectively). Children with a bacterial diagnosis had higher IL-8 levels (p = 0.002) compared to the other diagnosis groups. CONCLUSIONS: Results indicate that parent-reported child TSE increases the expression of CRP in ill children and supports prior work demonstrating that IL-8 is higher in children with TSE who have bacterial infections. These findings should be examined in future research with ill children with and without TSE.

Decipher Score predicts prostate specific antigen persistence after prostatectomy.

BACKGROUND: The aim of this study was to evaluate genomic risk of patients with persistent prostate specific antigen (PSA) using mRNA expression analysis and a validated prognostic genomic-risk classifier. METHODS: Monocentric retrospective study including all patients who underwent radical prostatectomy (RP) by one surgeon and Decipher Test from October 2013 to December 2018. PSA persistent population was defined as all patients with two consecutive PSA>0.1 ng/mL at follow-up after the surgery. Neurovascular Structure-adjacent Frozen-section Examination (NeuroSAFE) was performed intraoperatively for research of positive surgical margins. Multivariate analysis was performed for persistent PSA (pPSA) predictors. A specific localized, organ-confined, and negative margins sub-population with PSA persistence was compared to a similar sub-population without PSA persistence for genomic differential expression analyses. RESULTS: A total of 564 patients were included and 61 of them had pPSA. Preoperative PSA was higher in the PSA persistent group (11.6 [6.4, 21.2] vs. 6.2 [4.7, 9.2] P=0.00010), as well as PSA density (PSAd) (0.3 [0.2, 0.5] vs. 0.2 [0.1, 0.3] P=0.0001). Postoperative characteristics, Gleason Score, and positive surgical margins were significantly higher in the PSA persistent population. 31 patients had pPSA in our specific subpopulation and were compared to 217 patients with no pPSA. On multivariate analysis, only Decipher Score (OR=5.64 [1.28; 24.89], P=0.022) and preoperative PSA (OR=1.06, [1.02; 1.09], P=0.001) were significant predictors for PSA persistence. We found two genes to be significantly upregulated with a 2.5-fold change in our specific subpopulation (SERPINB11 and PDE11A). CONCLUSIONS: We found unique genomic features of patients with pPSA, whilst confirming previous clinical findings that this condition behaves to a worse prognosis. Given this high genomic risk, further imaging studies should be performed to select patients for early treatment intensification.

Association between Expression of Connective Tissue Genes and Prostate Cancer Growth and Progression.

To find an association between genomic features of connective tissue and pejorative clinical outcomes on radical prostatectomy specimens. We performed a retrospective analysis of patients who underwent radical prostatectomy and underwent a Decipher transcriptomic test for localized prostate cancer in our institution (n = 695). The expression results of selected connective tissue genes were analyzed after multiple t tests, revealing significant differences in the transcriptomic expression (over- or under-expression). We investigated the association between transcript results and clinical features such as extra-capsular extension (ECE), clinically significant cancer, lymph node (LN) invasion and early biochemical recurrence (eBCR), defined as earlier than 3 years after surgery). The Cancer Genome Atlas (TCGA) was used to evaluate the prognostic role of genes on progression-free survival (PFS) and overall survival (OS). Out of 528 patients, we found that 189 had ECE and 27 had LN invasion. The Decipher score was higher in patients with ECE, LN invasion, and eBCR. Our gene selection microarray analysis showed an overexpression in both ECE and LN invasion, and in clinically significant cancer for COL1A1, COL1A2, COL3A1, LUM, VCAN, FN1, AEBP1, ASPN, TIMP1, TIMP3, BGN, and underexpression in FMOD and FLNA. In the TCGA population, overexpression of these genes was correlated with worse PFS. Significant co-occurrence of these genes was observed. When presenting overexpression of our gene selection, the 5-year PFS rate was 53% vs. 68% (p = 0.0315). Transcriptomic overexpression of connective tissue genes correlated to worse clinical features, such as ECE, clinically significant cancer and BCR, identifying the potential prognostic value of the gene signature of the connective tissue in prostate cancer. TCGAp cohort analysis showed a worse PFS in case of overexpression of the connective tissue genes.

When to order genomic tests: development and external validation of a model to predict high-risk prostate cancer at the genotypic level.

