Part Of Table Research Articles

Extracapsular extension risk assessment using an artificial intelligence prostate cancer mapping algorithm

AbstractObjectiveThe objective of this study is to compare detection rates of extracapsular extension (ECE) of prostate cancer (PCa) using artificial intelligence (AI)‐generated cancer maps versus MRI and conventional nomograms.Materials and methodsWe retrospectively analysed data from 147 patients who received MRI‐targeted biopsy and subsequent radical prostatectomy between September 2016 and May 2022. AI‐based software cleared by the United States Food and Drug Administration (Unfold AI, Avenda Health) was used to map 3D cancer probability and estimate ECE risk. Conventional ECE predictors including MRI Likert scores, capsular contact length of MRI‐visible lesions, PSMA T stage, Partin tables, and the “PRedicting ExtraCapsular Extension” nomogram were used for comparison.Postsurgical specimens were processed using whole‐mount histopathology sectioning, and a genitourinary pathologist assessed each quadrant for ECE presence. ECE predictors were then evaluated on the patient (Unfold AI versus all comparators) and quadrant level (Unfold AI versus MRI Likert score). Receiver operator characteristic curves were generated and compared using DeLong's test.ResultsUnfold AI had a significantly higher area under the curve (AUC = 0.81) than other predictors for patient‐level ECE prediction. Unfold AI achieved 68% sensitivity, 78% specificity, 71% positive predictive value, and 75% negative predictive value. At the quadrant level, Unfold AI exceeded the AUC of MRI Likert scores for posterior (0.89 versus 0.82, p = 0.003), anterior (0.84 versus 0.80, p = 0.34), and all quadrants (0.89 versus 0.82, p = 0.002). The false negative rate of Unfold AI was lower than MRI in both the anterior (−60%) and posterior prostate (−40%).ConclusionsUnfold AI accurately predicted ECE risk, outperforming conventional methodologies. It notably improved ECE prediction over MRI in posterior quadrants, with the potential to inform nerve‐spare technique and prevent positive margins. By enhancing PCa staging and risk stratification, AI‐based cancer mapping may lead to better oncological and functional outcomes for patients.

Alan Partin, 1961–2023

Alan Partin – a leading figure in international urology and publisher of the Partin tables.

Head-to-head comparison between microUS, the partin tables and memorial sloan kettering cancer center nomogram in predicting extraprostatic disease in patients undergoing robot-assisted radical prostatectomy

Head-to-head comparison between microUS, the partin tables and memorial sloan kettering cancer center nomogram in predicting extraprostatic disease in patients undergoing robot-assisted radical prostatectomy

Contemporary Pathological Stage Distribution After Radical Prostatectomy in North American High-Risk Prostate Cancer Patients.

To investigate pathological stage at radical prostatectomy (RP) using the "Partin tables" approach in NCCN high-risk (HR) prostate cancer (PCa) patients. Within the SEER 2010 to 2016 database, we identified 7,718 NCCN HR PCa patients. Cross-tabulation was used to illustrate the distribution of organ confined disease (OC, pT2), extra-prostatic extension (EPE, pT3a), seminal vesicles invasion (SVI, pT3b), lymph node invasion (LNI, pT2N1), extra-prostatic and lymph node invasion (EPE+LNI, pT3aN1), and seminal vescicale and lymph node invasion (SVI+LNI, pT3bN1), according to preoperative criteria, which consisted in PSA, clinical T stage, biopsy Gleason Score (GS). Binomial 95%CI was constructed for the reported proportions. Median (IQR) PSA levels was 9 (6-20) ng/ml. The majority of patient harbored cT1c (51%) followed by cT2 (35%) and cT3 (14%) stage. Most patients exhibited GS 4+4 (43%). Overall, 87 vs. 15 vs. 2% of patients harbored only 1 vs. 2 vs. all 3 HR criteria. At RP, OC, EPE, SVI, and LNI rates were respectively 36%, 27%, 17%, and 19%. Highest levels of OC were recorded for cT1c, PSA <10 ng/mL and biopsy GS4+4. Conversely, EPE, SVI and LNI were the highest in patients with cT3, PSA ≥20 ng/mL and GS 5+5. After stratification according to clinical stages, OC rates decreased with increasing PSA levels and GS. Conversely, EPE, SVI and LNI rates increased with increasing PSA and GS. We provide a lookup table to illustrate the relationship between clinical and pathological characteristics in NCCN HR PCa patients.

