Prediction of pathological stage based on clinical stage, serum prostate-specific antigen, and biopsy Gleason score: Partin Tables in the contemporary era

Evaluation of complications and biochemical recurrence rates after (super) extended lymph node dissection during radical prostatectomy

OBJECTIVE

To evaluate the effectiveness of extended (e-PLND) and super-extended pelvic lymph node dissection (se-PLND) during robot-assisted radical prostatectomy (RARP) by examining lymph node (LN) yield, complications, LN metastasis, and biochemical recurrence (BCR) incidence.

METHODS

Between January 2016 and January 2020, 354 consecutive patients with > 5% risk of lymph node involvement (LNI), as predicted by the Memorial Sloan Kettering Cancer Center nomogram, underwent RARP with (s)e-PLND at a high-volume center. The e-PLND involved removing fibrofatty lymphatic tissue around the obturator fossa, internal iliac region, and external iliac vessels. The se-PLND, performed at the discretion of the surgeons, also included lymph nodes from the pre-sacral and common iliac regions. Outcomes included histopathological findings by anatomical region; complications; and BCR incidence during follow-up.

RESULTS

The median LNI risk was 18% (IQR 9-31%). A median of 22 LN (IQR 16-28) were removed, with se-PLND yielding a higher number: 25 (IQR 20-32) compared to e-PLND: 17 (IQR 13-24) (p < 0.001). pN1 disease was detected in 22% of patients overall, higher in se-PLND (29%) than e-PLND (14%) (p < 0.001). Of metastatic LNs, 14% were situated outside the e-PLND template. Operation time was longer for se-PLND, but perioperative complications were similar between both groups. After a median follow-up of 24 months (IQR 7-33), BCR incidence was comparable between the two groups.

CONCLUSION

Compared to standard extended pelvic lymph node dissection (PLND), super extended PLND increases lymph node yield and removal of metastatic deposits but does not contribute to progression free survival at mid-term.

Comparison of staging using [68Ga]Ga-PSMA-11 PET/CT and histopathological results in intermediate- and high-risk prostate cancer patients treated with radical prostatectomy and pelvic lymph node dissection

OBJECTIVE

To evaluate the diagnostic accuracy of [68Ga]Ga-PSMA-11 PET/CT (PET-PSMA) in local and loco-regional nodal staging compared with histopathological results in intermediate- and high-risk prostate cancer patients treated with radical prostatectomy (RP) and pelvic lymph node dissection (PLND).

MATERIALS Y METHODS

A total of 122 intermediate- and high-risk prostate cancer (PCa) patients staged with PET-PSMA and treated with RP (36/122) and RP plus PLND (86/122) from December 2018 to December 2023 were included. Visual and semiquantitative analysis findings using the SUVmax of the molecular imaging were correlated with histopathological results.

RESULTS

The primary tumor was visible by PET-PSMA in 96.7% of the patients. A positive correlation was found between PSA levels and SUVmax (Spearman's r: 0.303, p < 0.001). PET-PSMA detected nodal involvement in 25/89 patients (28.08%). The sensitivity, specificity, and diagnostic accuracy of PET-PSMA for detecting nodal involvement were 75%, 82.2%, and 80.9%, respectively. Patients with PSA levels >20 ng/mL, Gleason score ≥7b, ISUP grade >2, and extracapsular extension showed significantly higher SUVmax values. No differences were observed in SUVmax between risk groups or in other histopathological variables.

CONCLUSIONS

PET-PSMA is an effective tool for the initial staging of intermediate- and high-risk PCa. SUVmax values were significantly higher in patients with unfavorable clinical features.

Machine learning and explainable artificial intelligence to predict pathologic stage in men with localized prostate cancer

BACKGROUND

Though several nomograms exist, machine learning (ML) approaches might improve prediction of pathologic stage in patients with prostate cancer. To develop ML models to predict pathologic stage that outperform existing nomograms that use readily available clinicopathologic variables.

METHODS

Patients with prostate adenocarcinoma who underwent surgery were identified in the National Cancer Database. Seven ML models were trained to predict organ-confined (OC) disease, extracapsular extension, seminal vesicle invasion (SVI), and lymph node involvement (LNI). Model performance was measured using area under the curve (AUC) on a holdout testing data set. Clinical utility was evaluated using decision curve analysis (DCA). Performance metrics were confirmed on an external validation data set.

