Development and external validation of a biopsy-derived nomogram to predict risk of ipsilateral extraprostatic extension

Predictive Factors for Extracapsular Extension of Prostate Cancer to Select the Candidates for Nerve-sparing Radical Prostatectomy

Nerve-sparing radical prostatectomy (NSRP) for prostate cancer (PC) enables better postoperative recovery of continence and potency but may increase the risk of positive surgical margins. This study aimed to investigate preoperative predictive factors for extracapsular extension (ECE) of PC to select patients for NSRP. We retrospectively evaluated 288 patients with PC (576 lobes) diagnosed with 12-core transrectal ultrasound-guided biopsy and magnetic resonance imaging (MRI) who underwent laparoscopic or robot-assisted radical prostatectomy at our institution. Surgical specimens and preoperative parameters (prostate-specific antigen, prostate volume, biopsy and MRI findings, preoperative therapy) were analyzed. Of 576 prostate lobes, the incidence Ipsilateral ECE was identified in 97 (16.8%) lobes. The higher number of unilateral positive biopsy cores, the highest Gleason score 8 or more and positive unilateral findings on MRI are significant higher in prostate sides with ECE in univariate analysis. In multivariate analysis, positive unilateral MRI findings (odds ratio [OR], 2.86; p < 0.001) and unilateral biopsy positive core ≥ 3 (OR, 3.73; p < 0.001) were independent predictors of unilateral ECE. The detection rate of unilateral ECE in those cases with two factors (side-specific positive biopsy core 2 or less and side-specific MRI findings negative) was 7.1% (19/269). Patients with fewer unilateral positive biopsy cores and negative unilateral MRI findings might be good candidates for NSRP.

Multimodal radiomics based on 18F-Prostate-specific membrane antigen-1007 PET/CT and multiparametric MRI for prostate cancer extracapsular extension prediction

OBJECTIVES

To compare the performance of the multiparametric magnetic resonance imaging (mpMRI) radiomics and 18F-Prostate-specific membrane antigen (PSMA)-1007 PET/CT radiomics model in diagnosing extracapsular extension (EPE) in prostate cancer (PCa), and to evaluate the performance of a multimodal radiomics model combining mpMRI and PET/CT in predicting EPE.

METHODS

We included 197 patients with PCa who underwent preoperative mpMRI and PET/CT before surgery. mpMRI and PET/CT images were segmented to delineate the regions of interest and extract radiomics features. PET/CT, mpMRI, and multimodal radiomics models were constructed based on maximum correlation, minimum redundancy, and logistic regression analyses. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and indices derived from the confusion matrix.

RESULTS

AUC values for the mpMRI, PET/CT, and multimodal radiomics models were 0.85 (95% CI, 0.78-0.90), 0.73 (0.64-0.80), and 0.83 (0.75-0.89), respectively, in the training cohort and 0.74 (0.61-0.85), 0.62 (0.48-0.74), and 0.77 (0.64-0.87), respectively, in the testing cohort. The net reclassification improvement demonstrated that the mpMRI radiomics model outperformed the PET/CT one in predicting EPE, with better clinical benefits. The multimodal radiomics model performed better than the single PET/CT radiomics model (P < .05).

CONCLUSION

The mpMRI and 18F-PSMA-PET/CT combination enhanced the predictive power of EPE in patients with PCa. The multimodal radiomics model will become a reliable and robust tool to assist urologists and radiologists in making preoperative decisions.

ADVANCES IN KNOWLEDGE

This study presents the first application of multimodal radiomics based on PET/CT and MRI for predicting EPE.

Defining the role of multiparametric MRI in predicting prostate cancer extracapsular extension

OBJECTIVES

To identify the predictive factors of prostate cancer extracapsular extension (ECE) in an institutional cohort of patients who underwent multiparametric MRI of the prostate prior to radical prostatectomy (RP).

PATIENTS AND METHODS

Overall, 126 patients met the selection criteria, and their medical records were retrospectively collected and analysed; 2 experienced radiologists reviewed the imaging studies. Logistic regression analysis was conducted to identify the variables associated to ECE at whole-mount histology of RP specimens; according to the statistically significant variables associated, a predictive model was developed and calibrated with the Hosmer-Lomeshow test.