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.

Performance evaluation of the Partin Table (2007), in a current ecuadorian sample

Background: Partin tables made from a 2007 cohort in a North American population are regularly used to predict the extent of prostate cancer after radical prostatectomy. There is no study that evaluated this table in the Ecuadorian population. Objective: To determine the usefulness in the prediction made by Partin tables in Ecuadorian patients with prostate cancer who underwent radical prostatectomy. Materials and methods: A descriptive, cross-sectional, quantitative and retrospective study was designed, the latter being its limitation. The records of 119 patients with prostate cancer who underwent radical prostatectomy during the period from 2010 to 2020 were reviewed. The area under the curve (AUC) was calculated using the ROC curve, and the result for extraprostatic extension (EEP) was compared. , infiltration of seminal vesicles (IVS) and lymph nodes (INL) with the Partin 2007 cohort. Results: The ROC curve for EEP showed an AUC 0.54 (0.43-0.65), for ISV 0.60 (0.44-0-76) with low sensitivity and specificity, while for INL an AUC of 0.73 (0.59-0.86) with acceptable sensitivity but low specificity. Conclusions: Partin Tables did not predict pathological stages for EPP and ISV; however, for INL it can be used with caution.

Validation of user-friendly models predicting extracapsular extension in prostate cancer patients

ObjectiveThere are many models to predict extracapsular extension (ECE) in patients with prostate cancer. We aimed to externally validate several models in a Japanese cohort. MethodsWe included patients treated with robotic-assisted radical prostatectomy for prostate cancer. The risk of ECE was calculated for each patient in several models (prostate side-specific and non-side-specific). Model performance was assessed by calculating the receiver operating curve and the area under the curve (AUC), calibration plots, and decision curve analyses. ResultsWe identified ECE in 117 (32.9%) of the 356 prostate lobes included. Patients with ECE had a statistically significant higher prostate-specific antigen level, percentage of positive digital rectal examination, percentage of hypoechoic nodes, percentage of magnetic resonance imaging nodes or ECE suggestion, percentage of biopsy positive cores, International Society of Urological Pathology grade group, and percentage of core involvement. Among the side-specific models, the Soeterik, Patel, Sayyid, Martini, and Steuber models presented AUC of 0.81, 0.78, 0.77, 0.75, and 0.73, respectively. Among the non-side-specific models, the memorial Sloan Kettering Cancer Center web calculator, the Roach formula, the Partin tables of 2016, 2013, and 2007 presented AUC of 0.74, 0.72, 0.64, 0.61, and 0.60, respectively. However, the 95% confidence interval for most of these models overlapped. The side-specific models presented adequate calibration. In the decision curve analyses, most models showed net benefit, but it overlapped among them. ConclusionModels predicting ECE were externally validated in Japanese men. The side-specific models predicted better than the non-side-specific models. The Soeterik and Patel models were the most accurate performing models.

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.

Prostate Magnetic Resonance Imaging Analyses, Clinical Parameters, and Preoperative Nomograms in the Prediction of Extraprostatic Extension.

