Percentage Of Positive Biopsy Cores Research Articles

The clinical meaning of lymphovascular invasion: preoperative predictors and postoperative implications in prostate cancer - a retrospective study.

Lymphovascular invasion (LVI) is a pivotal histopathological parameter in prostate cancer (PCa), holding significant prognostic implications. Our study pursued a dual objective: firstly, to identify preoperative factors associated with LVI, aiming to unveil markers facilitating the recognition of patients prone to LVI during postoperative examination; and secondly, to assess postoperative outcomes correlated with LVI. We retrospectively analyzed 861 nonmetastatic PCa patients who underwent radical prostatectomy (RP), investigating preoperative factors and postoperative outcomes. Surgical specimens were processed following established guidelines. Statistical analyses utilized non-parametric tests to assess the association between LVI and both pre- and postoperative factors. Furthermore, logistic regression analyses were utilized to develop models aimed at identifying the most significant predictors of LVI and pN1 status, respectively. Numerous preoperative factors exhibited significant correlations with LVI, offering valuable clinical insights. Logistic regression identified magnetic resonance imaging (MRI)-based clinical tumor stage (cT) 3-4, biopsy Gleason Grading Group (GGG) 3-5, preoperative prostate specific antigen (PSA) ≥20 and percentage of positive biopsy cores (PPBC) ≥50% as the strongest preoperative predictors of LVI. Additionally, the study uncovered an association between LVI and postoperative outcomes, including postoperative PSA (p value <0.001), extracapsular extension (ECE) (<0.001), positive surgical margins (PSM) (<0.001), perineural invasion (PNI) (<0.001), pathological tumor stage (pT) (<0.001), pathological lymph node status (pN) (<0.001), postoperative GGG (<0.001), and operative time (0.023). Notably, the study revealed a novel and substantial association between LVI and an increased number of positive lymph nodes in pN+ patients in the univariate analysis (<0.001). Furthermore, we have found an association between LVI and pN1 status in the logistic regression analysis (odds ratio [OR] = 23.905; p <0.001). Our findings underscore the pivotal role of LVI in influencing the prognosis of prostate cancer (PCa). The study acknowledges the challenges associated with preoperative LVI assessment and emphasizes the need for future research to unravel the factors associated with this histopathological finding. Significantly, our research stands out as the first, to the best of our knowledge, toreveal the association between LVI and the number of positive lymph nodes in pN+ patients.

Microboost dose escalation for localized prostate cancer within a statewide radiation oncology quality consortium.

298 Background: The phase III FLAME trial demonstrated that in localized prostate cancer, an external beam radiotherapy (EBRT) simultaneous integrated microboost to an MRI-defined dominant intraprostatic lesion improves biochemical control without affecting toxicity and quality of life. Given the complexity required to deliver high microboost doses, we hypothesized that practice patterns are variable in clinical practice. We aimed to characterize microboost utilization and evaluate dosimetric parameters within the statewide Michigan Radiation Oncology Quality Consortium (MROQC). Methods: Men with intermediate or high-risk prostate adenocarcinoma treated with curative intent radiotherapy for intact disease were included. Data was prospectively collected, including T/N-category, Gleason score, prostate-specific antigen, and percent positive biopsy cores. Full DICOM files were available for dosimetric data. Multivariable analyses (MVA) were used to evaluate associations between receipt of microboost, known prognostic factors, and fiducial marker and rectal spacer placement (advanced image guided radiation therapy (IGRT)). Results: From 10/26/20 to 06/26/23, 741 patients across 26 centers were enrolled, 71% (n=528) with intermediate-risk and 29% (n=213) with high-risk disease. Androgen deprivation therapy was planned in 61%. EBRT + whole-gland brachytherapy boost was utilized in 29% (n=217/741) of patients. Of those treated with EBRT (71%, n=524/741), 10% received a microboost (n = 53/524). Brachytherapy boost and microboost were used in 9 and 7 centers, respectively. Most patients received either conventional fractionation or moderate hypofractionation- 11% and 66% in EBRT without microboost and 21% and 60% with microboost, respectively. Microboost treatment was associated with use of a planning MRI (91% vs. 62%, p &lt;0.0001) as well as fiducial marker and rectal spacer placement (76% vs. 45%, p &lt;0.0001). Median prostate planning target volume (PTV) was smaller in microboost patients (127 cc vs. 91 cc, p = 0.002); median boost volume was 21 cc. Median boost dose (D0.1cc) was 117% (IQR 115% – 119%) of the PTV prescription. Microboost patients had significantly higher mean bladder D1cc[%] (105.7% vs. 103.4%, p = 0.002) and numerically lower rectal D1cc[%] (90.7% vs. 94.1%, p = 0.065). On MVA, receipt of microboost was significantly associated with grade group 4 or 5 disease, planning MRI use, and fiducial and rectal spacer use. Conclusions: Within a large, diverse prospective cohort of men with prostate cancer treated in both academic and community settings, a microboost was utilized in 10% of EBRT cases. Microboost use was associated with higher grade group, MRI planning, and advanced IGRT. EBRT microboost is an emerging dose escalation strategy and further studies confirming safety and improved clinically meaningful outcomes may increase uptake in routine practice.