RESULTS

The predictive ability to detect ECE with the generated model was 81.4% by including the length of capsular involvement (LCI) and intraprostatic perineural invasion (IPNI). The predictive accuracy of the model at the ROC curve analysis showed an area under the curve (AUC) of 0.83 [95% CI (0.76-0.90)], p < 0.001. Concordance between radiologists was substantial in all parameters examined (p < 0.001). Limitations include the retrospective design, limited number of cases, and MRI images reassessment according to PI-RADS v2.0.

CONCLUSION

The LCI is the most robust MRI factor associated to ECE; in our series, we found a strong predictive accuracy when combined in a model with the IPNI presence. This outcome may prompt a change in the definition of PI-RADS score 5.

Oncological outcomes after attempted nerve-sparing radical prostatectomy (NSRP) in patients with high-risk prostate cancer are comparable to standard non-NSRP: a longitudinal long-term propensity-matched single-centre study

OBJECTIVE

To assess the long-term safety of nerve-sparing radical prostatectomy (NSRP) in men with high-risk prostate cancer (PCa) by comparing survival outcomes, disease recurrence, the need for additional therapy, and perioperative outcomes of patients undergoing NSRP to those having non-NSRP.

PATIENTS AND METHODS

We included consecutive patients at a single, academic centre who underwent open RP for high-risk PCa, defined as preoperative prostate-specific antigen level of > 20 ng/mL and/or postoperative International Society of Urological Pathology Grade Group 4 or 5 (i.e., Gleason score ≥ 8) and/or ≥pT3 and/or pN1 assessing the RP and lymph node specimen. We calculated a propensity score and used inverse probability of treatment weighting to match baseline characteristics of patients with high-risk PCa who underwent NSRP vs non-NSRP. We analysed oncological outcome as time-to-event and calculated hazard ratios (HRs).

RESULTS

A total of 726 patients were included in this analysis of which 84% (n = 609) underwent NSRP. There was no evidence for the positive surgical margin rate being different between the NSRP and non-NSRP groups (47% vs 49%, P = 0.64). Likewise, there was no evidence for the need for postoperative radiotherapy being different in men who underwent NSRP from those who underwent non-NSRP (HR 0.78, 95% confidence interval [CI] 0.53-1.15). NSRP did not impact the risk of any recurrence (HR 0.99, 95% CI 0.73-1.34, P = 0.09) and there was no evidence for survival being different in men who underwent NSRP to those who underwent non-NSRP (HR 0.65, 95% CI 0.39-1.08). There was also no evidence for the cancer-specific survival (HR 0.56, 95% CI 0.29-1.11) or progression-free survival (HR 0.99, 95% CI 0.73-1.34) being different between the groups.

CONCLUSION

In patients with high-risk PCa, NSRP can be attempted without compromising long-term oncological outcomes provided a comprehensive assessment of objective (e.g., T Stage) and subjective (e.g., intraoperative appraisal of tissue planes) criteria are conducted.

Biopsy prostate cancer perineural invasion and tumour load are associated with positive posterolateral margins at radical prostatectomy: implications for planning of nerve-sparing surgery

AIMS

Radical prostatectomy (RP) for prostate cancer is frequently complicated by erectile dysfunction and urinary incontinence. However, sparing of the nerve bundles adjacent to the posterolateral sides of the prostate reduces the number of complications at the risk of positive surgical margins. Preoperative selection of men eligible for safe, nerve-sparing surgery is therefore needed. Our aim was to identify pathological factors associated with positive posterolateral surgical margins in men undergoing bilateral nerve-sparing RP.

METHODS AND RESULTS

Prostate cancer patients undergoing RP with standardised intra-operative surgical margin assessment according to the NeuroSAFE technique were included. Preoperative biopsies were reviewed for grade group (GG), cribriform and/or intraductal carcinoma (CR/IDC), perineural invasion (PNI), cumulative tumour length and extraprostatic extension (EPE). Of 624 included patients, 573 (91.8%) received NeuroSAFE bilaterally and 51 (8.2%) unilaterally, resulting in a total of 1197 intraoperative posterolateral surgical margin assessments. Side-specific biopsy findings were correlated to ipsilateral NeuroSAFE outcome. Higher biopsy GG, CR/IDC, PNI, EPE, number of positive biopsies and cumulative tumour length were all associated with positive posterolateral margins. In multivariable bivariate logistic regression, ipsilateral PNI [odds ratio (OR) = 2.98, 95% confidence interval (CI) = 1.62-5.48; P < 0.001] and percentage of positive cores (OR = 1.18, 95% CI = 1.08-1.29; P < 0.001) were significant predictors for a positive posterolateral margin, while GG and CR/IDC were not.