Introduction: Proper planning of laparoscopic radical prostatectomy (RP) in patients with prostate cancer (PCa) is crucial to achieving good oncological results with the possibility of preserving potency and continence. Aim: The aim of this study was to identify the radiological and clinical parameters that can predict the risk of extraprostatic extension (EPE) for a specific site of the prostate. Predictive models and multiparametric magnetic resonance imaging (mpMRI) data from patients qualified for RP were compared. Material and methods: The study included 61 patients who underwent laparoscopic RP. mpMRI preceded transrectal systematic and cognitive fusion biopsy. Martini, Memorial Sloan-Kettering Cancer Center (MSKCC), and Partin Tables nomograms were used to assess the risk of EPE. The area under the curve (AUC) was calculated for the models and compared. Univariate and multivariate logistic regression analyses were used to determine the combination of variables that best predicted EPE risk based on final histopathology. Results: The combination of mpMRI indicating or suspecting EPE (odds ratio (OR) = 7.49 (2.31–24.27), p < 0.001) and PSA ≥ 20 ng/mL (OR = 12.06 (1.1–132.15), p = 0.04) best predicted the risk of EPE for a specific side of the prostate. For the prediction of ipsilateral EPE risk, the AUC for Martini’s nomogram vs. mpMRI was 0.73 (p < 0.001) vs. 0.63 (p = 0.005), respectively (p = 0.131). The assessment of a non-specific site of EPE by MSKCC vs. Partin Tables showed AUC values of 0.71 (p = 0.007) vs. 0.63 (p = 0.074), respectively (p = 0.211). Conclusions: The combined use of mpMRI, the results of the systematic and targeted biopsy, and prostate-specific antigen baseline can effectively predict ipsilateral EPE (pT3 stage).

Development and Validation of Models to Predict Pathological Outcomes of Radical Prostatectomy in Regional and National Cohorts.

Prediction models are recommended by national guidelines to support clinical decision making in prostate cancer. Existing models to predict pathological outcomes of radical prostatectomy (RP)-the Memorial Sloan Kettering (MSK) models, Partin tables, and the Briganti nomogram-have been developed using data from tertiary care centers and may not generalize well to other settings. Data from a regional cohort (Michigan Urological Surgery Improvement Collaborative [MUSIC]) were used to develop models to predict extraprostatic extension (EPE), seminal vesicle invasion (SVI), lymph node invasion (LNI), and nonorgan-confined disease (NOCD) in patients undergoing RP. The MUSIC models were compared against the MSK models, Partin tables, and Briganti nomogram (for LNI) using data from a national cohort (Surveillance, Epidemiology, and End Results [SEER] registry). We identified 7,491 eligible patients in the SEER registry. The MUSIC model had good discrimination (SEER AUC EPE: 0.77; SVI: 0.80; LNI: 0.83; NOCD: 0.77) and was well calibrated. While the MSK models had similar discrimination to the MUSIC models (SEER AUC EPE: 0.76; SVI: 0.80; LNI: 0.84; NOCD: 0.76), they overestimated the risk of EPE, LNI, and NOCD. The Partin tables had inferior discrimination (SEER AUC EPE: 0.67; SVI: 0.76; LNI: 0.69; NOCD: 0.72) as compared to other models. The Briganti LNI nomogram had an AUC of 0.81 in SEER but overestimated the risk. New models developed using the MUSIC registry outperformed existing models and should be considered as potential replacements for the prediction of pathological outcomes in prostate cancer.