Development and Validation of Nomograms Based on Nutritional Risk Index for Predicting Extracapsular Extension and Seminal Vesicle Invasion in Patients Undergoing Radical Prostatectomy.

The aim of the study was to investigate the predictive value of the nutritional risk index (NRI) for extracapsular extension (ECE) and seminal vesicle invasion (SVI) in prostate cancer (PCa) patients undergoing radical prostatectomy (RP), and further develop and validate predictive nomograms for ECE and SVI based on the NRI. We retrospectively analyzed 734 PCa patients who underwent RP between 2010 and 2020 in the Department of Urology at Peking University Third Hospital. The enrolled patients were randomly divided into a primary cohort (n = 489) and a validation cohort (n = 245) in a 2:1 manner. The baseline NRI of patients was calculated using serum albumin level and body mass index, and a malnutrition status was defined as NRI ≤ 98. Univariate and multivariate logistic regression analyses were conducted to identify predictors for ECE and SVI. Nomograms for predicting ECE and SVI were established based on the results of the multivariate logistic regression analysis. The performance of the nomograms was estimated using Harrell's concordance index (C-index), the area under curve (AUC) of receiver operating characteristic (ROC) curves and the calibration curves. In the primary cohort, 70 (14.3%) patients with NRI ≤ 98 were classified as malnutrition, while the remaining 419 (85.7%) patients with NRI > 98 were considered to have normal nutrition. The nomograms for predicting ECE and SVI shared common factors including NRI, percentage of positive biopsy cores (PPC) and biopsy Gleason score, while prostate-specific antigen (PSA) levels and PSA density (PSAD) were only incorporated in ECE nomogram. The C-indexes of the nomograms for predicting ECE and SVI were 0.785 (95% confidence interval (CI): 0.745 - 0.826) and 0.852 (95% CI: 0.806 - 0.898), respectively. The calibration curves demonstrated excellent agreement between the predictions by the nomograms and the actual observations. The results remained reproducible when the nomograms were applied to the validation cohort. The NRI is significantly associated with ECE and SVI in PCa patients. The nomogram established based on the NRI in our study can provide individualized risk estimation for ECE and SVI in PCa patients, and may be valuable for clinicians in making well-informed decisions regarding treatment strategies and patient management.

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

Background and objectiveActive 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, GG1) PCa. Opponents are concerned that initial undersampling and delay of definitive management for patients with GG2 disease may lead to adverse outcomes. We sought to determine whether the timing for definitive management of GG2 PCa, either upfront or after initial AS, affects recurrence outcomes after radical prostatectomy (RP). MethodsParticipants were diagnosed with cT1–2N0/xM0/x, prostate-specific antigen (PSA) <20 ng/ml, and GG1–2 PCa between 2000 and 2020 and underwent immediate RP for GG2 or AS followed by delayed RP on upgrading to GG2. 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 GG2, 190 underwent RP within 12 mo of upgrading to GG2 on AS, and 90 men underwent RP >12 mo after upgrading to GG2. 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 GG2 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. Conclusionsand clinical implications: PPC and PSA before RP, but not the timing of definitive surgery after upgrade to GG2, 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 GG2 disease. Patient summaryIn 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.

An MRI-Based Convolutional Neural Network to Predict Biochemical Recurrence Following Radiotherapy for Intermediate and High-Risk Localized Prostate Cancer.