CONCLUSIONS

Ipsilateral PNI and percentage of positive cores were significant predictors for a positive posterolateral surgical margin at RP. Biopsy PNI and tumour volume can therefore support clinical decision-making on the level of nerve-sparing surgery in prostate cancer patients.

MRI-based nomograms and radiomics in presurgical prediction of extraprostatic extension in prostate cancer: a systematic review

PURPOSE

Prediction of extraprostatic extension (EPE) is essential for accurate surgical planning in prostate cancer (PCa). Radiomics based on magnetic resonance imaging (MRI) has shown potential to predict EPE. We aimed to evaluate studies proposing MRI-based nomograms and radiomics for EPE prediction and assess the quality of current radiomics literature.

METHODS

We used PubMed, EMBASE, and SCOPUS databases to find related articles using synonyms for MRI radiomics and nomograms to predict EPE. Two co-authors scored the quality of radiomics literature using the Radiomics Quality Score (RQS). Inter-rater agreement was measured using the intraclass correlation coefficient (ICC) from total RQS scores. We analyzed the characteristic s of the studies and used ANOVAs to associate the area under the curve (AUC) to sample size, clinical and imaging variables, and RQS scores.

RESULTS

We identified 33 studies-22 nomograms and 11 radiomics analyses. The mean AUC for nomogram articles was 0.783, and no significant associations were found between AUC and sample size, clinical variables, or number of imaging variables. For radiomics articles, there were significant associations between number of lesions and AUC (p < 0.013). The average RQS total score was 15.91/36 (44%). Through the radiomics operation, segmentation of region-of-interest, selection of features, and model building resulted in a broader range of results. The qualities the studies lacked most were phantom tests for scanner variabilities, temporal variability, external validation datasets, prospective designs, cost-effectiveness analysis, and open science.

CONCLUSION

Utilizing MRI-based radiomics to predict EPE in PCa patients demonstrates promising outcomes. However, quality improvement and standardization of radiomics workflow are needed.

Development, multi-institutional external validation, and algorithmic audit of an artificial intelligence-based Side-specific Extra-Prostatic Extension Risk Assessment tool (SEPERA) for patients undergoing radical prostatectomy: a retrospective cohort study

BACKGROUND

Accurate prediction of side-specific extraprostatic extension (ssEPE) is essential for performing nerve-sparing surgery to mitigate treatment-related side-effects such as impotence and incontinence in patients with localised prostate cancer. Artificial intelligence (AI) might provide robust and personalised ssEPE predictions to better inform nerve-sparing strategy during radical prostatectomy. We aimed to develop, externally validate, and perform an algorithmic audit of an AI-based Side-specific Extra-Prostatic Extension Risk Assessment tool (SEPERA).

METHODS

Each prostatic lobe was treated as an individual case such that each patient contributed two cases to the overall cohort. SEPERA was trained on 1022 cases from a community hospital network (Trillium Health Partners; Mississauga, ON, Canada) between 2010 and 2020. Subsequently, SEPERA was externally validated on 3914 cases across three academic centres: Princess Margaret Cancer Centre (Toronto, ON, Canada) from 2008 to 2020; L'Institut Mutualiste Montsouris (Paris, France) from 2010 to 2020; and Jules Bordet Institute (Brussels, Belgium) from 2015 to 2020. Model performance was characterised by area under the receiver operating characteristic curve (AUROC), area under the precision recall curve (AUPRC), calibration, and net benefit. SEPERA was compared against contemporary nomograms (ie, Sayyid nomogram, Soeterik nomogram [non-MRI and MRI]), as well as a separate logistic regression model using the same variables included in SEPERA. An algorithmic audit was performed to assess model bias and identify common patient characteristics among predictive errors.