Comparative results of nerve-sparing robotic-assisted prostatectomy

Background. Considering decreasing age of patients with prostate cancer, increasing cancer alertness of first-line doctors as well as increased frequency of radical prostatectomies (RP), the problem of preservation of erectile function (EF) is vitally important (erectile dysfunction develops in 25–75 % of all patients who underwent surgery).The study objective is to analyze preservation of EF after RP depending on the type of endoscopic access and nerve preservation.Materials and methods. Between February of 2015 and February of 2016, in the Urology Clinic of the Sechenov University, 507 RPs were performed; the retrospective single-center study included 231 patients with localized prostate cancer. Surgery was performed with the following accesses: laparoscopic, extraperitoneal laparoscopic, and robotic. Indications for nerve preservation were formulated based on the Briganti nomogram, Partin table as well as patient’s desire to preserve EF. Further evaluation of EF was performed using the International Index of Erectile Function (IIEF5), evaluation of quality of life – using the QoL (Quality of Life) scale.Results. RP with nerve preservation was performed in 150 patients. Surgical time and blood loss did not significantly differ for surgeries with and without nerve preservation (р = 0.064 and р = 0.073 respectively). Pathomorphological examination showed that in all cases (n = 231) integrity of the prostatic capsule and negative surgical margin were achieved. Frequent significant erectile dysfunction and full loss of EF were observed in patients after RP without nerve preservation compared to the group with preserved neurovascular bundles (5.0 (0.0–10.0) points compared to 6.5 (0.8–19.0) points per the IIEF5 scale, р = 0.271): 96.2 % versus 72.2 % (p &lt;0.001). Nerve preservation significantly improved quality of life: 1.63 ± 1.16 points versus 1.88 ± 1.02 points per the QoL scale (р = 0.035).Conclusions. The best results were achieved in the robotic access group. Surgery with nerve preservation decreased frequency of EF loss. This benefit in conjunction with the radical nature of the operative intervention allows to consider RP techniques with nerve preservation as reasonable approach to erectile dysfunction prevention in patients with localized prostate cancer.

A Modified Partın Table to Better Predict Extracapsular Extensıon in Clinically Localized Prostate Cancer.

Prediction of extracapsular extension (ECE) before radical prostatectomy in clinically localized prostate cancer (PCa) is very important for clinical practice. ECE affects our decision on treatment strategy. The aim of this study is to identify the predictors of ECE, determine cut-off values, and compare them with the accuracy of Partin Table parameters to improve tumor staging in clinical practice. 374 patients with clinically localized PCa who underwent open radical retropubic prostatectomy (RRP) were included in this study. Gleason Score (GS), age, digital rectal examination (DRE), prostate specific antigen (PSA), prostate specific antigen density (PSAD), free PSA, Free/Total PSA, prostate volume (PV), number of cores involved, tumor length, and tumor percentage in maximum involved core in biopsy were investigated. PSAD, tumor percentage, and tumor length are predictive factors of ECE. The cut-off values of PSA, PSAD, maximum tumor length, and maximum tumor percentages in predicting ECE are: > 8.90 ng/mL, > 0.26 ng/mL2, >5mm, and >50%, respectively. The cut-off values for Partin extraprostatic extension (EPE) and organ confined (OC) disease are >29% and ≤ 64%, respectively. Partin tables could better predict extracapsular extension in clinically localized PCa if they include PSAD, tumor percentage, and tumor length. The cut-off values of these predictive factors can be beneficial in treatment strategies and in the decisions of lymphadenectomy and nerve-sparing surgery at radical prostatectomy.

Development and Validation of a Genomic Tool to Predict Seminal Vesicle Invasion in Adenocarcinoma of the Prostate.

Pretreatment estimates of seminal vesicle invasion (SVI) are challenging and significantly influence the management of prostate cancer. We sought to improve current models to predict SVI through the development of an SVI prediction genomic signature. A total of 15,889 patients who underwent radical prostatectomy (RP) with available baseline clinical, pathology, and transcriptome data were retrieved from the GRID registry (ClinicalTrials.gov identifier: NCT02609269) and other retrospective cohorts. These data were divided into a training (n = 6,766), test (n = 3,363), and two validation (n = 5,062 and 698) cohorts. Multivariable logistic regression was performed to assess the predictive effect of the genomic SVI (gSVI) classifier in the presence of established nomograms (Partin Tables and Memorial Sloan Kettering Cancer Center [MSKCC]). In the training cohort, univariable filtering identified 2,132 genes that were differentially expressed between RP tumors with and without SVI. Model parameters were tuned to maximize the area under the curve (AUC) in the testing cohort, resulting in a logistic generalized linear model with 581 genes. The gSVI model scores range from 0 to 1. In the first validation set, gSVI showed superior discrimination of patients with and without SVI at RP compared with other prognostic signatures trained to predict distant metastasis or clinical recurrence. Of the 698 patients in the second validation set, gSVI combined with the MSKCC nomogram had a superior AUC (0.86) compared with either nomogram individually (0.81). The gSVI represents a novel and validated expression signature to predict the presence of SVI before treatment with surgery. This genomic tool adds discriminatory power to existing clinical predictive nomograms and may help with pretreatment counseling and decision making.

