Extraprostatic Extension Of Prostate Cancer Research Articles

MRI-based radiomics for prediction of extraprostatic extension of prostate cancer: a systematic review and meta-analysis.

We to systematically evaluate the diagnostic performance of MRI radiomics in detecting extracapsular extension (EPE) of prostate cancer (PCa). A literature search of online databases of PubMed, EMBASE, Cochrane Library, Web of Science, and Google Scholar online scientific publication databases was performed to identify studies published up to July 2023. The summary estimates were pooled with the hierarchical summary receiver-operating characteristic (HSROC) model. This study was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement, the quality of included studies was assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool (QUADAS-2) and the radiomics quality score (RQS). Meta-regression and subgroup analyses were performed to explore the impact of varying clinical settings. A total of ten studies met the inclusion criteria. The pooled sensitivity and specificity were 0.77 (95% CI 0.68-0.84, I2 = 83.5%) and 0.75 (95% CI 0.67-0.82, I2 = 83.5%), respectively, with an area under the HSROC curve of 0.88 (95% CI 0.85-0.91). Study quality was not high while assessing with the RQS. Substantial heterogeneity was observed between studies; however, meta-regression analysis did not reveal any significant contributing factors. MRI radiomics demonstrated moderate sensitivity and specificity, offering similar diagnostic performance with previous risk stratifications and models that primarily based on radiologists' subjective experience. However, all studies included were retrospective, thus the performance of radiomics needs to validate in prospective, multicenter studies.

[Diagnostic value of multiparametric MRI-based models in the assessment of extra-prostatic extension of prostate cancer].

Objective: To evaluate the diagnostic value of multiparametric magnetic resonance imaging (mpMRI) based models in the assessment of extra-prostatic extension (EPE) of prostate cancer. Methods: This retrospective study included 168 consecutive men with prostate cancers [aged 48 to 82 (66.6±6.8) years] who underwent radical prostatectomy and preoperative mpMRI examinations at the First Medical Center of the PLA General Hospital from January 2021 to February 2022. According to European Society of Urogenital Radiology (ESUR) score, EPE grade and mEPE score, all cases were independently evaluated by two radiologists, with disagreement reviewed by a senior radiologist as the final result. The diagnostic performance of each MRI-based model for pathologic EPE prediction was assessed using receiver operating characteristic curve (ROC), and the differences between the corresponding area under the curve (AUC) were compared using the DeLong test. The weighted Kappa test was used to evaluate the inter-reader agreement of each MRI-based model. Results: A total of 62 (36.9%) prostate cancer patients had pathologic confirmed EPE after radical prostatectomy. The AUC of ESUR score, EPE grade and mEPE score for predicting pathologic EPE were 0.836 (95%CI: 0.771-0.888), 0.834 (95%CI: 0.769-0.887) and 0.785 (95%CI: 0.715-0.844), respectively. The AUC of ESUR score and EPE grade were both superior to that of mEPE score with significant differences (all P<0.05), while there was no significant difference between the ESUR score and EPE grade models (P=0.900). EPE grading and mEPE score had good inter-reader consistency, with weighted Kappa values of 0.65 (95%CI: 0.56-0.74) and 0.74 (95%CI: 0.64-0.84), respectively. The inter-reader consistency of ESUR score was moderate, and the weighted Kappa value was 0.52 (95%CI: 0.40-0.63). Conclusion: All MRI-based models showed good preoperative diagnostic value in predicting EPE, among which the EPE grade resulted in more reliable performance with substantial inter-reader agreement.