FINDINGS

Overall, 2468 patients comprising 4936 cases (ie, prostatic lobes) were included in this study. SEPERA was well calibrated and had the best performance across all validation cohorts (pooled AUROC of 0·77 [95% CI 0·75-0·78] and pooled AUPRC of 0·61 [0·58-0·63]). In patients with pathological ssEPE despite benign ipsilateral biopsies, SEPERA correctly predicted ssEPE in 72 (68%) of 106 cases compared with the other models (47 [44%] in the logistic regression model, none in the Sayyid model, 13 [12%] in the Soeterik non-MRI model, and five [5%] in the Soeterik MRI model). SEPERA had higher net benefit than the other models to predict ssEPE, enabling more patients to safely undergo nerve-sparing. In the algorithmic audit, no evidence of model bias was observed, with no significant difference in AUROC when stratified by race, biopsy year, age, biopsy type (systematic only vs systematic and MRI-targeted biopsy), biopsy location (academic vs community), and D'Amico risk group. According to the audit, the most common errors were false positives, particularly for older patients with high-risk disease. No aggressive tumours (ie, grade >2 or high-risk disease) were found among false negatives.

INTERPRETATION

We demonstrated the accuracy, safety, and generalisability of using SEPERA to personalise nerve-sparing approaches during radical prostatectomy.

FUNDING

None.

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

Objective

There are many models to predict extracapsular extension (ECE) in patients with prostate cancer. We aimed to externally validate several models in a Japanese cohort.

Methods

We 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.

Results

We 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.

Conclusion

Models 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.

External Validation of Models for Prediction of Side-specific Extracapsular Extension in Prostate Cancer Patients Undergoing Radical Prostatectomy

BACKGROUND

Predicting the risk of side-specific extracapsular extension (ECE) is essential for planning nerve-sparing radical prostatectomy (RP) in patients with prostate cancer (PCa).

OBJECTIVE

To externally validate available models for prediction of ECE.

DESIGN, SETTING, AND PARTICIPANTS

Sixteen models were assessed in a cohort of 737 consecutive PCa patients diagnosed via multiparametric magnetic resonance imaging (MRI)-targeted and systematic biopsies and treated with RP between January 2016 and November 2021 at eight referral centers.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Model performance was evaluated in terms of discrimination using area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA).

RESULTS AND LIMITATIONS

Overall, ECE was identified in 308/1474 (21%) prostatic lobes. Prostatic lobes with ECE had higher side-specific clinical stage on digital rectal examination and MRI, number of positive biopsy cores, and International Society of Urological Pathology grade group in comparison to those without ECE (all p < 0.0001). Less optimistic performance was observed in comparison to previous published studies, although the models described by Pak, Patel, Martini, and Soeterik achieved the highest accuracy (AUC ranging from 0.73 to 0.77), adequate calibration for a probability threshold <40%, and the highest net benefit for a probability threshold >8% on DCA. Inclusion of MRI-targeted biopsy data and MRI information in models improved patient selection and clinical usefulness. Using model-derived cutoffs suggested by their authors, approximately 15% of positive surgical margins could have been avoided. Some available models were not included because of missing data, which constitutes a limitation of the study.

CONCLUSIONS

We report an external validation of models predicting ECE and identified the four with the best performance. These models should be applied for preoperative planning and patient counseling.

PATIENT SUMMARY

We validated several tools for predicting extension of prostate cancer outside the prostate gland. These tools can improve patient selection for surgery that spares nerves affecting recovery of sexual potency after removal of the prostate. They could potentially reduce the risk of finding cancer cells at the edge of specimens taken for pathology, a finding that suggests that not all of the cancer has been removed.

E-Cadherin, Integrin Alpha2 (Cd49b), and Transferrin Receptor-1 (Tfr1) Are Promising Immunohistochemical Markers of Selected Adverse Pathological Features in Patients Treated with Radical Prostatectomy