RESULTS

The ML-based extreme gradient boosted trees model achieved the best performance with an AUC of 0.744, 0.749, 0.816, 0.811 for the OC, ECE, SVI, and LNI models, respectively. The MSK nomograms achieved an AUC of 0.708, 0.742, 0.806, 0.802 for the OC, ECE, SVI, and LNI models, respectively. These models also performed the best on DCA. Findings were consistent on both a holdout internal validation data set as well as an external validation data set.

CONCLUSIONS

Our ML models better predicted pathologic stage relative to existing nomograms at predicting pathologic stage. Accurate prediction of pathologic stage can help oncologists and patients determine optimal definitive treatment options for patients with prostate cancer.

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

Objective

The 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 methods

We 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.

Results

Unfold 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%).

Conclusions

Unfold 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.

phi and phiD predict adverse pathological features after radical prostatectomy for prostate cancer in Chinese population

BACKGROUND

Anticipating the postoperative pathological stage and potential for adverse features of prostate cancer (PCa) patients before radical prostatectomy (RP) is crucial for guiding perioperative treatment.

METHODS

A cohort consisting of three sub-cohorts with a total of 709 patients has been enlisted from two major tertiary medical centres in China. The primary assessment parameters for adverse pathological features in this study are the pathological T stage, the AJCC prognostic stage groups and perineural invasion (PNI). Logistic regression analyses were performed to investigate the relationship between prostate specific antigen (PSA), its derivatives (incluing Prostate Health Index, phi and phi density, phiD), and the pathological outcomes after RP.

RESULTS

Both phi and phiD showed a significant association with pathologic T stage of pT3 or above (phi, adjusted OR, AOR = 2.82, 95% confidence interval, 95% CI: 1.88-4.23, p < 0.001; phiD, AOR = 2.47, 95% CI: 1.76-3.48, p < 0.001) and PNI (phi, AOR = 2.15, 95% CI: 1.39-3.32, p < 0.001; phiD, AOR = 1.94, 95% CI: 1.38-2.73, p < 0.001). In a subgroup analysis with a total PSA value <10 ng/mL, phi and phiD continued to show a significant correlation with pT3 or above (phi, AOR = 4.70, 95% CI: 1.29-17.12, p = 0.019; phiD, AOR = 3.44, 95% CI: 1.51-7.85, p = 0.003), and phiD also maintained its predictive capability for PNI in this subgroup (AOR = 2.10, 95% CI: 1.17-3.80, p = 0.014). Sensitivity analysis indicated that the findings in the combined cohort are mainly influenced by one of the sub-cohorts, partially attributable to disparities in sample sizes between sub-cohorts. Combined analysis of phi(D) and multiparametric MRI (mpMRI) data yielded similar results.

CONCLUSIONS

Preoperative measurement of serum phi and phiD is valuable for predicting the occurrence of adverse pathological features in Chinese PCa patients after RP.

Comparison between Three Radiomics Models and Clinical Nomograms for Prediction of Lymph Node Involvement in PCa Patients Combining Clinical and Radiomic Features

PURPOSE

We aim to compare the performance of three different radiomics models (logistic regression (LR), random forest (RF), and support vector machine (SVM)) and clinical nomograms (Briganti, MSKCC, Yale, and Roach) for predicting lymph node involvement (LNI) in prostate cancer (PCa) patients.

MATERIALS AND METHODS

The retrospective study includes 95 patients who underwent mp-MRI and radical prostatectomy for PCa with pelvic lymphadenectomy. Imaging data (intensity in T2, DWI, ADC, and PIRADS), clinical data (age and pre-MRI PSA), histological data (Gleason score, TNM staging, histological type, capsule invasion, seminal vesicle invasion, and neurovascular bundle involvement), and clinical nomograms (Yale, Roach, MSKCC, and Briganti) were collected for each patient. Manual segmentation of the index lesions was performed for each patient using an open-source program (3D SLICER). Radiomic features were extracted for each segmentation using the Pyradiomics library for each sequence (T2, DWI, and ADC). The features were then selected and used to train and test three different radiomics models (LR, RF, and SVM) independently using ChatGPT software (v 4o). The coefficient value of each feature was calculated (significant value for coefficient ≥ ±0.5). The predictive performance of the radiomics models and clinical nomograms was assessed using accuracy and area under the curve (AUC) (significant value for p ≤ 0.05). Thus, the diagnostic accuracy between the radiomics and clinical models were compared.