PURPOSE: The aim of this study was to develop a model to predict high-genomic-risk prostate cancer (PCa) according to Decipher score, a validated 22 gene prognostic panel. By doing so, one might select the individuals who are likely to benefit from genomic testing and improve pre-op counseling about the need for adjuvant treatments. METHODS: We retrospectively reviewed IRB-approved databases at two institutions. All patients had preoperative magnetic resonance imaging (MRI) and Decipher prostate radical prostatectomy (RP), a validated 22 gene prognostic panel. We used binary logistic regression to estimate high-risk Decipher (Decipher score > 0.60) probability on RP specimen. Area under the curve (AUC) and calibration were used to assess the accuracy of the model in the development and validation cohort. Decision curve analysis (DCA) was performed to assess the clinical benefit of the model. RESULTS: The development and validation cohort included 622 and 185 patients with 283 (35%) and 80 (43%) of those with high-risk Decipher. The multivariable model included PSA density, biopsy Gleason Grade Group, percentage of positive cores and MRI extracapsular extension. AUC was 0.73 after leave-one-out cross-validation. DCA showed a clinical benefit in a range of probabilities between 15 and 60%. In the external validation cohort, AUC was 0.70 and calibration showed that the model underestimates the actual probability of the outcome. CONCLUSIONS: The proposed model to predict high-risk Decipher score at RP is helpful to improve risk stratification of patients with PCa and to assess the need for additional testing and treatments.

A SelectMDx/magnetic resonance imaging-based nomogram to diagnose prostate cancer.

BACKGROUND: 70%-80% of prostate cancer (PCa) biopsies performed in the US annually may be unnecessary. Specific antigen testing (PSA) and tans rectal ultrasound (TRUS) are imprecise predictive methods for risk of PCa. Novel strategies are critical to guide biopsy decision-making. AIM: We assessed the utility and accuracy of combining Select MDx and multiparametric magnetic resonance imaging (mpMRI) scores for predicting risk of PCa. METHODS AND RESULTS: Our study was conducted at Mount Sinai hospital at Urology department in New York City from January 2020 to April 2021. Total 129 men performed select MDx test. Indications for prostate biopsy were high-risk Select MDx score, suspicious DRE, PI-RADS scores 3/4/5 on mpMRI, or any combination of these. Fifty-one percentage of 129 patients underwent systemic or combined systemic and MRI/US (ultrasound) fusion biopsy; All men underwent 3 T MRI of Prostate w/wo contrast using standard protocols prior to biopsy. A single surgeon performed prostate biopsies. Gleason score ≥3 + 3 on biopsy is defined as outcome. Descriptive statistics were calculated as cross tables. Binary logistic regression model is used to determine the outcome. The nomogram was based on the coefficients of the logit function. ROCs were plotted and decision curve analysis was performed. Using both high-risk Select MDx and PI-RADS scores of 4/5, 87% of biopsies could have been avoided, while detecting 64% of PCa and missing 36%. If biopsies were performed on men with positive Select MDx or PI-RADS 4/5 results, 16% of biopsies could have been avoided while detecting all PCa. Combining these scores improved specificity and accuracy for the detection of PCa over either used alone. Study limitations include limited sample size, sole institution study, and risk or overfitting for the proposed model which may limit generalizability. CONCLUSION: Combining SelectMDx and mpMRI PI-PADS scores of 4/5 may be useful for PCa biopsy decision-making.

The Mount Sinai Prebiopsy Risk Calculator for Predicting any Prostate Cancer and Clinically Significant Prostate Cancer: Development of a Risk Predictive Tool and Validation with Advanced Neural Networking, Prostate Magnetic Resonance Imaging Outcome Database, and European Randomized Study of Screening for Prostate Cancer Risk Calculator.

Background: The European Association of Urology guidelines recommend the use of imaging, biomarkers, and risk calculators in men at risk of prostate cancer. Risk predictive calculators that combine multiparametric magnetic resonance imaging with prebiopsy variables aid as an individualized decision-making tool for patients at risk of prostate cancer, and advanced neural networking increases reliability of these tools. Objective: To develop a comprehensive risk predictive online web-based tool using magnetic resonance imaging (MRI) and clinical data, to predict the risk of any prostate cancer (PCa) and clinically significant PCa (csPCa) applicable to biopsy-naïve men, men with a prior negative biopsy, men with prior positive low-grade cancer, and men with negative MRI. Design setting and participants: Institutional review board-approved prospective data of 1902 men undergoing biopsy from October 2013 to September 2021 at Mount Sinai were collected. Outcome measurements and statistical analysis: Univariable and multivariable analyses were used to evaluate clinical variables such as age, race, digital rectal examination, family history, prostate-specific antigen (PSA), biopsy status, Prostate Imaging Reporting and Data System score, and prostate volume, which emerged as predictors for any PCa and csPCa. Binary logistic regression was performed to study the probability. Validation was performed with advanced neural networking (ANN), multi-institutional European cohort (Prostate MRI Outcome Database [PROMOD]), and European Randomized Study of Screening for Prostate Cancer Risk Calculator (ERSPC RC) 3/4. Results and limitations: Overall, 2363 biopsies had complete clinical information, with 57.98% any cancer and 31.40% csPCa. The prediction model was significantly associated with both any PCa and csPCa having an area under the curve (AUC) of 81.9% including clinical data. The AUC for external validation was calculated in PROMOD, ERSPC RC, and ANN for any PCa (0.82 vs 0.70 vs 0.90) and csPCa (0.82 vs 0.78 vs 0.92), respectively. This study is limited by its retrospective design and overestimation of csPCa in the PROMOD cohort. Conclusions: The Mount Sinai Prebiopsy Risk Calculator combines PSA, imaging and clinical data to predict the risk of any PCa and csPCa for all patient settings. With accurate validation results in a large European cohort, ERSPC RC, and ANN, it exhibits its efficiency and applicability in a more generalized population. This calculator is available online in the form of a free web-based tool that can aid clinicians in better patients counseling and treatment decision-making. Patient summary: We developed the Mount Sinai Prebiopsy Risk Calculator (MSP-RC) to assess the likelihood of any prostate cancer and clinically significant disease based on a combination of clinical and imaging characteristics. MSP-RC is applicable to all patient settings and accessible online.