In patients treated for prostate cancer (PCa) with radical prostatectomy (RP), determining the risk of extraprostatic extension (EPE) and nodal involvement (NI) remains crucial for planning nerve-sparing and extended lymphadenectomy. The study aimed to determine proteins that could serve as immunohistochemical markers of locally advanced PCa. To select candidate proteins associated with adverse pathologic features (APF) reverse-phase protein array data of 498 patients was retrieved from The Cancer Genome Atlas. The analysis yielded 6 proteins which were then validated as predictors of APF utilizing immunohistochemistry in a randomly selected retrospective cohort of 53 patients. For univariate and multivariate analysis, logistic regression was used. Positive expression of TfR1 (OR 13.74; p = 0.015), reduced expression of CD49b (OR 10.15; p = 0.013), and PSA (OR 1.29; p = 0.013) constituted independent predictors of EPE, whereas reduced expression of e-cadherin (OR 10.22; p = 0.005), reduced expression of CD49b (OR 24.44; p = 0.017), and PSA (OR 1.18; p = 0.002) were independently associated with NI. Both models achieved high discrimination (AUROC 0.879 and 0.888, respectively). Immunohistochemistry constitutes a straightforward tool that might be easily utilized before RP. Expression of TfR1 and CD49b is associated with EPE, whereas expression of e-cadherin and CD49b is associated with NI. Since following immunohistochemical markers predicts respective APFs independently from PSA, in the future they might supplement existing preoperative nomograms or be implemented in novel tools.

External validation of a magnetic resonance imaging-based algorithm for prediction of side-specific extracapsular extension in prostate cancer

Introduction

Recently developed algorithm for prediction of side-specific extracapsular extension (ECE) of prostate cancer required validation before being recommended to use. The algorithm assumed that ECE on a particular side was not likely with same side maximum tumor diameter (MTD) <15 mm AND cancerous tissue in ipsilateral biopsy <15% AND PSA <20 ng/mL (both sides condition). The aim of the study was to validate this predictive tool in patients from another department.

Material and methods

Data of 154 consecutive patients (308 prostatic lateral lobes) were used for validation. Predictive factors chosen in the development set of patients were assessed together with other preoperative parameters using logistic regression to check for their significance. Sensitivity, specificity, negative and positive predictive values were calculated for bootstrapped risk-stratified validation dataset.

Results

Validation cohort did not differ significantly from development cohort regarding PSA, PSA density, Gleason score (GS), MTD, age, ECE and seminal vesicle invasion rate. In bootstrapped data set (n = 200 random sampling) algorithm revealed 70.2% sensitivity (95% confidence interval (CI) 58.8–83.0%), 49.9% specificity (95%CI: 42.0–57.7%), 83.9% negative predictive value (NPV; 95%CI: 76.1–91.4%) and 31.1% positive predictive value (PPV; 95%CI: 19.6–39.7%). When limiting analysis to high-risk patients (Gleason score >7) the algorithm improved its performance: sensitivity 91%, specificity 47%, PPV 53%, NPV 89%.

Conclusions

Analyzed algorithm is useful for identifying prostate lobes without ECE and deciding on ipsilateral nerve-sparing technique during radical prostatectomy, especially in patients with GS >7. Due to significant number of false positives in case of: MTD ≥15 mm OR cancer in biopsy ≥15% OR PSA ≥20 ng/mL additional evaluation is necessary to aid decision-making.

Multiparametric Magnetic Resonance Imaging-Based Peritumoral Radiomics for Preoperative Prediction of the Presence of Extracapsular Extension With Prostate Cancer

BACKGROUND

Preoperative prediction of extracapsular extension (ECE) of prostate cancer (PCa) is important to guide clinical decision-making and improve patient prognosis.

PURPOSE

To investigate the value of multiparametric magnetic resonance imaging (mpMRI)-based peritumoral radiomics for preoperative prediction of the presence of ECE.

STUDY TYPE

Retrospective.

POPULATION

Two hundred eighty-four patients with PCa from two centers (center 1: 226 patients; center 2: 58 patients). Cases from center 1 were randomly divided into training (158 patients) and internal validation (68 patients) sets. Cases from center 2 were assigned to the external validation set.

FIELD STRENGTH/SEQUENCE

A 3.0 T MRI scanners (three vendors). Sequence: Pelvic T2-weighted turbo/fast spin echo sequence and diffusion weighted echo planar imaging sequence.

ASSESSMENT

The peritumoral region (PTR) was obtained by 3-12 mm (half of the tumor length) 3D dilatation of the intratumoral region (ITR). Single-MRI radiomics signatures, mpMRI radiomics signatures, and integrated models, which combined clinical characteristics with the radiomics signatures were built. The discrimination ability was assessed by area under the receiver operating characteristic curve (AUC) in the internal and external validation sets.