The risk of biochemical recurrence (BCR) following radiotherapy (RT) for localized prostate cancer (LPCa) varies considerably within risk stratification groups defined by classic clinical and pathologic variables; there is an unmet need for low-cost tools that more robustly predict BCR and allow for individualized therapy. Published imaging-based algorithms for BCR prediction after RT are limited to hand-crafted radiomics and/or small cohorts. We aimed to develop a deep learning model to predict BCR at 5 years after RT for intermediate and high risk LPCa using pre-treatment T2-weighted (T2W) MRI. Patients with intermediate and high risk LPCa treated with radical RT at our institution between 2010 and 2015 were included. We excluded those who did not have a pre-treatment T2W-MRI and those with less than 5 years of follow-up. The Phoenix definition for BCR was used. The dataset (DS1) was split into training (70%), validation (20%), and test (10%) sets using a stratified technique. A U-Net model for prostate segmentation was trained and tested on a separate annotated prostate T2W-MRI dataset (DS2) of 225 patients from our institution. The U-Net model was then used to segment the whole prostate gland on the MRI images of DS1, and the segmented images were fed into four 2D convolutional neural networks (CNNs) using different network architectures and regularization techniques (VGG blocks with batch normalization, dropout, and max pooling layers) to predict BCR at 5 years. The CNNs were evaluated using the area under the receiver operating characteristic curve (AUC) on the test set. For benchmarking, three machine learning classifiers (Random Forest, Logistic Regression, and Support Vector Machines) were developed using the 5 most important features selected by Mean Decrease in Impurity from a set of 18 clinical variables. A total of 189 patients were included in DS1. Androgen deprivation therapy (ADT) was received by 83.6% of patients. BCR was identified in 26% of the cases. The Dice score for the U-Net segmentation model was 78% on the test set of DS2. The AUC achieved by the different CNNs for predicting BCR ranged between 0.53 and 0.75. The best performing CNN consisted of 3 convolutional layers, the first two followed by max-pooling layers, a flattening layer, a dense layer, and an output layer with softmax activation function. The best clinical model was a Random Forest algorithm with an AUC of 0.70. The selected clinical variables by decreasing feature importance were: age, time to nadir PSA, pre-treatment PSA, percentage of positive biopsy cores at diagnosis, and nadir PSA. We developed a deep learning model based on pre-treatment T2W-MRI to predict BCR at 5 years following radical RT for intermediate and high-risk LPCa. This CNN outperformed a model based on clinical variables and warrants further validation in external cohorts.

A Phase I/II Dose-Escalation Study Evaluating the Safety of 21 Gy, 23 Gy, and 25 Gy for High Dose Rate (HDR) Prostate Brachytherapy: An Interim Toxicity Report.

Single-fraction high dose rate (HDR) prostate brachytherapy has shown improved actuarial biochemical control rates from 66% to 82% with dose escalation from 19 to 20.5 Gy, respectively, but is still inferior to the reported low dose rate brachytherapy control rates of over 90%. We aimed to identify whether dose escalation to 21, 23, and 25 Gy can be safely accomplished. Patients with previously untreated, pathologically confirmed, low-risk (cT1-T2a, Gleason ≤6, PSA <10 ng/mL) or favorable intermediate risk (Gleason 3+4, percentage of positive biopsy cores <50%, ≤1 NCCN intermediate risk factor) prostate adenocarcinoma were enrolled from a single institution. PSA and toxicity assessment were performed at baseline and at routine 6-month follow-ups. From May 9, 2018 to May 12, 2022, 18 patients were enrolled. None had received prior androgen deprivation therapy, 44% had low risk disease, and 61% were ECOG 0 at baseline. Median age was 68 years (total range: 43-79), and 83% were Caucasian. Eight patients received 21 Gy, nine patients received 23 Gy, and one patient received 25 Gy, with the 25 Gy cohort still accruing. The mean prostate size and range, determined by pre-operative prostate volume study on trans-rectal ultrasound, were as follows: 21Gy cohort (mean: 41.2cc; range: 21.9-63.1cc), 23Gy Cohort (mean: 41.2cc, range 28.3-71.7cc), 25Gy cohort (65.3cc). A median of 17 catheters (range: 16-20) were implanted. At a median follow-up of 35.7 months (range: IQR 4.4 - 50.2), only one grade 3 toxicity was reported, which was an ulcerative colitis (UC) flare noted in a patient whose UC was poorly controlled, requiring multiple courses of prednisone in the 6 months prior to his brachytherapy. Regarding toxicities attributable to therapy, fourteen and four patients experienced a grade 1 and 2 genitourinary toxicity respectively; four and four patients experienced a grade 1 and 2 reproductive system toxicity respectively; one patient experienced a grade 1 GI toxicity. Two patients needed foley catheters upon discharge, with neither requiring the foley long term. There were two treatment failures in the 21Gy cohort at 1.39 and 1.67 years from date of HDR brachytherapy; date of failure was defined by the first PSA 2.0ng/mL over nadir. Of these patients, one underwent focal salvage HDR, whilst the other underwent radical prostatectomy with only 1% of the volume involved by prostate cancer. Of note, there was seminal vesicle involvement on restaging prostate biopsy for the patient that underwent radical prostatectomy, however, this was not appreciated on the final prostatectomy specimen. Both patients have no evidence of disease currently. HDR brachytherapy appears well tolerated in patients with low to favorable intermediate risk prostate cancer at 21 and 23 Gy, with more accrual needed at 25Gy. Long-term follow-up needed to assess efficacy.

Machine Learning and Explainable Artificial Intelligence to Predict Occult Pelvic Nodal Metastases in Prostate Cancer.