Imaging features of the PI-RADS for predicting extraprostatic extension of prostate cancer: systematic review and meta-analysis

ObjectivesTo systematically determine the diagnostic performance of each MRI feature of the PI-RADS for predicting extraprostatic extension (EPE) in prostate cancer.MethodsA literature search in the MEDLINE and EMBASE databases was conducted to identify original studies reporting the accuracy of each feature on MRI for the dichotomous diagnosis of EPE. The meta-analytic pooled diagnostic odds ratio (DOR), sensitivity, specificity, and their 95% confidence intervals (CIs) were obtained using a bivariate random-effects model.ResultsAfter screening 1955 studies, 17 studies with a total of 3062 men were included. All six imaging features, i.e., bulging prostatic contour, irregular or spiculated margin, asymmetry or invasion of neurovascular bundle, obliteration of rectoprostatic angle, tumor-capsule interface > 10 mm, and breach of the capsule with evidence of direct tumor extension, were significantly associated with EPE. Breach of the capsule with direct tumor extension demonstrated the highest pooled DOR (15.6, 95% CI [7.7–31.5]) followed by tumor-capsule interface > 10 mm (10.5 [5.4–20.2]), asymmetry or invasion of neurovascular bundle (7.6 [3.8–15.2]), and obliteration of rectoprostatic angle (6.1 [3.8–9.8]). Irregular or spiculated margin showed the lowest pooled DOR (2.3 [1.3–4.2]). Breach of the capsule with direct tumor extension and tumor-capsule interface > 10 mm showed the highest pooled specificity (98.0% [96.2–99.0]) and sensitivity (86.3% [70.0–94.4]), respectively.ConclusionsAmong the six MRI features of prostate cancer, breach of the capsule with direct tumor extension and tumor-capsule interface > 10 mm were the most predictive of EPE with the highest specificity and sensitivity, respectively.Graphical abstract

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.

MEPE-score: a comprehensive grading system for predicting pathologic extraprostatic extension of prostate cancer at multiparametric magnetic resonance imaging

ObjectiveTo investigate the diagnostic accuracy of the PI-RADS v2.1 multiparametric magnetic resonance imaging (mpMRI) features in predicting extraprostatic extension (mEPE) of prostate cancer (PCa), as well as to develop and validate a comprehensive mpMRI-derived score (mEPE-score).MethodsWe retrospectively reviewed all consecutive patients admitted to two institutions for radical prostatectomy for PCa with available records of mpMRI performed between January 2015 and December 2020. Data from one institution was used for investigating diagnostic performance of each mEPE feature using radical prostatectomy specimens as benchmark. The results were implemented in a mEPE-score as follows: no mEPE features: 1; capsular abutment: 2; irregular or spiculated margin: 3; bulging prostatic contour, or asymmetry of the neurovascular bundles, or tumor-capsule interface > 1.0 cm: 4; ≥ 2 of the previous three parameters or measurable extraprostatic disease: 5. The performance of mEPE features was evaluated using the five diagnostic parameters and ROC curve analysis.ResultsTwo-hundred patients were enrolled at site 1 and 76 at site 2. mEPE features had poor sensitivities ranging from 0.08 (0.00–0.15) to 0.71 (0.59–0.83), whereas specificity ranged from 0.68 (0.58–0.79) to 1.00. mEPE-score showed excellent discriminating ability (AUC > 0.8) and sensitivity = 0.82 and specificity = 0.77 with a threshold of 3. mEPE-score had AUC comparable to ESUR-score (p = 0.59 internal validation; p = 0.82 external validation), higher than or comparable to mEPE-grade (p = 0.04 internal validation; p = 0.58 external validation), and higher than early-and-late-EPE (p < 0.0001 internal and external validation). There were no significant differences between readers having different expertise with EPE-score (p = 0.32) or mEPE-grade (p = 0.45), but there were significant differences for ESUR-score (p = 0.02) and early-versus-late-EPE (p = 0.03).ConclusionsThe individual mEPE features have low sensitivity and high specificity. The use of mEPE-score allows for consistent and reliable assessment for pathologic EPE.Key Points• Individual PI-RADS v2.1 mpMRI features had poor sensitivities ranging from 0.08 (0.00–0.15) to 0.71 (0.59–0.83), whereas Sp ranged from 0.68 (0.58–0.79) to 1.00.• mEPE-score is an all-inclusive score for the assessment of pEPE with excellent discriminating ability (i.e., AUC > 0.8) and Se = 0.82, Sp = 0.77, PPV = 0.74, and NPV = 0.84 with a threshold of 3.• The diagnostic performance of the expert reader and beginner reader with pEPE-score was comparable (p = 0.32).