Association between Incidental Pelvic Inflammation and Aggressive Prostate Cancer.

The impact of pelvic inflammation on prostate cancer (PCa) biology and aggressive phenotype has never been studied. Our study objective was to evaluate the role of pelvic inflammation on PCa aggressiveness and its association with clinical outcomes in patients following radical prostatectomy (RP). This study has been conducted on a retrospective single-institutional consecutive cohort of 2278 patients who underwent robot-assisted laparoscopic prostatectomy (RALP) between 01/2013 and 10/2019. Data from 2085 patients were analyzed to study the association between pelvic inflammation and adverse pathology (AP), defined as Gleason Grade Group (GGG) > 2 and ≥ pT3 stage, at resection. In a subset of 1997 patients, the association between pelvic inflammation and biochemical recurrence (BCR) was studied. Alteration in tumor transcriptome and inflammatory markers in patients with and without pelvic inflammation were studied using microarray analysis, immunohistochemistry, and culture supernatants derived from inflamed sites used in functional assays. Changes in blood inflammatory markers in the study cohort were analyzed by O-link. In univariate analyses, pelvic inflammation emerged as a significant predictor of AP. Multivariate cox proportional-hazards regression analyses showed that high pelvic inflammation with pT3 stage and positive surgical margins significantly affected the time to BCR (p ≤ 0.05). PCa patients with high inflammation had elevated levels of pro-inflammatory cytokines in their tissues and in blood. Genes involved in epithelial-to-mesenchymal transition (EMT) and DNA damage response were upregulated in patients with pelvic inflammation. Attenuation of STAT and IL-6 signaling decreased tumor driving properties of conditioned medium from inflamed sites. Pelvic inflammation exacerbates the progression of prostate cancer and drives an aggressive phenotype.

Prostate MRI percentage tumor involvement or "PI-RADS percent" as a predictor of adverse surgical pathology.

BACKGROUND: This study assesses magnetic resonance imaging (MRI) prostate % tumor involvement or "PI-RADs percent" as a predictor of adverse pathology (AP) after surgery for localized prostate cancer (PCa). Two separate variables, "All PI-RADS percent" (APP) and "Highest PI-RADS percent" (HPP), are defined as the volume of All PI-RADS 3-5 score lesions on MRI and the volume of the Highest PI-RADS 3-5 score lesion each divided by TPV, respectively. METHOD: An analysis was done of an IRB approved prospective cohort of 557 patients with localized PCa who had targeted biopsy of MRI PIRADs 3-5 lesions followed by RARP from April 2015 to May 2020 performed by a single surgeon at a single center. AP was defined as ISUP GGG ≥3, pT stage ≥T3 and/or LNI. Univariate and multivariable analyses were used to evaluate APP and HPP at predicting AP with other clinical variables such as Age, PSA at surgery, Race, Biopsy GGG, mpMRI ECE and mpMRI SVI. Internal and External Validation demonstrated predicted probabilities versus observed probabilities. RESULTS: AP was reported in 44.5% (n = 248) of patients. Multivariable regression showed both APP (odds ratio [OR]: 1.10, 95% confidence interval [CI]: 1.04-1.14, p = 0.0007) and HPP (OR: 1.10; 95% CI: 1.04-1.16; p = 0.0007) were significantly associated with AP with individual area under the operating curves (AUCs) of 0.6142 and 0.6229, respectively, and AUCs of 0.8129 and 0.8124 when incorporated in models including preoperative PSA and highest biopsy GGG. CONCLUSIONS: Increasing PI-RADS Percent was associated with a higher risk of AP, and both APP and HPP may have clinical utility as predictors of AP in GGG 1 and 2 patients being considered for AS. PATIENT SUMMARY: Using PIRADs percent to predict AP for presurgical patients may help risk stratification, and for low and low volume intermediate risk patients, may influence treatment decisions.