Determination of risk of occult pelvic lymph node involvement (LNI) in patients with cN0 prostate cancer is critical for determination of optimal treatment options. Though several nomograms exist, machine learning (ML) approaches might enable physicians to better assess individual risk by incorporating multiple clinical risk factors. Herein, we developed a ML model to predict occult LNI, and explained its composition using an explainable artificial intelligence (XAI) framework. Patients with cN0 prostate adenocarcinoma diagnosed from 2018-2020 were identified in the National Cancer Database. The query was limited to patients with known clinical staging and biopsy results who did not receive neoadjuvant therapy prior to pelvic nodal examination. Occult LNI was defined as pN1 disease based on surgical evaluation, with a minimum of 10 nodes examined. Five ML models were trained to predict LNI. Variables incorporated into the model were age, core biopsy results, Gleason scores, preoperative prostate specific antigen (PSA), and clinical T-stage. Model performance, measured using area under the receiver operator characteristic curve (AUC) on a holdout testing dataset, was compared to multivariable logistic regression. The best-performing model was explained using SHapley Additive exPlanation (SHAP) values. To permit more clinically-meaningful statistical interpretation, using a novel approach SHAP values were converted into odds ratios (OR), confidence intervals (CI), and p-values. A total of 23,131 patients met inclusion criteria; 2,676 (11.6%) had occult LNI. The Extreme Gradient Boosting model outperformed all other models with an AUC of 0.82 (95% CI: 0.78-0.86) compared to 0.80 (95% CI: 0.76-0.84) for logistic regression. Increasing PSA (OR: 1.031; p<0.001), number of positive biopsy cores (OR: 1.055; p<0.001), and percent positive biopsy cores (OR: 1.01; p<0.001) were all associated with increased risk of LNI. Based on observation of SHAP dependence plots, risk of LNI plateaued at PSA>20 ng/dL and >11 positive cores, while no plateau was observed for percent positive biopsy cores. Relative to T1c disease, patients with T3b were at highest risk of LNI (OR: 1.461; p = 0.003). Gleason score of 9 was associated with significant risk of LNI (Ref: Gleason 6; OR: 1.891; p<0.001). This was primarily driven by the primary Gleason score; primary Gleason 5 disease was associated with significant risk of LNI (Ref: Gleason 3; OR: 1.915; p<0.001) while a secondary Gleason score of 5 was the only grade with significant increased risk of LNI (Ref: Gleason 3; OR: 1.185; p = 0.004). Age and number of cores examined were not significant predictors of LNI. Our ML achieved improved performance relative to logistic regression at predicting occult LNI. XAI provided insight into the inner-working of the ML model. ML can be used to identify patients at risk for occult LNI and therefore inform clinical decision-making.

PD39-06 INTEGRATING INDEX LESION VOLUME TO BETTER CLASSIFY MEN WITH INDOLENT PROSTATE CANCER AMONG PATIENTS WITH INTERMEDIATE RISK DISEASE. RESULTS FROM A LARGE, MULTI-INSTITUTIONAL SERIES

PD39-06 INTEGRATING INDEX LESION VOLUME TO BETTER CLASSIFY MEN WITH INDOLENT PROSTATE CANCER AMONG PATIENTS WITH INTERMEDIATE RISK DISEASE. RESULTS FROM A LARGE, MULTI-INSTITUTIONAL SERIES

The percentage of positive biopsy cores in predicting biochemical recurrence and adverse pathology in prostate cancer patients after radical prostatectomy: a systematic review and meta-analysis

Background: Biochemical recurrence (BCR) occurs in more than one-third of prostate cancer (PCa) patients 10 years after radical prostatectomy. The percentage of positive biopsy cores (PPBC) obtained from prostate needle biopsy is suggested as one of the predictors of BCR. We aim to investigate the role of PPBC in predicting BCR and adverse pathology in PCa patients after RP. Methods: A systematic search was conducted based on PRISMA guidelines from Pubmed, Scopus, and Cochrane Library databases up to July 2022. We screened studies that met our inclusion criteria and NOS (Newcastle-Ottawa Scale) was utilized as the quality assessment tool. The primary outcome was BCR measured as Hazard Ratios (HRs). The secondary outcome was adverse pathology, including positive surgical margin (PSM), Extra-prostatic disease (EPD), and seminal vesicle invasion (SVI). Review Manager®5.4 was used as the statistical analysis tool. Results: A total of 5971 patients were included from eleven eligible studies with overall good quality scores. Eleven studies were included in the qualitative synthesis and five of them were analyzed in the meta-analysis. The pooled analysis demonstrated that higher PPBC has a 2.77 times risk of BCR (OR 2.77 (95% CI: 1.97, 3.9; p&lt;0.00001) after RP. Similarly, it has significant results in SVI (OR 2.61 (95% CI: 1.19, 5.73; p=0.02). However, there were insignificant results in terms of EPD (p=0.17) and PSM (p=0.33). Conclusion: This systematic review and meta-analysis (SRMA) indicate that a high PPBC is strongly correlated with a greater risk of BCR and SVI, but not EPD and PSM in patients following RP.