External Validation of a Multiparametric Magnetic Resonance Imaging–based Nomogram for the Prediction of Extracapsular Extension and Seminal Vesicle Invasion in Prostate Cancer Patients Undergoing Radical Prostatectomy

The nomogram reported by Gandaglia et al (The key combined value of multiparametric magnetic resonance imaging, and magnetic resonance imaging-targeted and concomitant systematic biopsies for the prediction of adverse pathological features in prostate cancer patients undergoing radical prostatectomy. Eur Urol 2020;77:733–41) predicting extracapsular extension (ECE) or seminal vesicle invasion (SVI) has been developed using multiparametric magnetic resonance imaging (MRI) parameters and MRI-targeted biopsy. We aimed to validate this nomogram externally by analyzing 566 patients harboring prostate cancer diagnosed on MRI-targeted biopsy followed by radical prostatectomy. At final pathology, 37% and 12% patients had ECE and SVI, respectively. Performance of the nomogram, in comparison with the Memorial Sloan Kettering Cancer Center (MSKCC) model and Partin tables, was evaluated using discrimination, calibration, and decision curve analysis. Regarding ECE prediction, the nomogram showed higher discrimination (71.8% vs 69.8%, p = 0.3 and 71.8% vs 61.3%, p < 0.001), and similar miscalibration and net benefit for probability threshold above 30% when compared with MSKCC model and Partin tables, respectively. Performance of the nomogram with regard to SVI was comparable in terms of discrimination (68.5% vs 70.4% vs 67.8%, p ≥ 0.6), presenting a slight overestimation on calibration plots and a net benefit for probability threshold above 7.5%. This is the first multicentric study that externally validates a nomogram predicting ECE and SVI in patients diagnosed with MRI-targeted biopsy. Its performance was less optimistic than expected, and implementation of MRI in this setting was not associated with a clear improvement in patient selection and clinical usefulness when compared with available models. We proposed an updated version of the nomogram predicting ECE using the recalibration method, which leads to an improvement in its performance and needs to be validated in another external set. Patient summaryWe validate a prediction tool based on multiparametric magnetic resonance imaging (MRI) parameters and MRI-targeted biopsy predicting extracapsular extension and seminal vesicle invasion at radical prostatectomy. An improvement of patient selection was not clearly demonstrated when compared with available models based on clinical parameters, and implementation of MRI in this setting still needs to be clarified.

Predicting the Probability of Lymph Node Involvement with Prostate Cancer Nomograms: Can We Trust the Prediction Models?

Abstract Introduction Prediction of lymph node involvement (LNI) is of paramount importance for patients with prostate cancer (PCa) undergoing radical prostatectomy (RP). Multiple statistical models predicting LNI have been developed to support clinical decision-making regarding the need of extended pelvic lymph node dissection (ePLND). Our aim is to evaluate the prediction ability of the best-performing prediction tools for LNI in PCa in a Latin-American population. Methods Clinicopathological data of 830 patients with PCa who underwent RP and ePLND between 2007 and 2018 was obtained. Only data from patients who had ≥ 10 lymph nodes (LNs) harvested were included (n = 576 patients). Four prediction models were validated using this cohort: The Memorial Sloan Kettering Cancer Center (MSKCC) web calculator, Briganti v.2017, Yale formula and Partin tables v.2016. The performance of the prediction tools was assessed using the area under the receiver operating characteristic (ROC) curve (AUC). Results The median age was 61 years old (interquartile range [IQR] 56–66), the median Prostate specific antigen (PSA) was 6,81 ng/mL (IQR 4,8–10,1) and the median of LNs harvested was 17 (IQR 13–23), and LNI was identified in 53 patients (9.3%). Predictions from the 2017 Briganti nomogram AUC (0.85) and the Yale formula AUC (0.85) were the most accurate; MSKCC and 2016 Partin tables AUC were both 0,84. Conclusion There was no significant difference in the performance of the four validated prediction tools in a Latin-American population compared with the European or North American patients in whom these tools have been validated. Among the 4 models, the Briganti v.2017 and Yale formula yielded the best results, but the AUC overlapped with the other validated models.