Nomogram Predicting Adverse Pathology Outcome on Radical Prostatectomy in Low-Risk Prostate Cancer Men.

OBJECTIVE: To develop and validate a prediction model to predict the risk of adverse pathology outcome on final pathology in low-risk prostate cancer (PCa) men. MATERIALS AND METHODS: This study was a monocentric retrospective analysis of 426 men who underwent radical prostatectomy (RP) for low-risk PCa. The validation cohort included 103 men from another hospital. Adverse pathology outcome was defined either by upgrading on RP Gleason Score (GS) (from GS 3+3 to GS ≥ 3+4 with Gleason pattern 4 ≥ 10%) or a non-organ confined disease (pathologic stage ≥ pT3a). Multivariable logistic regression analysis was performed to build nomogram for predicting adverse pathology outcome. Nomogram validation was performed by calculating the area under receiver operating characteristic curves (AUC) and comparing nomogram-predicted probabilities with actual rates of adverse pathology outcome in the external cohort. The Kaplan-Meier method was used to estimate and compare the biochemical recurrence-free survival rates between the two groups. RESULTS: Of 426 men in the development cohort, 45.7% showed adverse pathology outcome on RP. Age, body mass index, prostate specific antigen density, history of prior negative biopsy, magnetic resonance imaging prostate imaging reporting and data system score 4-5 and percentage of positive biopsies were significant predictors in multivariate analysis. A nomogram was constructed with an area under curve of 87%. There was agreement between predicted and actual rates of adverse pathology outcome in the validation cohort. The 5-year biochemical recurrence-free survival rates in patients with and without adverse pathology outcome was 70% and 98%, respectively. CONCLUSION: This novel nomogram would help identify low-risk PCa men at risk of adverse pathology outcome and can be relevant for treatment decision-making.

Development and External Validation of a Prediction Model to Identify Candidates for Prostate Biopsy.

PURPOSE: Prostate biopsies are associated with infectious complications and approximately 80% are either benign or clinically insignificant prostate cancer. Our aim is to develop and independently validate prediction model to avoid unnecessary prostate biopsies by predicting clinically significant prostate cancer (csPCa) Materials and Methods: Retrospective analysis of single-center cohort (Mount Sinai Hospital, NY) of 1632 men who underwent systematic or combined systematic and Magnetic Resonance Imaging (MRI)/ultrasound fusion targeted prostate biopsy between 2014-2020. External cohort (University of Miami) included 622 men that underwent biopsy. Outcome for predicting csPCa was defined as International Society of Urologic Pathology (ISUP) Gleason grade ≥ 2 on biopsy. Multivariable logistic regression analysis was performed to build nomogram using coefficients of logit function. Nomogram validation was performed in external cohort by plotting receiver operating characteristics (ROC). We also plotted decision curve analysis (DCA) and compared nomogram-predicted probabilities with actual rates of csPCa probabilities in external cohort. RESULTS: Of 1632 men, 43% showed csPCa on biopsy. PSA density, prior negative biopsy, and Prostate Imaging and Reporting Data System (PI-RADS) scores 3, 4, and 5 were significant predictors for csPCa. ROC for prediction of csPCa was 0.88 in external cohort. There was agreement between predicted and actual rate of csPCa in external cohort. DCA demonstrated net benefit using the model. Using the prediction model at threshold of 30, 35% of biopsies and 46% of diagnosed indolent PCa could be avoided, while missing 5% of csPCa. CONCLUSION: Using our prediction model can help reduce unnecessary prostate biopsies with minimal impact on csPCa detection rates.

Expanding Active Surveillance Inclusion Criteria: A Novel Nomogram Including Preoperative Clinical Parameters and Magnetic Resonance Imaging Findings.