Metabolic syndrome and its components predict the biochemical recurrence and adverse pathological features of patients following radical prostatectomy: a propensity score matching study

BackgroundTo investigate the predictive value of metabolic syndrome (MetS) and its components in biochemical recurrence (BCR) and adverse pathological features of patients with prostate cancer (PCa) after radical prostatectomy (RP).MethodsA total of 525 PCa patients who underwent RP between 2010 and 2019 at Peking University Third Hospital were analyzed retrospectively. The Kaplan–Meier method was performed to assess BCR-free survival (BCRFS). Univariate and multivariate Cox regression models and multivariate logistic regression models were conducted to identify the predictive factors of BCRFS and adverse pathological features respectively before and after propensity score matching (PSM).ResultsEnrolled patients were allocated into MetS group (n = 136) and non-MetS group (n = 389) according to the presence or absence of MetS, and 127 new matched pairs were identified to balance the baseline characteristics after 1:1 PSM. In propensity matched patients, the Kaplan–Meier analysis revealed that MetS (P = 0.020), hyperglycemia (P = 0.015) and hypertriglyceridemia (P = 0.001) were significantly associated with worse BCRFS; the results of multivariate Cox analyses showed that hyperglycemia (P = 0.040), hypertriglyceridemia (P = 0.017), percentage of positive biopsy cores (P = 0.041) and prostate specific antigen (P = 0.019) were identified as independent prognostic factors for BCRFS. In addition, hypertriglyceridemia was independently associated with non-organ confined disease (NOCD) (P = 0.010), extra-capsular extension (ECE) (P = 0.010) and upgrading (P = 0.017) in the multivariate logistic analyses.ConclusionsHyperglycemia and hypertriglyceridemia are the two effective MetS components both identified as independent risk factors for worse BCRFS after RP, while hypertriglyceridemia was independently associated with NOCD, ECE and upgrading as well.

3D-Reconstructed Contact Surface Area and Tumour Volume on Magnetic Resonance Imaging Improve the Prediction of Extraprostatic Extension of Prostate Cancer.

This study is to determine whether the volume and contact surface area (CSA) of a tumour with an adjacent prostate capsule on MRI in a three-dimensional (3D) model that can predict side-specific extraprostatic extension (EPE) at radical prostatectomy (RP). Patients with localised prostate cancer (PCa) who underwent robot-assisted RP between July 2015 and March 2021 were included in this retrospective study. MRI-based 3D prostate models incorporating the PCa volume and location were reconstructed. The tumour volume and surface variables were extracted. For the prostate-to-tumour and tumour-to-prostate CSAs, the areas in which the distances were ≤ 1, ≤ 2, ≤ 3, ≤ 4, and ≤ 5mm were defined, and their surface (cm2) were determined. Differences in prostate sides with and without pathological EPE were analysed. Multivariable logistic regression analysis to find independent predictors of EPE. Overall, 75/302 (25%) prostate sides showed pathological EPE. Prostate sides with EPE had higher cT-stage, higher PSA density, higher percentage of positive biopsy cores, higher biopsy Gleason scores, higher radiological tumour stage, larger tumour volumes, larger prostate CSA, and larger tumour CSA (all p < 0.001). Multivariable logistic regression analysis showed that the radiological tumour stage (p = 0.001), tumour volume (p < 0.001), prostate CSA (p < 0.001), and tumour CSA (p ≤ 0.001) were independent predictors of pathological EPE. A 3D reconstruction of tumour locations in the prostate improves prediction of extraprostatic extension. Tumours with a higher 3D-reconstructed volume, a higher surface area of tumour in contact with the prostate capsule, and higher surface area of prostate capsule in contact with the tumour are at increased risk of side-specific extraprostatic extension.

Potential Role of Seven Proteomics Tissue Biomarkers for Diagnosis and Prognosis of Prostate Cancer in Urine.

As the currently available tests for the clinical management of prostate cancer (PCa) are still far from providing precise diagnosis and risk stratification, the identification of new molecular marker(s) remains a pertinent clinical need. Candidate PCa biomarkers from the published proteomic comparative studies of prostate tissue (2002-2020) were collected and systematically evaluated. AZGP1, MDH2, FABP5, ENO1, GSTP1, GSTM2, and EZR were chosen for further evaluation in the urine of 85 PCa patients and controls using ELISA. Statistically significant differences in protein levels between PCa and BPH showed FABP5 (p = 0.019) and ENO1 (p = 0.015). A biomarker panel based on the combination of FABP5, ENO1, and PSA provided the highest accuracy (AUC = 0.795) for PCa detection. The combination of FABP5, EZR, AZGP1, and MDH2 showed AUC = 0.889 in PCa prognosis, with 85.29% of the samples correctly classified into low and high Gleason score (GS) groups. The addition of PSA to the panel slightly increased the AUC to 0.914. AZGP1, FABP5, and EZR showed significant correlation with GS, stage, and percentage of positive biopsy cores. Although validation using larger patient cohorts will be necessary to establish the credibility of the proposed biomarker panels in a clinical context, this study opens a way for the further testing of more high-quality proteomics biomarkers, which could ultimately add value to the clinical management of PCa.