Prostate MRI added to CAPRA, MSKCC and Partin cancer nomograms significantly enhances the prediction of adverse findings and biochemical recurrence after radical prostatectomy.

To determine the added value of preoperative prostate multiparametric MRI (mpMRI) supplementary to clinical variables and their role in predicting post prostatectomy adverse findings and biochemically recurrent cancer (BCR). All consecutive patients treated at HUS Helsinki University Hospital with robot assisted radical prostatectomy (RALP) between 2014 and 2015 were included in the analysis. The mpMRI data, clinical variables, histopathological characteristics, and follow-up information were collected. Study end-points were adverse RALP findings: extraprostatic extension, seminal vesicle invasion, lymph node involvement, and BCR. The Memorial Sloan Kettering Cancer Center (MSKCC) nomogram, Cancer of the Prostate Risk Assessment (CAPRA) score and the Partin score were combined with any adverse findings at mpMRI. Predictive accuracy for adverse RALP findings by the regression models was estimated before and after the addition of MRI results. Logistic regression, area under curve (AUC), decision curve analyses, Kaplan-Meier survival curves and Cox proportional hazard models were used. Preoperative mpMRI data from 387 patients were available for analysis. Clinical variables alone, MSKCC nomogram or Partin tables were outperformed by models with mpMRI for the prediction of any adverse finding at RP. AUC for clinical parameters versus clinical parameters and mpMRI variables were 0.77 versus 0.82 for any adverse finding. For MSKCC nomogram versus MSKCC nomogram and mpMRI variables the AUCs were 0.71 and 0.78 for any adverse finding. For Partin tables versus Partin tables and mpMRI variables the AUCs were 0.62 and 0.73 for any adverse finding. In survival analysis, mpMRI-projected adverse RP findings stratify CAPRA and MSKCC high-risk patients into groups with distinct probability for BCR. Preoperative mpMRI improves the predictive value of commonly used clinical variables for pathological stage at RP and time to BCR. mpMRI is available for risk stratification prebiopsy, and should be considered as additional source of information to the standard predictive nomograms.

Role of Multiparametric Magnetic Resonance Imaging in Predicting Pathologic Outcomes in Prostate Cancer.

Multiparametric magnetic resonance imaging (mpMRI) and the introduction of standardized protocols for its interpretation have had a significant impact on the field of prostate cancer (PC). Multiple randomized controlled trials have shown that the sensitivity for detection of clinically significant PC is increased when mpMRI results are the basis for indication of a prostate biopsy. The added value with regards to sensitivity has been strongest for patients with persistent suspicion for PC after a prior negative biopsy. Although enhanced sensitivity of mpMRI is convincing, studies that have compared mpMRI with prostatectomy specimens prepared by whole-mount section analysis have shown a significant number of lesions that were not detected by mpMRI. In this context, the importance of an additional systematic biopsy (SB) is still being debated. While SB in combination with targeted biopsies leads to an increased detection rate, most of the tumors detected by SB only are considered clinically insignificant. Currently, multiple risk calculation tools are being developed that include not only clinical parameters but mpMRI results in addition to clinical parameters in order to improve risk stratification for PC, such as the Partin tables. In summary, mpMRI of the prostate has become a standard procedure recommended by multiple important guidelines for the diagnostic work-up of patients with suspicion of PC.

Prospective Study of the Clinical Impact of Epithelial and Mesenchymal Circulating Tumor Cells in Localized Prostate Cancer.