BACKGROUND: Current European Association of Urology, American Urological Association, and National Comprehensive Cancer Network guidelines recommend active surveillance (AS) for selected intermediate-risk prostate cancer (PCa) patients. However, limited evidence exists regarding which men can be selected safely. OBJECTIVE: To externally validate the Gandaglia risk calculator (Gandaglia-RC), designed to predict adverse pathology (AP) at radical prostatectomy (RP) and thus able to improve selection of intermediate-risk PCa patients suitable for AS, and to assess whether addition of magnetic resonance imaging (MRI) findings (MAP model) improves the predictive ability of Gandaglia-RC. DESIGN, SETTING, AND PARTICIPANTS: This is a retrospective analysis of a single-center cohort of 1284 consecutive men with low- and intermediate-risk PCa treated with RP between 2013 and 2019. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: AP was defined as non-organ-confined disease and/or lymph node invasion and/or pathological grade group≥3 at RP. Logistic regression was used to calculate the predictors of AP; calculated coefficients were used to develop a risk score. Receiver operating characteristic curve analysis and decision curve analysis were performed to evaluate the net benefit within models. RESULTS AND LIMITATIONS: At multivariable analysis, age at surgery, prostate-specific antigen, systematic and targeted biopsy Gleason grade group, MRI prostate volume, Prostate Imaging Reporting and Data System score, and MRI extraprostatic extension were significantly associated with AP. The model significantly improved the ability of Gandaglia-RC to predict AP (area under the curve 0.71 vs 0.63 [p<0.0001]). Using a 30% threshold, the proportions of men eligible for AS were 45% and 77% and the risks of AP were 16% and 17%, for Gandaglia-RC and MAP model, respectively. CONCLUSIONS: Compared with Gandaglia-RC, the MAP model significantly increased the number of patients eligible for AS without significantly increasing the risk of AP at RP. PATIENT SUMMARY: In this report, we have developed a risk prediction tool to select men for conservative treatment of prostate cancer. Using the novel tool, more men could safely be allocated to conservative treatment rather than surgery or radiation.

The Role of 3D Models Obtained from Multiparametric Prostate MRI in Performing Robotic Prostatectomy.

Background: Multiparametric prostate MRI (mpMRI) can provide important information for surgical planning, yet its interpretation is not immediate and imaging consultation at the time of surgery can result in interruptions and delay. The use of three-dimensional (3D) models based on mpMRI might obviate these issues. We aimed to evaluate the role of the prospective integration of 3D models from mpMRI in the robotic console in reducing the rate of positive surgical margins (PSMs). Materials and Methods: PSMs at our center are evaluated intraoperatively using the Neurovascular Structure Adjacent Frozen Section Examination method. Based on the rate of PSMs on frozen section during the year before the implementation of 3D models during surgery (22.5%), we estimated that 151 subjects were needed to detect a statistically significant difference of at least 40%. Patients with biopsy-proven prostate cancer (PCa) who received a 3T mpMRI at our institution and had a PIRADS ≥3 on mpMRI were included. Results: One hundred fifty-one patients were included. Overall, 17 (11.3%) patients had a PSM, 6 (35%) of them had PSM in an area where the mpMRI did not demonstrate any lesions. The rates of PSMs on both frozen (22.5% vs 11.3%) and permanent section (13.1% vs 6.6%) were significantly lower (p ≤ 0.03) compared with the cohort of patients operated during 2018 (n = 358). No significant differences among clinical characteristics were found between the study cohort and the 2018 cohort (all p > 0.05). Conclusions: The use of 3D models at the time of surgery was shown to reduce the PSM rate on both frozen and permanent section. Integrating 3D models in the robotic console could lead to improved PCa outcomes.

Unified model involving genomics, magnetic resonance imaging and prostate-specific antigen density outperforms individual co-variables at predicting biopsy upgrading in patients on active surveillance for low risk prostate cancer.

BACKGROUND: Active surveillance (AS) is the reference standard treatment for the management of low risk prostate cancer (PCa). Accurate assessment of tumor aggressiveness guides recruitment to AS programs to avoid conservative treatment of intermediate and higher risk patients. Nevertheless, underestimating the disease risk may occur in some patients recruited, with biopsy upgrading and the concomitant potential for delayed treatment. AIM: To evaluate the accuracy of mpMRI and GPS for the prediction of biopsy upgrading during active surveillance (AS) management of prostate cancer (PCa). METHOD: A retrospective analysis was performed on 144 patients recruited to AS from October 2013 to December 2020. Median follow was 4.8 (IQR 3.6, 6.3) years. Upgrading was defined as upgrading to biopsy grade group ≥2 on follow up biopsies. Cox proportional hazard regression was used to investigate the effect of PSA density (PSAD), baseline Prostate Imaging-Reporting and Data System (PI-RADS) v2.1 score and GPS on upgrading. Time-to-event outcome, defined as upgrading, was estimated using the Kaplan-Meier method with log-rank test. RESULTS: Overall rate of upgrading was 31.9% (n = 46). PSAD was higher in the patients who were upgraded (0.12 vs. 0.08 ng/ml2 , p = .005), while no significant difference was present for median GPS in the overall cohort (overall median GPS 21; 22 upgrading vs. 20 no upgrading, p = .2044). On univariable cox proportional hazard regression analysis, the factors associated with increased risk of biopsy upgrading were PSA (HR = 1.30, CI 1.16-1.47, p = <.0001), PSAD (HR = 1.08, CI 1.05-1.12, p = <.0001) and higher PI-RADS score (HR = 3.51, CI 1.56-7.91, p = .0024). On multivariable cox proportional hazard regression analysis, only PSAD (HR = 1.10, CI 1.06-1.14, p = <.001) and high PI-RADS score (HR = 4.11, CI 1.79-9.44, p = .0009) were associated with upgrading. A cox regression model combining these three clinical features (PSAD ≥0.15 ng/ml2 at baseline, PI-RADS Score and GPS) yielded a concordance index of 0.71 for the prediction of upgrading. CONCLUSION: In this study PSAD has higher accuracy over baseline PI-RADS score and GPS score for the prediction of PCa upgrading during AS. However, combined use of PSAD, GPS and PI-RADS Score yielded the highest predictive ability with a concordance index of 0.71.