Predicting the probability of pT3 or higher pathological stage at radical prostatectomy: COVID19-specific considerations.

We tested whether a model identifying prostate cancer (PCa) patients at risk of pT3-4/pN1 can be developed for use during COVID19 pandemic, in order to guarantee appropriate treatment to patients harboring advanced disease patients without compromising sustainability of care delivery. Within the Surveillance, Epidemiology and End Results database 2010-2016, we identified 27,529 patients with localized PCa and treated with radical prostatectomy. A multivariable logistic regression model predicting presence of pT3-4/pN1 disease was fitted within a development cohort (n=13,977, 50.8%). Subsequently, external validation (n=13,552, 49.2%) and head-to-head comparison with NCCN risk group stratification was performed. In model development, age, PSA, biopsy Gleason Grade Group (GGG) and percentage of positive biopsy cores were independent predictors of pT3-4/pN1 stage. In external validation, prediction of pT3-4/pN1 with novel nomogram was 74% accurate versus 68% for NCCN risk group stratification. Nomogram achieved better calibration and showed net-benefit over NCCN risk group stratification in decision curve analyses. The use of nomogram cut-off of 49% resulted in pT3-4/pN1 rate of 65%, instead of the average 35%. The newly developed, externally validated nomogram predicts presence of pT3-4/pN1 better than NCCN risk group stratification and allows to focus radical prostatectomy treatment on individuals at highest risk of pT3-4/pN1.

Long-term outcomes of radical prostatectomy versus low-dose-rate brachytherapy in patients with intermediate-risk prostate cancer: Propensity score matched comparison.

To compare long-term outcomes of radical prostatectomy (RP) and low-dose-rate brachytherapy (LDR-BT) using propensity score-matched analysis in patients with clinically localized, intermediate-risk prostate cancer (PCa). Between October 2003 and March 2014, our institution treated 1241 patients with intermediate-risk PCa (RP: n = 531; LDR-BT: n = 710). Biochemical recurrence (BCR) was defined as prostate-specific antigen (PSA) levels of 0.2 ng/ml or greater for RP, and as PSA nadir plus 2 ng/ml or higher (Phoenix definition) for LDR-BT. We calculated propensity scores by multivariate logistic regression based on covariates that included age, pretreatment PSA, biopsy Gleason grade, the percentage of positive biopsy cores (PPBC), and clinical T stage. Median follow-up was 108 months for RP and 99 months for LDR-BT. After propensity score adjustment, a total of 642 (321 each) patients remained for further analysis. Kaplan-Meier curves showed no statistically significant difference in overall survival (OS) (p = 0.99). LDR-BT was associated with improved BCR-free survival and salvage therapy-free survival compared to RP (p < 0.001), and RP was associated with improved metastasis-free survival (MFS, p < 0.001). BCR cannot be a surrogate for survival comparison, primarily due to differences between treatment modalities in how this term was defined post-therapy. Long-term follow-up showed that RP was associated with lower MFS in intermediate-risk PCa. However, this has not yet translated into superior OS.

MRI Extraprostatic Extension Grade: Accuracy and Clinical Incremental Value in the Assessment of Extraprostatic Cancer.

Objective The purpose was to compare the accuracy of extraprostatic extension (EPE) grade on MRI predicting EPE with Partin tables, Memorial Sloan Kettering Cancer Center nomogram (MSKCCn), and combined models and to analyze the clinical incremental value of EPE grade. Materials and Methods 105 prostate cancer patients confirmed by pathology after radical prostatectomy in our hospital from 2017 to 2021 were selected. The clinical stage, PSA, Gleason score, number of positive biopsy cores, and percentage of positive biopsy cores were recorded. Evaluate EPE grade according to EPE grade criteria, and calculate the probability of predicting EPE with Partin tables and MSKCCn. EPE grade is combined with Partin tables and MSKCCn to construct EPE grade+Partin tables and EPE grade+MSKCCn models. Calculate the area under the curve (AUC), sensitivity, and specificity of EPE grade, Partin tables, MSKCCn, EPE grade+Partin tables, and EPE grade+MSKCCn and compare their diagnostic efficacy. The clinical decision curve was used to analyze the clinical net income of each prediction scheme. Results The AUC of EPE grade was 0.79, Partin tables was 0.50, MSKCCn was 0.78, the EPE grade+Partin table model was 0.79, and the EPE grade+MSKCCn model was 0.83. After EPE grade was combined with Partin tables and MSKCCn, the diagnostic efficiency of clinical model was significantly improved (P < 0.05). There was no significant difference in the diagnostic efficacy of the combined model compared with the single EPE grade (P > 0.05). The calibration curve of the combined model shows that it has a good calibration degree for EPE. In the analysis of the decision curve, the net income of the EPE grade is higher than that of Partin tables and MSKCCn and is equal to the EPE grade+Partin tables and is slightly lower than that of EPE grade+MSKCCn. The clinical net income of the combined model is obviously higher than that of individual clinical models. Conclusion The accuracy of EPE classification in predicting prostate cancer EPE is high, and combined with the clinical model, it can significantly improve the diagnostic efficiency of the clinical model and increase the clinical benefit.