Use of high-resolution micro-ultrasound to predict extraprostatic extension of prostate cancer prior to surgery: a prospective single-institutional study

We aim to evaluate the accuracy of micro-ultrasound (microUS) in predicting extraprostatic extension (EPE) of Prostate Cancer (PCa) prior to surgery. Patients with biopsy-proven PCa scheduled for robot-assisted radical prostatectomy (RARP) were prospectively recruited. The following MRI-derived microUS features were evaluated: capsular bulging, visible breach of the prostate capsule (visible extracapsular extension; ECE), presence of hypoechoic halo, and obliteration of the vesicle-prostatic angle. The ability of each feature to predict EPE was determined. Overall, data from 140 patients were examined. All predictors were associated with non-organ-confined disease (p < 0.001). Final pathology showed that 79 patients (56.4%) had a pT2 disease and 61 (43.3%) ≥ pT3. Rate of non-organ-confined disease increased from 44% in those individuals with only 1 predictor (OR 7.71) to 92.3% in those where 4 predictors (OR 72.00) were simultaneously observed. The multivariate logistic regression model including clinical parameters showed an area under the curve (AUC) of 82.3% as compared to an AUC of 87.6% for the model including both clinical and microUS parameters. Presence of ECE at microUS predicted EPE with a sensitivity of 72.1% and a specificity of 88%, a negative predictive value of 80.5% and positive predictive value of 83.0%, with an AUC of 80.4%. MicroUS can accurately predict EPE at the final pathology report in patients scheduled for RARP.

MRI utility in predicting extraprostatic extension of prostate cancer and biochemical recurrence after radical prostatectomy

Introduction. The study aimed to evaluate the performance of multiparametric magnetic resonance imaging (mpMRI) and the Martini model to predict extraprostatic extension (EPE) and biochemical recurrence (BCR) of prostate cancer (PCa). Materials and methods. 61 patients underwent a radical laparoscopic prostatectomy. The preoperative risk of EPE was determined using mpMRI and the Martini model. Results. MpMRI predicts the presence of EPE of PCa with a sensitivity and specificity of 47.4% and 85.7%, respectively (AUC 0.66, 95% CI: 0.51–0.82, p = 0.046). The Martini model’s sensitivity was higher, but the specificity was lower than that of mpMRI and was 84.2% and 66.7%, respectively (AUC 0.78, 95% CI: 0.66–0.89, p &lt; 0.001). Univariate and multivariate Cox analysis indicated that EPE in mpMRI (HR 6.6, 95% CI: 1.8–24.1), and the presence of positive surgical margins (PSM) (HR 7.1, 95% CI: 1.9–26.7) are independent factors increasing the probability of BCR. Conclusions. MpMRI and Martini model are valuable tools in local staging of PCa, managing and predicting the oncological treatment outcomes of patients with PCa.

International Multi-Site Initiative to Develop an MRI-Inclusive Nomogram for Side-Specific Prediction of Extraprostatic Extension of Prostate Cancer.

Simple SummaryFor patients with newly diagnosed prostate cancer, it is important to detect tumor growth beyond the prostate, as this can affect a patient’s prognosis, influence management decisions, and alter treatment strategies. It is recognized that on prostate MRI, some instances of extraprostatic tumor growth can be missed. In this study, we merged patient data from multiple hospitals in different countries and developed a type of mathematical formula called “nomogram” that combines MRI findings with other available patient data. The results of our study allow physicians to more accurately diagnose extraprostatic tumor growth by combining clinical, biopsy, and MRI-derived information according to their relative statistical importance.Background: To develop an international, multi-site nomogram for side-specific prediction of extraprostatic extension (EPE) of prostate cancer based on clinical, biopsy, and magnetic resonance imaging- (MRI) derived data. Methods: Ten institutions from the USA and Europe contributed clinical and side-specific biopsy and MRI variables of consecutive patients who underwent prostatectomy. A logistic regression model was used to develop a nomogram for predicting side-specific EPE on prostatectomy specimens. The performance of the statistical model was evaluated by bootstrap resampling and cross validation and compared with the performance of benchmark models that do not incorporate MRI findings. Results: Data from 840 patients were analyzed; pathologic EPE was found in 320/840 (31.8%). The nomogram model included patient age, prostate-specific antigen density, side-specific biopsy data (i.e., Gleason grade group, percent positive cores, tumor extent), and side-specific MRI features (i.e., presence of a PI-RADSv2 4 or 5 lesion, level of suspicion for EPE, length of capsular contact). The area under the receiver operating characteristic curve of the new, MRI-inclusive model (0.828, 95% confidence limits: 0.805, 0.852) was significantly higher than that of any of the benchmark models (p < 0.001 for all). Conclusions: In an international, multi-site study, we developed an MRI-inclusive nomogram for the side-specific prediction of EPE of prostate cancer that demonstrated significantly greater accuracy than clinical benchmark models.