Impact of diverticular disease on prostate cancer risk among hypertensive men.

INTRODUCTION: Prostate cancer (PCa) is a heterogenous disease with multiple etiological factors playing a role in its development. Recently, chronic and systemic inflammatory conditions such as inflammatory bowel disease were identified as key risk factors influencing its development. The study aimed to evaluate the relationship between diverticular disease (DD) (local and acute inflammation) and PCa. METHODS: Hypertensive patients with DD and hypertensive controls were identified between 1995 and 2010 from the Statewide Planning and Research Cooperative System database. Cohorts were queried for PCa incidence through 2015. Univariable and multivariable logistic regression analyses were used for determining independent predictors of PCa diagnosis. RESULTS: A total of 51,353 patients with DD and 111,541 controls were identified. In all, 6.26% of DD developed PCa, and 3.71% of controls developed PCa (p < 0.01). DD was a significant risk factor for PCa (OR: 1.27 CI: 1.19-1.34, p < 0.01). On subgroup analysis, the patients diagnosed with DD <50 years old had an OR of 3.39 for PCa (CI: 2.52-4.56, p < 0.01), age 50-59 had an OR of 2.12 (CI: 1.86-2.15, p < 0.01), and age 60-69 had an OR of 1.20 (CI: 1.10-1.31, p < 0.01). Finally, age and race stratification showed that white patients <50 had an OR of 2.56 (CI: 1.75-3.76, p < 0.01), while black patients <50 had an OR of 3.98 (CI: 2.61-6.07, p < 0.01). The trend in differing odds between these populations was the same for age groups 50-59 and 60-69. CONCLUSION: Our analysis shows that DD is associated with diagnosis of PCa in hypertensive men. Importantly, the earlier the diagnosis of DD, the higher the odds for development of PCa, particularly in black men.

Multiphoton Microscopy for Identifying Collagen Signatures Associated with Biochemical Recurrence in Prostate Cancer Patients.

Prostate cancer is a heterogeneous disease that remains dormant for long periods or acts aggressively with poor clinical outcomes. Identifying aggressive prostate tumor behavior using current glandular-focused histopathological criteria is challenging. Recent evidence has implicated the stroma in modulating prostate tumor behavior and in predicting post-surgical outcomes. However, the emergence of stromal signatures has been limited, due in part to the lack of adoption of imaging modalities for stromal-specific profiling. Herein, label-free multiphoton microscopy (MPM), with its ability to image tissue with stromal-specific contrast, is used to identify prostate stromal features associated with aggressive tumor behavior and clinical outcome. MPM was performed on unstained prostatectomy specimens from 59 patients and on biopsy specimens from 17 patients with known post-surgery recurrence status. MPM-identified collagen content, organization, and morphological tumor signatures were extracted for each patient and screened for association with recurrent disease. Compared to tumors from patients whose disease did not recur, tumors from patients with recurrent disease exhibited higher MPM-identified collagen amount and collagen fiber intensity signal and width. Our study shows an association between MPM-identified stromal collagen features of prostate tumors and post-surgical disease recurrence, suggesting their potential for prostate cancer risk assessment.

Clinical Utility of Negative Multiparametric Magnetic Resonance Imaging in the Diagnosis of Prostate Cancer and Clinically Significant Prostate Cancer.