BackgroundAlthough circulating tumor cells (CTCs) are considered as a surrogate marker in monitoring disease progression and treatment response in late stage prostate cancer (PCa), its clinical impact in localized PCa remains unclear, indicating the limitation that is simply based on cell count. This perspective observational study aimed to detect the epithelial-to-mesenchymal transition (EMT) subtypes of CTCs in localized PCa and analyze their clinical relevance and application in predicting PCa stages before surgery compared with the Partin table.Patients and MethodsBetween August 2017 and April 2019, 80 newly diagnosed localized PCa patients were enrolled in the study. Peripheral blood samples (5 mL) were collected prior to surgery. The CanPatrolTM CTC enrichment technique, a size-based isolation method, was used to detect the EMT CTCs. Clinical relevance of the CTCs was analyzed with Spearman’s rank correlation test. Models to predict pathological were built with multivariate logistic regression. Receiver operating characteristic (ROC) curve and area under the curve (AUC) analysis were performed to evaluate the accuracy of the prediction model.ResultsCTCs were detected in 55% of all patients. The biophenotypic CTCs were most valuable and closely correlated with PSA, Gleason score, D’Amico risk classification, and pathological stage in localized PCa. The mesenchymal subtype was rare in this population but associated with seminal vesicle invasion, while the epithelial subtype had limited clinical significance. In addition, the biophenotypic CTCs combined with traditional clinical variables were analyzed by multivariate logistic regression to predict organ-confined disease before surgery, of which the AUC reached 0.818 and was superior to the Partin table 2017 in our cohort.ConclusionThis study highlights the clinical impact of the biophenotypic CTCs in localized PCa, which was most closely related to clinical variables and could help to predict pathology outcomes before surgery.

Treatment of localized prostate cancer and use of nomograms among urologists in the West Africa sub-region.

Introductionthere is a high incidence of prostate cancer among men of African descent. The disease tends to occur at an early age with a tendency to be aggressive. The objective was to determine the practice of urologists in the West African sub-region regarding treatment of localized prostate cancer, the use of nomograms and their perception of the usefulness of nomograms.Methodsthis was a cross-sectional study that involved urologists practicing in the West African sub-region attending urology and surgery conferences of the “Société Internationale d´Urologie”, West African college of surgeons and the Ghana association of urological surgeons. A structured questionnaire was used that sort to ascertain the treatment modalities used for localized prostate cancer and the use of nomograms in the sub-region. The study period spanned the years 2018 and 2019.Resultsfifty-six urologists practicing in eleven West African countries responded. Fifty percent had been in practice for less than 5 years. Sixty eight percent (38/56) had been involved in the treatment of localized prostate cancer. Radical prostatectomy was widely available and the treatment modality most used 94.7% (36/38). Nomograms was used by 57.9% of them (22/38) with the Partin tables being the most commonly used nomogram (34.2%). No Locally developed nomogram for treatment of localized prostate cancer was identified.Conclusionradical prostatectomy is the commonest treatment modality used for the management of localized prostate cancer in the West Africa sub-region. Majority of the urologists used nomograms with the Partin tables being the most used.

THE ROLE OF LYMPHADENECTOMY IN PROSTATE CANCER PATIENTS.

SUMMARYProstate cancer is one of the most important men’s health issues in developed countries. For patients with prostate cancer a preoperative staging of the disease must be made. Involvement of lymph nodes could be assessed using imaging methods (CT or/and MRI), however, newer methods also exist (PET/CT, PSMA PET/CT). For some patients during radical prostatectomy a pelvic lymphadenectomy is recommended. Pelvic lymphadenectomy is indicated in intermediate- and high-risk group patients and with increased probability of lymph node invasion. The most used prediction tools for preoperative assessment of lymph nodes are Briganti and MSKCC nomograms and Partin tables. Pelvic lymphadenectomy can include different lymph nodes group, but extended lymphadenectomy is the recommended procedure. In 1-20% of patients, the lymph node invasion is present. Pelvic lymphadenectomy is primarily a diagnostic and staging method, and in minority of patients with positive lymph nodes it can be a curative method, too. In other patients with positive lymph nodes adjuvant therapy (radiotherapy and androgen deprivation therapy) can be beneficial.