Combining the Tumor Contact Length and Apparent Diffusion Coefficient Better Predicts Extraprostatic Extension of Prostate Cancer with Capsular Abutment: A 3 Tesla MR Imaging Study.

Purpose: To assess the diagnostic performance of the tumor contact length (TCL) and apparent diffusion coefficient (ADC) for predicting extraprostatic extension (EPE) of prostate cancer with capsular abutment (CA).Methods: Ninety-three patients with biopsy-proven prostate cancer underwent 3-Tesla MRI, including diffusion-weighted imaging (b value = 0, 2000 s/mm2) and radical prostatectomy. Two experienced radiologists, blinded to the clinicopathological data, retrospectively assessed the presence of CA on T2-weighted imaging (T2WI). TCL on T2WI and ADC values were measured on detecting CA in prostate cancer. We used the receiver operating characteristic curves to assess the diagnostic performance of TCL and ADC values for predicting EPE.Results: CA was present in 58 prostate cancers among 93 patients. The cut-off value for TCL was 6.9 mm, which yielded an area under the curve (AUC) of 0.75. This corresponded to a sensitivity, specificity, and accuracy of 84.2%, 61.5%, and 69.0%, respectively. The cut-off value for ADC was 0.63 × 10–3 mm2/s, which yielded an AUC of 0.76. This, in turn, corresponded to a sensitivity, specificity, and accuracy of 84.2%, 59.0%, and 67.2%, respectively. The combined cut-off value of TCL and ADC yielded an AUC of 0.82. The specificity (84.6%) and accuracy (81.0%) of the combined value were superior to their individual values (P < 0.05).Conclusion: A combination of TCL and ADC values provided high specificity and accuracy for detecting EPE of prostatic cancer with CA.

The absolute tumor-capsule contact length in the diagnosis of extraprostatic extension of prostate cancer.

Extraprostatic extension (EPE) of prostate cancer is associated with a poor prognosis. The broad-based capsule-tumor interface has been recognized as one of the worrisome imaging features in multiparametric prostate MRI (mpMRI). However, there was significant heterogeneity among the measurement method used in prior studies. This study's objectives were to investigate and compare the accuracy between the curvilinear and linear measurement, find the optimal cut-off contact surface threshold for the diagnosis of EPE, and assess the benefit of the additional contact surface measurement versus visual assessment alone. The status of EPE in mpMRI and the overall PI-RADS were assessed. The tumor's dimensions, the actual tumor-capsule contact length (ACTCL), and the absolute tumor-capsule contact length (ABTCL) were measured. The parameters were analyzed and correlated with the EPE status from prostatectomy specimens. Ninety-five patients who underwent mpMRI followed by prostatectomy were included in the study. High Gleason score (score 8-9), radiologist's impression of EPE, and PI-RADS 5 were significantly correlated with EPE in surgical specimens (p = 0.014, p < 0.001, and p < 0.001, respectively). Both ACTCL and ABTCL of patients with EPE were significantly higher than those without EPE in all imaging sequences (p < 0.001 to p = 0.003). The ABTCL has higher accuracy than the ACTCL. Dynamic contrast enhancement (DCE) was the most accurate sequence to measure the contact interface. The recommended cut-off value of ABTCL was 15.0mm, which had a sensitivity and specificity of 75.86% and 72.09%. Multivariable analysis revealed that the ABTCL > 15mm and the radiologist's impression on visual assessment were the only two independent predictors for the prediction of EPE (p = 0.048 and p = 0.016, respectively). Improvement of diagnostic performance was achieved when the two factors were combined. The ABTCL has better accuracy than the curvilinear measurement in the prediction of EPE. The optimum sequence for the measurement of the contact surface is the DCE. We recommended using 15.0mm as a cut-off point. The addition of the ABTCL measurement showed an increase in diagnostic performance. We encourage radiologists to use the capsular contact measurement in addition to their visual assessment to detect EPE in pre-operative MRI.