BACKGROUND: Multiparametric magnetic resonance imaging (MRI) is increasingly used to diagnose prostate cancer (PCa). It is not yet established whether all men with negative MRI (Prostate Imaging-Reporting and Data System version 2 score <3) should undergo prostate biopsy or not. OBJECTIVE: To develop and validate a prediction model that uses clinical parameters to reduce unnecessary prostate biopsies by predicting PCa and clinically significant PCa (csPCa) for men with negative MRI findings who are at risk of harboring PCa. DESIGN SETTING AND PARTICIPANTS: This was a retrospective analysis of 200 men with negative MRI at risk of PCa who underwent prostate biopsy (2014-2020) with prostate-specific antigen (PSA) >4 ng/ml, 4Kscore of >7%, PSA density ≥0.15 ng/ml/cm3, and/or suspicious digital rectal examination. The validation cohort included 182 men from another centre (University of Miami) with negative MRI who underwent systematic prostate biopsy with the same criteria. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: csPCa was defined as Gleason grade group ≥2 on biopsy. Multivariable logistic regression analysis was performed using coefficients of logit function for predicting PCa and csPCa. Nomogram validation was performed by calculating the area under receiver operating characteristic curves (AUC) and comparing nomogram-predicted probabilities with actual rates of PCa and csPCa. RESULTS AND LIMITATIONS: Of 200 men in the development cohort, 18% showed PCa and 8% showed csPCa on biopsy. Of 182 men in the validation cohort, 21% showed PCa and 6% showed csPCa on biopsy. PSA density, 4Kscore, and family history of PCa were significant predictors for PCa and csPCa. The AUC was 0.80 and 0.87 for prediction of PCa and csPCa, respectively. There was agreement between predicted and actual rates of PCa in the validation cohort. Using the prediction model at threshold of 40, 47% of benign biopsies and 15% of indolent PCa cases diagnosed could be avoided, while missing 10% of csPCa cases. The small sample size and number of events are limitations of the study. CONCLUSIONS: Our prediction model can reduce the number of prostate biopsies among men with negative MRI without compromising the detection of csPCa. PATIENT SUMMARY: We developed a tool for selection of men with negative MRI (magnetic resonance imaging) findings for prostate cancer who should undergo prostate biopsy. This risk prediction tool safely reduces the number of men who need to undergo the procedure.

Clinical characteristics and oncological outcomes in negative multiparametric MRI patients undergoing robot-assisted radical prostatectomy.

BACKGROUND: Efforts are ongoing to try and find ways to reduce the number of unnecessary prostate biopsies without missing clinically significant prostate cancers (csPCa). The utility of multiparametric magnetic resonance imaging (mpMRI) in detecting prostate cancer (PCa) shows promise to be used as triage test for systematic prostate biopsy. Our aim is to Study clinical parameters and oncological outcomes in men with negative mpMRI (nMRI; PI-RADS v2 scores of ≤ 2) who underwent robot-assisted radical prostatectomy (RARP) to evaluate nMRI's practicality as a biopsy triage test. METHODS: Retrospective analysis of 331 men with nMRI who underwent RARP between 2014 and 2020 compared with men with positive mpMRI (pMRI; PI-RADS v2 scores ≥ 3, N = 1770). csPCa was defined as Gleason score ≥ 3 + 4 and biochemical recurrence (BCR) was defined as PSA > 0.2 ng/ml on two occasions. Biopsies were graded with the International Society of Urologic Pathology [ISUP] grade. Descriptive statistics for nMRI and pMRI were performed. Mann-Whitney U test was used for continuous variables and χ 2 for categorical variables. Univariable and multivariable regression analyses were performed. RESULTS: Univariable analysis shows statistically significant difference (p < .05) between median age (nMRI-61 years vs. pMRI 63 years), race (higher incidence of nMRI in African American men), use of 5-alpha reductase inhibitors (higher rate in nMRI). While incidence rates of family history of PCa, suspicious digital rectal examination (DRE) findings, median PSA levels and 4Kscore, were lower in nMRI versus pMRI. Rates of positive surgical margins and BCR were comparable in nMRI versus pMRI. Biopsy ISUP Grades I and II upgraded by 51% and 12%, respectively in final pathology. African American race and no history of the prior negative biopsy were significant predictors for upgrading. CONCLUSION: Men with nMRI pose diagnostic challenges as they tend to be younger patients with lower rates of suspicious DRE findings and lower 4K scores, yet comparable oncological outcomes in csPCa rates, positive surgical margins, and BCR rates.

Increased Hospitalization and Mortality from COVID-19 in Prostate Cancer Patients.

BACKGROUND: Cancer patients with COVID-19 have a poor disease course. Among tumor types, prostate cancer and COVID-19 share several risk factors, and the interaction of prostate cancer and COVID-19 is purported to have an adverse outcome. METHODS: This was a single-institution retrospective study on 286,609 patients who underwent the COVID-19 test at Mount Sinai Hospital system from March 2020 to December 2020. Chi-square/Fisher's exact tests were used to summarize baseline characteristics of categorical data, and Mann-Whitney U test was used for continuous variables. Univariable logistic regression analysis to compare the hospitalization and mortality rates and the strength of association was obtained by the odds ratio and confidence interval. RESULTS: This study aimed to compare hospitalization and mortality rates between men with COVID-19 and prostate cancer and those who were COVID-19-positive with non-prostate genitourinary malignancy or any solid cancer, and with breast cancer patients. We also compared our studies to others that reported the incidence and severity of COVID-19 in prostate cancer patients. Our studies highlight that patients with prostate cancer had higher susceptibility to COVID-19-related pathogenesis, resulting in higher mortality and hospitalization rates. Hospitalization and mortality rates were higher in prostate cancer patients with COVID-19 when compared with COVID-19 patients with non-prostate genitourinary (GU) malignancies.