RESULTS

This study identified 343 features per patient (330 radiomics features and 13 clinical features). The most significant features were T2_nodulofirstordervariance and T2_nodulofirstorderkurtosis. The highest predictive performance was achieved by the RF model with DWI (accuracy 86%, AUC 0.89) and ADC (accuracy 89%, AUC 0.67). Clinical nomograms demonstrated satisfactory but lower predictive performance compared to the RF model in the DWI sequences.

CONCLUSIONS

Among the prediction models developed using integrated data (radiomics and semantics), RF shows slightly higher diagnostic accuracy in terms of AUC compared to clinical nomograms in PCa lymph node involvement prediction.

Biparametric MRI of the prostate radiomics model for prediction of pelvic lymph node metastasis in prostate cancers : a two-centre study

OBJECTIVES

Exploring the value of adding correlation analysis (radiomic features (RFs) of pelvic metastatic lymph nodes and primary lesions) to screen RFs of primary lesions in the feature selection process of establishing prediction model.

METHODS

A total of 394 prostate cancer (PCa) patients (263 in the training group, 74 in the internal validation group and 57 in the external validation group) from two tertiary hospitals were included in the study. The cases with pelvic lymph node metastasis (PLNM) positive in the training group were diagnosed by biopsy or MRI with a short-axis diameter ≥ 1.5 cm, PLNM-negative cases in the training group and all cases in validation group were underwent both radical prostatectomy (RP) and extended pelvic lymph node dissection (ePLND). The RFs of PLNM-negative lesion and PLNM-positive tissues including primary lesions and their metastatic lymph nodes (MLNs) in the training group were extracted from T2WI and apparent diffusion coefficient (ADC) map to build the following two models by fivefold cross-validation: the lesion model, established according to the primary lesion RFs selected by t tests and absolute shrinkage and selection operator (LASSO); the lesion-correlation model, established according to the primary lesion RFs selected by Pearson correlation analysis (RFs of primary lesions and their MLNs, correlation coefficient > 0.9), t test and LASSO. Finally, we compared the performance of these two models in predicting PLNM.

RESULTS

The AUC and the DeLong test of AUC in the lesion model and lesion-correlation model were as follows: training groups (0.8053, 0.8466, p = 0.0002), internal validation group (0.7321, 0.8268, p = 0.0429), and external validation group (0.6445, 0.7874, p = 0.0431), respectively.

CONCLUSION

The lesion-correlation model established by features of primary tumors correlated with MLNs has more advantages than the lesion model in predicting PLNM.

MRI assessment of seminal vesicle involvement by prostate cancer using T2 signal intensity and volume

BACKGROUND

Seminal vesicle involvement (SVI) in patients with newly diagnosed prostate cancer is associated with high rates of treatment failure and tumor recurrence; correct identification of SVI allows for effective management decisions and surgical planning.

METHODS

This single-center retrospective study analyzed MR images of the seminal vesicles from patients undergoing radical prostatectomy with confirmed T3b disease, comparing them to a control group without SVI matched for age and Gleason grade with a final stage of T2 or T3a. Seminal vesicles were segmented by an experienced uroradiologist, "raw" and bladder-normalized T2 signal intensity, as well as SV volume, were obtained.

RESULTS

Among the 82 patients with SVI, 34 (41.6%) had unilateral invasion, and 48 (58.4%) had bilateral disease. There was no statistically significant difference in the degree of distension between normal and involved seminal vesicles (P = 0.08). Similarly, no statistically significant difference was identified in the raw SV T2 signal intensity (P = 0.09) between the groups. In the 159 patients analyzed, SVI was prospectively suspected in 10 of 82 patients (specificity, 100%; sensitivity, 12.2%). In all these cases, lesions macroscopically invaded the seminal vesicle, and the raw T2 signal intensity was significantly lower than that in the SVI and control groups (P = 0.02 and 0.01).

CONCLUSION

While signal intensity measurements in T2-weighted images may provide insight into T3b disease, our findings suggest that this data alone is insufficient to reliably predict SVI, indicating the need for further investigation and complementary diagnostic approaches.