Prostate cancer grade downgrading at time of prostatectomy provides risk-stratification insight into future tumor behavior after prostatectomy.

Prostate biopsy (Bx) sampling-based diagnosis of prostate cancer (PCa) has well-described inaccuracy when compared against whole gland analysis upon prostatectomy. Although upgrading of PCa Grade Group (GG) is often described, the occurrence and prognostic implications of downgrading PCa GG at the time of radical prostatectomy (RP) is less understood. Our objective was to evaluate whether downgrading PCa GG at the time of RP was associated with future tumor behavior. The SEER database was searched from 2010 to 2017 and patients were included if they were assigned pathological grades on both Bx and RP specimen. Patients were stratified into Bx GG > RP GG and Bx GG ≤ RP GG groups, and tumor behavior after treatment was examined. Cox regression was used for the survival analysis. Here, 99,835 patients were included in this study. A total of 18,516 (18.5%) patients encountered downgrading from Bx GG to RP GG. A downgrading of 1 grade occurred in 13,969 (75.4%) of these patients and of 2 or more grades occurred in 4547 (24.6%) patients. A history of higher Bx GG compared with RP GG increased the risk of cancer-specific mortality (CSM) for each given RP GG controlling for age, race, preop prostate-specific antigen level, percentage of positive biopsy cores, and pathologic TNM stages. Specifically, a history of high Bx GG conferred a 45% increased risk of CSM for any given RP GG (hazard ratio = 1.45 95% confidence interval = 1.16-1.82, p < 0.001). A history of higher Bx GG, and hence downgrading at the time of RP, demonstrates some value as a risk-stratification tool for future cancer outcomes after prostatectomy.

External validation of the Candiolo nomogram for high-risk prostate cancer patients treated with carbon ion radiotherapy plus androgen deprivation therapy: a retrospective cohort study.

The aim of this study was to reclassify high-risk prostate cancer patients treated with carbon-ion radiotherapy and androgen deprivation therapy using the Candiolo nomogram and evaluate usefulness to predict the following 10-year biochemical recurrence. Six hundred seventy-two high-risk prostate cancer patients were reclassified according to the Candiolo nomogram. The cumulative incidence curves for biochemical recurrence were compared by Gray's test. Furthermore, five predictors of the Candiolo nomogram in our patients were evaluated by Fine and Gray regression hazards model. The higher the Candiolo risk, the worse the biochemical recurrence, especially in high- and very high-risk patients. Out of five predictors, age≥70years, cT3 stage, biopsy Gleason score≥9 or the percentage of positive biopsy cores ≥50% had significant impacts on 10-year biochemical recurrence in our patients. The Candiolo nomogram can reclassify our high-risk prostate cancer patients treated with carbon-ion radiotherapy and androgen deprivation therapy and evaluate the biochemical recurrence preciously.

Contemporary seminal vesicle invasion rates in NCCN high-risk prostate cancer patients.

BackgroundContemporary seminal vesicle invasion (SVI) rates in National Cancer Comprehensive Network (NCCN) high‐risk prostate cancer (PCa) patients are not well known but essential for treatment planning. We examined SVI rates according to individual patient characteristics for purpose of treatment planning.Materials and MethodsWithin Surveillance, Epidemiology, and End Results (SEER) database (2010–2015), 4975 NCCN high‐risk patients were identified. In the development cohort (SEER geographic region of residence: South, North‐East, Mid‐West, n = 2456), we fitted a multivariable logistic regression model predicting SVI. Its accuracy, calibration, and decision curve analyses (DCAs) were then tested versus previous models within the external validation cohort (SEER geographic region of residence: West, n = 2519).ResultsOut of 4975 patients, 28% had SVI. SVI rate ranged from 8% to 89% according to clinical T stage, prostate‐specific antigen (PSA), biopsy Gleason Grade Group and percentage of positive biopsy cores. In the development cohort, these variables were independent predictors of SVI. In the external validation cohort, the current model achieved 77.6% accuracy vs 73.7% for Memorial Sloan Kettering Cancer Centre (MSKCC) vs 68.6% for Gallina et al. Calibration was better than for the two alternatives: departures from ideal predictions were 6.0% for the current model vs 9.8% for MSKCC vs 38.5% for Gallina et al. In DCAs, the current model outperformed both alternatives. Finally, different nomogram cutoffs allowed to discriminate between low versus high SVI risk patients.ConclusionsMore than a quarter of NCCN high‐risk PCa patients harbored SVI. Since SVI positivity rate varies from 8% to 89%, the currently developed model offers a valuable approach to distinguish between low and high SVI risk patients.