A 4K score/MRI-based nomogram for predicting prostate cancer, clinically significant prostate cancer, and unfavorable prostate cancer.

BACKGROUND: The detection of prostate cancer requires histological confirmation in biopsy core. Currently, number of unnecessary prostate biopsies are being performed in the United States. This is due to the absence of appropriate biopsy decision-making protocol. AIM: To develop and validate a 4K score/multiparametric magnetic resonance imaging (mpMRI)-based nomogram to predict prostate cancer (PCa), clinically significant prostate cancer (csPCa), and unfavorable prostate cancer (uPCa). METHODS AND RESULTS: Retrospective, single-center study evaluating a cohort of 574 men with 4K score test >7% or suspicious digital rectal examination (DRE) or Prostate Imaging Reporting and Data System (PI-RADS) scores 3, 4, or 5 on mpMRI that underwent systematic and/or mpMRI/ultrasound fusion-targeted prostate biopsy between 2016 and 2020. External cohort included 622 men. csPCa and uPCa were defined as Gleason score ≥3 + 4 and ≥4 + 3 on biopsy, respectively. Multivariable logistic regression analysis was performed to build nomogram for predicting PCa, csPCa, and uPCa. Validation was performed by plotting the area under the curve (AUC) and comparing nomogram-predicted probabilities with actual rates of PCa, csPCa, and uPCa probabilities in the external cohort. 4K score, a PI-RADS ≥4, prostate volume and prior negative biopsy were significant predictors of PCa, csPCa, and uPCa. AUCs were 0.84, 0.88, and 0.86 for the prediction of PCa, csPCa, and uPCa, respectively. The predicted and actual rates of PCa, csPCa, and uPCa showed agreement across all percentage probability ranges in the validation cohort. Using the prediction model at threshold of 30, 30% of overall biopsies, 41% of benign biopsies, and 19% of diagnosed indolent PCa could be avoided, while missing 9% of csPCa. CONCLUSION: This novel nomogram would reduce unnecessary prostate biopsies and decrease detection of clinically insignificant PCa.

Hood Technique for Robotic Radical Prostatectomy-Preserving Periurethral Anatomical Structures in the Space of Retzius and Sparing the Pouch of Douglas, Enabling Early Return of Continence Without Compromising Surgical Margin Rates.

BACKGROUND: A common side effect following radical prostatectomy is urinary incontinence. Here, we describe a novel surgical technique to reduce postoperative urinary incontinence and facilitate early return of continence. OBJECTIVE: To describe the novel "hood technique" for robotic-assisted radical prostatectomy (RARP). DESIGN, SETTING, AND PARTICIPANTS: This is an institutional review board-approved prospective study of 300 patients (median age 64 yr) with localized prostate cancer treated with the RARP hood technique at a major urban hospital between April 2018 and March 2019. The exclusion criteria were as follows: patients with anterior tumor location based on biopsy or multiparametric magnetic resonance imaging. All but one patient participated in follow-up over 12 mo after the procedure. SURGICAL PROCEDURE: The RARP "hood technique" was performed to preserve the detrusor apron, puboprostatic ligament complex, arcus tendineus, endopelvic fascia, and pouch of Douglas. MEASUREMENTS: Clinical data collected included pre- and intraoperative variables, and postoperative functional and oncological outcomes and complications. Descriptive statistical analysis was performed. RESULTS AND LIMITATIONS: Continence rates at 1, 2, 4, 6 12, 24, and 48 wk after catheter removal were 21%, 36%, 83%, 88%, 91%, 94%, and 95%, respectively. Positive surgical margin rate was 6%. Thirty patients (9.7%) experienced complications after RARP: 17 (5.7%), 11 (3.6%), and one (0.4%) had Clavien-Dindo grade I, II, and III complications, respectively. This study was conducted within a single health system and may not be generalizable. The study lacked randomization and a comparative arm. CONCLUSIONS: Results indicate that the hood technique spares musculofascial structures anterior to the urethral sphincter complex with early return of continence after surgery, without compromising positive surgical margin rates. Exclusion of anterior tumor location contributed to a reduction in positive surgical margins. PATIENT SUMMARY: By better preservation of anatomical structures around the urethra, we were able to achieve early return of urinary continence without a negative impact on complications and cancer outcomes.