Cerebral Cavernous Malformation 1 Determines YAP/TAZ Signaling-Dependent Metastatic Hallmarks of Prostate Cancer Cells.

Simple SummaryOur analysis of cerebral cavernous malformation 1 (CCM1) expression and function reveals a candidate predictive biomarker for prostate cancer metastasis and provides evidence that CCM1 abnormality can be pathogenic in prostate cancer. In particular, the CCM1 regulation of metastasis appears as a common molecular event in metastatic prostate cancer cells arising from disparate genetic backgrounds.Enhanced Yes-associated protein (YAP)/transcriptional co-activator with PDZ-binding motif (TAZ) signaling is correlated with the extraprostatic extension of prostate cancer. However, the mechanism by which YAP/TAZ signaling becomes hyperactive and drives prostate cancer progression is currently unclear. In this study, we revealed that higher expression of CCM1, which is uniquely found in advanced prostate cancer, is inversely correlated with metastasis-free and overall survival in patients with prostate cancer. We also demonstrated that CCM1 induces the metastasis of multiple types of prostate cancer cells by regulating YAP/TAZ signaling. Mechanistically, CCM1, a gene mutated in cerebral cavernous malformation, suppresses DDX5, which regulates the suppression of YAP/TAZ signaling, indicating that CCM1 and DDX5 are novel upstream regulators of YAP/TAZ signaling. Our findings highlight the importance of CCM1-DDX5-YAP/TAZ signaling in the metastasis of prostate cancer cells.

Radiomics Based on Multiparametric Magnetic Resonance Imaging to Predict Extraprostatic Extension of Prostate Cancer.

Background: To develop a radiomics model based on multiparametric MRI (mpMRI) for preoperative prediction of extraprostatic extension (EPE) in patients with prostate cancer (PCa).Methods: Ninety-five pathology-confirmed PCa patients with 115 lesions (49 positive and 66 negative) were retrospectively enrolled. A 3.0T MR scanner was used to perform T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced imaging (DCE). Radiomics features extracted from T2WI, DWI, apparent diffusion coefficient (ADC), and DCE were used to build a radiomics model. Patients' clinical and pathological variables were also obtained to build a clinical model. The radiomics model and clinical model were further integrated to build a combined nomogram. All lesions were randomly divided into the training group (82 lesions) and the validation group (33 lesions). A least absolute shrinkage and selection operator (LASSO) regression algorithm was applied to build the radiomics model. The diagnostic performance of different models was assessed by calculating the area under the curve (AUC) and compared using the Delong test. The calibration curve and decision curve analyses were used to assess the calibration and clinical usefulness of the radiomics model.Results: The AUC values for the radiomics model in the training and validation group were 0.919 and 0.865, respectively, with a good calibration performance. The decision curve analysis confirmed the clinical utility of the radiomics model. The accuracy, sensitivity, and specificity were 81.8, 71.4, and 89.5% in the validation group. In the validation group, the radiomics model outperformed the clinical model (AUC = 0.658, P = 0.020), and was comparable with the combined nomogram (AUC = 0.857, P = 0.644).Conclusion: The radiomics model based on mpMRI could different EPE and non-EPE lesions with satisfactory diagnostic performance, and this model might assist in predicting EPE before prostatectomy.