Impact of clinical staging based on DRE versus imaging (TRUS/MRI) in localized prostate cancer risk assessment.

227 Background: The Cancer of the Prostate Risk Assessment (CAPRA) score is a validated risk assessment tool for biochemical recurrence (BCR) risk after radical prostatectomy (RP) for localized prostate cancer. The score is calculated using five clinical variables: age, PSA at diagnosis, Gleason score pattern, percentage of positive biopsy cores, and clinical stage by digital rectal exam (DRE). Since its publication in 2005, transrectal ultrasonography (TRUS) and magnetic resonance imaging (MRI) have increasingly been used in clinical evaluation but have yet to be integrated into prostate cancer tumor staging rules or risk-stratification models. We incorporated both clinical- and imaging-derived stage into the CAPRA score to assess the association between the base and imaging-adjusted CAPRA model and the outcome of BCR. Methods: We performed a retrospective analysis of patients who underwent RP for prostate cancer staged ≤T3a on both DRE and imaging (TRUS/MRI) and diagnosed between 2000-2017 at our institution. DRE was treated as a binary variable (T1 vs ≥T2) as was imaging (≤T2 vs T3a). BCR was defined as 2 consecutive PSA ≥0.2 ng/ml starting 8 weeks post-surgery or receipt of salvage treatment. Two CAPRA scores were computed: one with DRE-derived and one with image-based T-stage. Life table estimates were used to evaluate likelihood of BCR-free survival after RP stratified by CAPRA scores. We performed Cox regression to examine the association between clinical CAPRA components and BCR risk and to determine whether stratifying clinical T1, T2, and T3a disease in CAPRA was independently associated with BCR risk on multivariate analysis. Results: Of 1972 patients studied, 103 (5%) had upstaging in CAPRA risk group with imaging-reported clinical stage. Life table estimates were comparable between DRE- and imaging-based CAPRA score with regard to likelihood of BCR-free survival. In separate unadjusted Cox regression models, DRE-based (HR 1.57; 95% CI 1.50-1.64) and imaging-based clinical CAPRA (HR 1.56; 95% CI 1.50-1.63) were both independently associated with risk of recurrence after RP and model fit did not differ significantly. On multivariable Cox regression adjusted for CAPRA component variables, clinical stage ≥T2 on DRE (HR 1.28; 95% CI 1.06-1.54) and T3a on imaging (HR 1.79; 95% CI 1.47-2.19) were independently associated with BCR risk. Conclusions: Both DRE- and imaging-based clinical CAPRA nomograms are associated with risk of BCR after RP for prostate cancer. On multivariable analysis, both modalities of clinical staging are independently associated with CAPRA score on the risk assessment nomogram. Each staging modality offers independent prognostic information, which supports the continued use of DRE-based clinical staging in prostate cancer risk stratification and demonstrates the potential benefit imaging-based clinical stage may add to these models and to tumor staging standards.

Validation of models predicting lymph node involvement probability in patients with prostate cancer.

There are many models to predict lymph node involvement in patients with prostate cancer. We aimed to externally validate several models in a Japanese cohort. We considered patients who were treated with robotic-assisted radical prostatectomy with extended pelvic lymph node dissection for prostate cancer. The risk of lymph node involvement was calculated for each patient in several models. Model performance was assessed by calculating the receiver operating characteristic curve and the area under the curve, calibration plots, and decision curve analyses. We identified lymph node involvement in 61 (18.4%) of the 331 considered patients. Patients with lymph node involvement had a higher prostate-specific antigen level, percentage of positive biopsy cores, primary Gleason grade, Gleason group grade, and clinical T-stage category. The Memorial Sloan Kettering Cancer Center web calculator presented the highest area under the curve (0.78) followed by the Yale formula area under the curve (0.77), the updated version of Briganti nomogram of 2017 area under the curve (0.76), and the updated version of the Partin table by Tosoian etal. had an area under the curve of 0.75. However, the 95% confidence interval for these models overlapped. The calibration plot showed that the Memorial Sloan Kettering Cancer Center web calculator and the updated version of the Briganti nomogram calibrated better. In the decision curve analyses, all models showed net benefit; however, it overlapped among them. However, the Memorial Sloan Kettering Cancer Center web calculator and the updated Briganti nomogram presented the highest net benefit for lymph node involvement risks <35%. Models predicting lymph node involvement were externally validated in Japanese men. The Memorial Sloan Kettering Cancer Center web calculator and the updated Briganti nomogram of 2017 were the most accurate performing models.