STATISTICAL TESTS

Fisher's exact test, Mann-Whitney U-test, DeLong test.

RESULTS

The PTR radiomics signatures demonstrated significantly better performance than the corresponding ITR radiomics signatures (AUC: 0.674 vs. 0.554, P < 0.05 on T2-weighted, 0.652 vs. 0.546, P < 0.05 on apparent diffusion coefficient, 0.682 vs. 0.556 on mpMRI in the external validation set). The integrated models combining the PTR radiomics signature with clinical characteristics performed better than corresponding radiomics signatures in the internal validation set (eg. AUC: 0.718 vs. 0.671, P < 0.05 on mpMRI) but performed similar in the external validation set (eg. AUC: 0.684, vs. 0.682, P = 0.45 on mpMRI).

DATA CONCLUSION

The peritumoral radiomics can better predict the presence of ECE preoperatively compared with the intratumoral radiomics and may have better generalization than clinical characteristics. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: 2.

Value of adding the apparent diffusion coefficient to capsular contact for the prediction of extracapsular extension in prostate cancer

Objective

To determine whether evaluating the mean apparent diffusion coefficient (ADC) together with capsular contact (CC) adds value in the prediction of microscopic extracapsular extension (ECE) of prostate cancer.

Materials and Methods

Between January 2012 and December 2016, 383 patients underwent multiparametric magnetic resonance imaging (mpMRI) of the prostate. A total of 67 patients were selected for inclusion. Two radiologists (observers 1 and 2), working independently, performed qualitative and quantitative analyses of ECE, macroscopic ECE, and microscopic ECE. A third radiologist assessed the correlation with the clinical data, and two experienced pathologists reviewed all histopathological findings.

Results

Among the 67 patients, mpMRI showed lesions that were confined to the capsule in 44 (66.7%), had microscopic ECE in 12 (17.9%), and had macroscopic ECE in 11 (16.4%). There were no significant differences, in terms of the diagnostic accuracy, as measured by determining the area under the curve (AUC), of CC on T2-weighted images (CCT2), CC on diffusion-weighted imaging (CCDWI), and the mean ADC for the prediction of microscopic ECE, between observer 1 (AUC of 0.728, 0.691, and 0.675, respectively) and observer 2 (AUC of 0.782, 0.821, and 0.799, respectively). Combining the mean ADC with the CCT2 or CCDWI did not improve the diagnostic accuracy for either observer. There was substantial interobserver agreement for the qualitative evaluation of ECE, as demonstrated by the kappa statistic, which was 0.77 (0.66-0.87). The diagnostic accuracy (AUC) of the qualitative assessment for predicting microscopic ECE was 0.745 for observer 1 and 0.804 for observer 2, and the difference was less than significant. In a multivariate analysis, none of clinical or imaging parameters were found to be associated with ECE.

Conclusion

For the detection of microscopic ECE on mpMRI, CC appears to have good diagnostic accuracy, especially if the observer has considerable experience. Adding the mean ADC to the CCT2 or CCDWI does not seem to provide any significant improvement in that diagnostic accuracy.

Development and External Validation of a Novel Nomogram to Predict Side-specific Extraprostatic Extension in Patients with Prostate Cancer Undergoing Radical Prostatectomy

BACKGROUND

Prediction of side-specific extraprostatic extension (EPE) is crucial in selecting patients for nerve-sparing radical prostatectomy (RP).

OBJECTIVE

To develop and externally validate nomograms including multiparametric magnetic resonance imaging (mpMRI) information to predict side-specific EPE.

DESIGN, SETTING, AND PARTICIPANTS

A retrospective analysis of 1870 consecutive prostate cancer patients who underwent robot-assisted RP from 2014 to 2018 at three institutions.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Four multivariable logistic regression models were established, including combinations of patient-based and side-specific variables: prostate-specific antigen (PSA) density, highest ipsilateral International Society of Urological Pathology (ISUP) biopsy grade, ipsilateral percentage of positive cores on systematic biopsy, and side-specific clinical stage assessed by both digital rectal examination and mpMRI. Discrimination (area under the curve [AUC]), calibration, and net benefit of these models were assessed in the development cohort and two external validation cohorts.