External validation of the Martini nomogram for prediction of side-specific extraprostatic extension of prostate cancer in patients undergoing robot-assisted radical prostatectomy

IntroductionTo establish oncological safe nerve-sparing robot-assisted radical prostatectomy, accurate assessment of extraprostatic extension (EPE) is critical. A recently developed nomogram including magnetic resonance imaging parameters accurately predicted side-specific EPE in the development cohort. The aim of this study is to assess this model's performance in an external patient population. Patients and methodsModel fit was assessed in a cohort of 550 patients who underwent robot-assisted radical prostatectomy in 2014 to 2017 for prostate cancer. Model calibration was evaluated using calibration slopes. Discriminative ability was quantified using the area under the receiver operating characteristic curve. Model updating was done by adjusting the linear predictor to minimize differences in expected and observed risk for EPE. ResultsA total of 792 prostate lobes were included for model validation. Discriminative ability expressed in terms of receiver operating characteristic curve was 0.78, 95%CI 0.75–0.82. Graphical evaluation of the calibration showed poor fit with a high disagreement between predicted probabilities and observed probabilities of EPE in the population. Model updating resulted in excellent agreement between mean predicted and observed probabilities. However, calibration plots showed substantial miscalibration; including both under- and overestimation. ConclusionExternal validation of the novel nomogram for the prediction of side specific EPE developed by Martini and co-workers showed good discriminative ability but poor calibration. After updating, substantial miscalibration was still present. Use of this nomogram for individualized risk predictions is therefore not recommended.

Comparison of Standard and Specialized Readings in Routine Practice for the Assessment of Extraprostatic Extension of Prostate Cancer on MRI after Biopsy

Comparison of Standard and Specialized Readings in Routine Practice for the Assessment of Extraprostatic Extension of Prostate Cancer on MRI after Biopsy

A Grading System for Extraprostatic Extension of Prostate Cancer That We Can All Agree Upon?

A Grading System for Extraprostatic Extension of Prostate Cancer That We Can All Agree Upon?

Re: A Grading System for the Assessment of Risk of Extraprostatic Extension of Prostate Cancer at Multiparametric MRI.

Re: A Grading System for the Assessment of Risk of Extraprostatic Extension of Prostate Cancer at Multiparametric MRI.

The prognostic value of zonal origin and extraprostatic extension of prostate cancer for biochemical recurrence after radical prostatectomy

ObjectiveTo investigate the influence of the zonal origin of prostate cancer and extraprostatic extension on biochemical recurrence (BCR). Patients and methodsWe included 638 consecutive patients undergoing radical prostatectomy between 2005 and 2015 who did not receive neoadjuvant/adjuvant therapy. The largest lesion was defined as the index tumor. We categorized each patient into the transition zone (TZ) or peripheral zone (PZ) group based on the lesion where the index tumor existed. Differences in the BCR defined as increasing prostate-specific antigen rate between groups were examined by Kaplan-Meier analysis and the Cox proportional hazards model. ResultsThere were 293 (46%) patients with TZ cancer and 345 (54%) with PZ cancer. TZ cancer was significantly associated with a higher prostate-specific antigen (P = 0.012), lower biopsy positive core rate (P = 0.020), lower pathological Gleason score (P = 0.017), lower pathological stage (P = 0.002), and lower rate of seminal vesicle invasion (P = 0.002). During a median follow-up period of 59 months, 79 patients (12%) developed BCR. In the entire cohort, the PZ origin (hazard ratio: 1.68, P = 0.033) and extraprostatic extension were independent risk factors for BCR. The 3-, 5-, and 7-year BCR-free survival rates of patients with pT3a TZ cancer were 89%, 88%, and 86%, respectively, which were significantly better than those of patients with pT3a PZ cancer (80%, 74%, and 62%, P = 0.012), but were similar to those of the pT2 cancer cohort (92%, 91%, and 90%, P = 0.376). ConclusionTZ cancer had more favorable pathological characteristics and oncological outcome than PZ cancer especially in pT3a cases.

Reliability of the different versions of Partin tables in predicting extraprostatic extension of prostate cancer: a systematic review and meta-analysis.