Development of a microultrasound-based nomogram to predict extra-prostatic extension in patients with prostate cancer undergoing robot-assisted radical prostatectomy

OBJECTIVES

To develop a microultrasound-based nomogram including clinicopathological parameters and microultrasound findings to predict the presence of extra-prostatic extension and guide the grade of nerve-sparing.

MATERIAL AND METHODS

All patients underwent microultrasound the day before robot-assisted radical prostatectomy. Variables significantly associated with extra-prostatic extension at univariable analysis were used to build the multivariable logistic model, and the regression coefficients were used to develop the nomogram. The model was subjected to 1000 bootstrap resamples for internal validation. The performance of the microultrasound-based model was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA).

RESULTS

Overall, 122/295 (41.4%) patients had a diagnosis of extra-prostatic extension on definitive pathology. Microultrasound correctly identify extra-prostatic extension in 84/122 (68.9%) cases showing a sensitivity and a specificity of 68.9% and 84.4%, with an AUC of 76.6%. After 1000 bootstrap resamples, the predictive accuracy of the microultrasound-based model was 85.9%. The calibration plot showed a satisfactory concordance between predicted probabilities and observed frequencies of extra-prostatic extension. The DCA showed a higher clinical net-benefit compared to the model including only clinical parameters. Considering a 4% cut-off, nerve-sparing was recommended in 173 (58.6%) patients and extra-prostatic extension was detected in 32 (18.5%) of them.

CONCLUSION

We developed a microultrasound-based nomogram for the prediction of extra-prostatic extension that could aid in the decision whether to preserve or not neurovascular bundles. External validation and a direct comparison with mpMRI-based nomogram is crucial to corroborate our results.

The 2012 Briganti nomogram predicts disease progression after surgery in high-risk prostate cancer patients

Objectives

We tested whether the 2012 Briganti nomogram for the risk of pelvic lymph node invasion (PLNI) may represent a predictor of disease progression after surgical management in high-risk (HR) prostate cancer (PCa) patients according to the European Association of Urology.

Methods

Between January 2013 and December 2021, HR PCa patients treated with robot-assisted radical prostatectomy (RARP) and extended pelvic lymph node dissection (ePLND) were identified. The 2012 Briganti nomogram was evaluated as a continuous and categorical variable, which was dichotomized using the median. The risk of disease progression, defined as the event of biochemical recurrence and/or local recurrence/distant metastases was assessed by Cox regression models.

Results

Overall, 204 patients were identified. The median 2012 Briganti nomogram score resulted 12.0% (IQR: 6.0-22.0%). PLNI was detected in 57 (27.9%) cases. Compared to patients who had preoperatively a 2012 Briganti nomogram score ≤12%, those with a score >12% were more likely to present with higher percentage of biopsy positive cores, palpable tumors at digital rectal examination, high-grade cancers at prostate biopsies, and unfavorable pathology in the surgical specimen. At multivariable Cox regression analyses, disease progression, which occurred in 85 (41.7%) patients, was predicted by the 2012 Briganti nomogram score (HR: 1.02; 95%CI: 1.00-1.03; p = 0.012), independently by tumors presenting as palpable (HR: 1.78; 95%CI: 1.10.2.88; p = 0.020) or the presence of PLNI in the surgical specimen (HR: 3.73; 95%CI: 2.10-5.13; p = 0.012).

Conclusions

The 2012 Briganti nomogram represented an independent predictor of adverse prognosis in HR PCa patients treated with RARP and ePLND. As the score increased, so patients were more likely to experience disease progression, independently by the occurrence of PLNI. The association between the nomogram, unfavorable pathology and tumor behavior might turn out to be useful for selecting a subset of patients needing different treatment paradigms in HR disease.

A non-invasive 25-Gene PLNM-Score urine test for detection of prostate cancer pelvic lymph node metastasis

BACKGROUND

Prostate cancer patients with pelvic lymph node metastasis (PLNM) have poor prognosis. Based on EAU guidelines, patients with >5% risk of PLNM by nomograms often receive pelvic lymph node dissection (PLND) during prostatectomy. However, nomograms have limited accuracy, so large numbers of false positive patients receive unnecessary surgery with potentially serious side effects. It is important to accurately identify PLNM, yet current tests, including imaging tools are inaccurate. Therefore, we intended to develop a gene expression-based algorithm for detecting PLNM.