RESULTS AND LIMITATIONS

On external validation, AUCs of the four models ranged from 0.80 (95% confidence interval [CI] 0.68-0.88) to 0.83 (95% CI 0.72-0.90) in cohort 1 and from 0.77 (95% CI 0.62-0.87) to 0.78 (95% CI 0.64-0.88) in cohort 2. The three models including mpMRI staging information resulted in relatively higher AUCs compared with the model without mpMRI information. No major differences between the four models regarding net benefit were established. The model based on PSA density, ISUP grade, and mpMRI T stage was superior in terms of calibration. Using this model with a cut-off of 20%, 1980/2908 (68%) prostatic lobes without EPE would be found eligible for nerve sparing, whereas non-nerve sparing would be advised in 642/832 (77%) lobes with EPE.

CONCLUSIONS

Our analysis resulted in a simple and robust nomogram for the prediction of side-specific EPE, which should be used to select patients for nerve-sparing RP.

PATIENT SUMMARY

We developed a prediction model that can be used to assess accurately the likelihood of tumour extension outside the prostate. This tool can guide patient selection for safe nerve-sparing surgery.

Independent external validation of nomogram to predict extracapsular extension in patients with prostate cancer

INTRODUCTION

The objective of this study was to perform an independent external validation of the Giganti-Coppola nomogram (GCN), which uses clinical and radiological parameters to predict prostate extracapsular extension (ECE) on the final pathology of patients undergoing radical prostatectomy (RP).

MATERIAL AND METHODS

Seventy-two patients diagnosed with prostate cancer (PCa), who were RP candidates from two institutions, were prospectively included. All patients underwent preoperative multi-parametric magnetic resonance imaging (mpMRI) at 1.5 T, without the use of an endorectal coil, with multiplanar images in T1WI, T2WI, DWI, and DCE. The AUC and a calibration graph were used to validate the nomogram, using the regression coefficients of the Giganti-Coppola study.

RESULTS

The original nomogram had an AUC of 0.90 (p = 0.001), with a sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 100%, 5.1%, 47.1%, 100%, and 48%, respectively. The calibration graph showed an overestimation of the nomogram for ECE.

CONCLUSION

The GCN has an adequate ability in predicting ECE; however, in our sample, it showed limited accuracy and overestimated likelihood of ECE in the final pathology of patients with PCa submitted to RP.

KEY POINTS

• Knowledge of preoperative local staging of prostate cancer is essential for surgical treatment. Extracapsular extension increases the chance of positive surgical margins. • Imaging modalities such as mpMRI alone does not have suitable accuracy in local staging. • Giganti-Coppola's nomogram achieved an adequate ability in predicting ECE.

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

INTRODUCTION

To 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 METHODS

Model 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.

RESULTS

A 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.

CONCLUSION

External 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.

Predicting side-specific prostate cancer extracapsular extension: a simple decision rule of PSA, biopsy, and MRI parameters

Objective

To develop an easy-to-use side-specific tool for the prediction of prostate cancer extracapsular extension (ECE) using clinical, biopsy, and MRI parameters.

Materials and methods

Retrospective analysis of patients who underwent radical prostatectomy preceded by staging multiparametric MRI of the prostate was performed. Multivariate logistic regression analysis was used to choose independent predictors of ECE. Continuous variables were transformed to categorical ones by choosing threshold values using spline knots or testing thresholds used in previously described models. Internal validation of the rule was carried out as well as validation of other algorithms on our group was performed.

Results

In the analyzed period of time, 88 out of 164 patients who underwent radical prostatectomy met inclusion criteria. ECE was evidenced at radical prostatectomy in 41 patients (46.6%) and in 53 lobes (30.1%). In the multivariate analysis PSA, total percentage of cancerous tissue in cores (%PCa) and maximum tumour diameter (MTD) of Likert 3–5 lesions on MRI were independent predictors of ECE. The following rule for predicting side-specific ECE was proposed: %PCa ≥ 15% OR MTD ≥ 15 mm OR PSA ≥ 20 ng/mL. Internal validation of the algorithm revealed safe lower confidence limits for sensitivity and NPV, proving that model offers accurate risk grouping that can be safely used in decision-making.

Conclusion

The rule developed in this study makes ECE prediction fast, intuitive, and side-specific. However, until validated externally it should be used with caution.