Accurate prediction of extraprostatic extension (EPE) of prostate cancer (PCa) is the keystone for deciding whether to perform a neurovascular bundle sparing (NVBs) radical prostatectomy or not, which will subsequently affect the postoperative functional outcomes especially potency. Partin tables are the most commonly used predictive tools (PTs) for prediction of EPE. Moreover, they are the most commonly externally validated. In these settings, the aim of our work is to perform a systematic review of the literature and a meta-analysis for the discriminative performance of the different versions of Partin tables for EPE prediction. A systematic search of Medline, Scopus and Cochrane library was performed to include all the external validation (EV) studies that reported the discriminative performance (area under the curve [AUC]) of the different versions of Partin tables as a PT for EPE. Different versions of Partin tables (1997, 2001, 2007, 2010, and 2013) were included in separate meta-analyses. The pooled AUC with 95% CI were calculated to determine the weighted summary AUC using the random effect model. Twenty-six studies carried out in different countries including the USA, Korea, Germany, Ireland, China, Austria, France, Italy, the UK, and India were included. Considering the small number and generally low quality of the EV studies in literature, most of the included studies showed some sort of bias especially in the sample size & missing data domain. The pooled EPE AUC were 0.642 (95% CI; 0.601-0.682), 0.672 (95% CI; 0.617-0.727), 0.659 (95% CI; 0.623-0.695), 0.669 (95% CI; 0.623-0.715) and 0.644 (95% CI; 0.545-0.742) for the 1997, 2001, 2007, 2010 and 2013 versions, respectively. Despite being the most commonly used predictive tool for prediction of EPE, the pooled EPE AUC for different versions of Partin tables showed poor discriminative performance. Thus, surgeons must be cautious when referring to Partin tables for prediction of EPE. Further EV studies are required to confirm these results.

A Grading System for the Assessment of Risk of Extraprostatic Extension of Prostate Cancer at Multiparametric MRI.

Purpose To evaluate MRI features associated with pathologically defined extraprostatic extension (EPE) of prostate cancer and to propose an MRI grading system for pathologic EPE. Materials and Methods In this prospective study, consecutive male study participants underwent preoperative 3.0-T MRI from June 2007 to March 2017 followed by robotic-assisted laparoscopic radical prostatectomy. An MRI-based EPE grading system was defined as follows: curvilinear contact length of 1.5 cm or capsular bulge and irregularity were grade 1, both features were grade 2, and frank capsular breach were grade 3. Multivariable logistic regression and decision curve analyses were performed to compare the MRI grade model and clinical parameters (prostate-specific antigen, Gleason score) for pathologic EPE prediction by using the area under the receiver operating characteristic curve (AUC) value. Results Among 553 study participants, the mean age was 60 years ± 8 (standard deviation); the median prostate-specific antigen value was 6.3 ng/mL. A total of 125 of 553 (22%) participants had pathologic EPE at radical prostatectomy. Detection of pathologic EPE, defined as number of pathologic EPEs divided by number of participants with individual MRI features, was as follows: curvilinear contact length, 88 of 208 (42%); capsular bulge and irregularity, 78 of 175 (45%); and EPE visible at MRI, 37 of 56 (66%). For MRI, grades 1, 2, and 3 for detection of pathologic EPE were 18 of 74 (24%), 39 of 102 (38%), and 37 of 56 (66%), respectively. Clinical features plus the MRI-based EPE grading system (prostate-specific antigen, International Society of Urological Pathology stage, MRI grade) predicted pathologic EPE better than did MRI grade alone (AUC, 0.81 vs 0.77, respectively; P < .001). Conclusion Higher MRI-based extraprostatic extension (EPE) grading categories were associated with a greater risk of pathologic EPE. Clinical features plus MRI grading had the highest diagnostic performance for prediction of pathologic EPE. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Eberhardt in this issue.

Development and Validation of Genomic Tools to Predict Extraprostatic Extension of Prostate Cancer, Opportunities for Personalizing Treatment

Development and Validation of Genomic Tools to Predict Extraprostatic Extension of Prostate Cancer, Opportunities for Personalizing Treatment