METHODS

An advanced random forest machine learning algorithm screening was conducted to develop a classifier for identifying PLNM using urine samples collected from a multi-center retrospective cohort (n = 413) as training set and validated in an independent multi-center prospective cohort (n = 243). Univariate and multivariate discriminant analyses were performed to measure the ability of the algorithm classifier to detect PLNM and compare it with the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram score.

RESULTS

An algorithm named 25 G PLNM-Score was developed and found to accurately distinguish PLNM and non-PLNM with AUC of 0.93 (95% CI: 0.85-1.01) and 0.93 (95% CI: 0.87-0.99) in the retrospective and prospective urine cohorts respectively. Kaplan-Meier plots showed large and significant difference in biochemical recurrence-free survival and distant metastasis-free survival in the patients stratified by the 25 G PLNM-Score (log rank P < 0.001 and P < 0.0001, respectively). It spared 96% and 80% of unnecessary PLND with only 0.51% and 1% of PLNM missing in the retrospective and prospective cohorts respectively. In contrast, the MSKCC score only spared 15% of PLND with 0% of PLNM missing.

CONCLUSIONS

The novel 25 G PLNM-Score is the first highly accurate and non-invasive machine learning algorithm-based urine test to identify PLNM before PLND, with potential clinical benefits of avoiding unnecessary PLND and improving treatment decision-making.

Use of PIRADS 2.1 to predict capsular invasion in patients with radiologic T3a prostate cancer

Objective

Using multi-parametric magnetic resonance imaging (mpMRI) to identify patients with clinical T3a (cT3a) who were overestimated on mpMRI with final pathological T2 (pT2). To suggest that the neurovascular bundle (NVB) can be preserved by evaluating the characteristics of patients according to their pathological grade among cT3a prostate cancer (PCa) patients using mpMRI.

Materials and methods

Patients who underwent robot-assisted laparoscopic radical prostatectomy (RALP) were retrospectively analyzed and those patients with clinical T3aN0M0 were enrolled. These enrolled patients were divided into a localized cancer group with pT2 PCa and a locally advanced group with pT3a or higher. Factors affecting the diagnosis of localized PCa after RALP in patients with cT3a PCa were evaluated.

Results

Among the preoperative parameters of patients with cT3a PCa, the prostate specific antigen density (PSAD) (OR: 3.76, 95% CI: 1.85-7.64, p<0.001), international society of urological pathology (ISUP) grade (p<0.05), and index lesion size (OR: 1.44, 95% CI: 1.85-7.64, p<0.001) were significantly associated with pathological locally advanced PCa. Optimal cut-off values for prediction of pT3a or higher were 0.36 ng/mL2 for PSAD (sensitivity: 55.7%, specificity: 70.8%), 1.77 cm for index lesion size (sensitivity: 54.3%, specificity: 66.0%), and 2.5 for ISUP grading (sensitivity: 67.6%, specificity: 53.2%). For prediction of pT3a or higher among patients with cT3a PCa, a nomogram was developed using ISUP grade, index lesion size, and PSAD on prostate biopsy (area under the curve: 0.71, 95% CI: 0.670-0.754, p<0.001). PSAD less than 0.36 index lesion size less than 1.77 cm, and biopsy ISUP grade 1-2 are highly likely to indicate that there is no actual extracapsular extension in cT3a PCa patients.

Conclusions

PSAD, ISUP, and index lesion size showed significant associations with the classification of pathologic localized and locally advanced PCa in patients with cT3a PCa. A nomogram including these features can predict the diagnosis of locally advanced PCa in patients with cT3a PCa.

Development of a nomogram to predict survival in advanced biliary tract cancer

The prognosis of advanced biliary tract cancer (BTC) patients remains poor due to limited efficacy of chemotherapy and difficulties in management. Thus, prediction of survival is crucial for the clinical management of advanced BTC. The aim was to develop and validate a nomogram to predict 6-month and 12-month survival in advanced BTC patients treated with chemotherapy. A multivariable Cox regression model was used to construct a nomogram in a training set (JCOG1113, a phase III trial comparing gemcitabine plus S-1 [GS] and gemcitabine plus cisplatin, n = 351). External validity of the nomogram was assessed using a test set (JCOG0805, a randomized, phase II trial comparing GS and S-1 alone, n = 100). Predictive performance was assessed in terms of discrimination and calibration. The constructed nomogram included lymph node metastasis, liver metastasis, carbohydrate antigen 19-9, carcinoembryonic antigen, albumin, and C-reactive protein. Uno's concordance index was 0.661 (95% confidence interval [CI] 0.629-0.696) in the training set and 0.640 (95% CI 0.566-0.715) in the test set. The calibration plots for 6-month and 12-month survival showed good agreement in the two analysis sets. The present nomogram can facilitate prediction of the prognosis of advanced BTC patients treated with chemotherapy and help clinicians' prognosis-based decision-making.

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

Background

The aim of the study was to investigate the predictive value of the nutritional risk index (NRI) for extracapsular extension (ECE) and seminal vesicle invasion (SVI) in prostate cancer (PCa) patients undergoing radical prostatectomy (RP), and further develop and validate predictive nomograms for ECE and SVI based on the NRI.

Methods

We retrospectively analyzed 734 PCa patients who underwent RP between 2010 and 2020 in the Department of Urology at Peking University Third Hospital. The enrolled patients were randomly divided into a primary cohort (n = 489) and a validation cohort (n = 245) in a 2:1 manner. The baseline NRI of patients was calculated using serum albumin level and body mass index, and a malnutrition status was defined as NRI ≤ 98. Univariate and multivariate logistic regression analyses were conducted to identify predictors for ECE and SVI. Nomograms for predicting ECE and SVI were established based on the results of the multivariate logistic regression analysis. The performance of the nomograms was estimated using Harrell's concordance index (C-index), the area under curve (AUC) of receiver operating characteristic (ROC) curves and the calibration curves.

Results

In the primary cohort, 70 (14.3%) patients with NRI ≤ 98 were classified as malnutrition, while the remaining 419 (85.7%) patients with NRI > 98 were considered to have normal nutrition. The nomograms for predicting ECE and SVI shared common factors including NRI, percentage of positive biopsy cores (PPC) and biopsy Gleason score, while prostate-specific antigen (PSA) levels and PSA density (PSAD) were only incorporated in ECE nomogram. The C-indexes of the nomograms for predicting ECE and SVI were 0.785 (95% confidence interval (CI): 0.745 - 0.826) and 0.852 (95% CI: 0.806 - 0.898), respectively. The calibration curves demonstrated excellent agreement between the predictions by the nomograms and the actual observations. The results remained reproducible when the nomograms were applied to the validation cohort.

Conclusions

The NRI is significantly associated with ECE and SVI in PCa patients. The nomogram established based on the NRI in our study can provide individualized risk estimation for ECE and SVI in PCa patients, and may be valuable for clinicians in making well-informed decisions regarding treatment strategies and patient management.

Comprehensive analysis of predictive factors for upstaging in intraprostatic cancer after radical prostatectomy: Different patterns of spread exist in lesions at different locations

BACKGROUND

Accurate assessment of the clinical staging is crucial for determining the need for radical prostatectomy (RP) in prostate cancer (PCa). However, the current methods for PCa staging may yield incorrect results. This study aimed to comprehensively analyze independent predictors of postoperative upstaging of intraprostatic cancer.

METHODS

We conducted a retrospective analysis of data from intraprostatic cancer patients who underwent radical surgery between March 2019 and December 2022. Intraprostatic cancer was defined as a lesion confined to the prostate, excluding cases where multiparameter magnetic resonance imaging (mpMRI) showed the lesion in contact with the prostatic capsule. We assessed independent predictors of extraprostatic extension (EPE) and analyzed their association with positive surgical margin (PSM) status. In addition, based on the distance of the lesion from the capsule on mpMRI, we divided the patients into non-transition zone and transition zone groups for further analysis.

RESULTS

A total of 500 patients were included in our study. Logistic regression analysis revealed that biopsy Gleason grade group (GG) (odds ratio, OR: 1.370, 95% confidence interval, CI: 1.093-1.718) and perineural invasion (PNI) (OR: 2.746, 95% CI: 1.420-5.309) were predictive factors for postoperative EPE. Both biopsy GG and PNI were associated with lateral (GG: OR: 1.270, 95% CI: 1.074-1.501; PNI: OR: 2.733, 95% CI: 1.521-4.911) and basal (GG: OR: 1.491, 95% CI: 1.194-1.862; PNI: OR: 3.730, 95% CI: 1.929-7.214) PSM but not with apex PSM (GG: OR: 1.176, 95% CI: 0.989-1.399; PNI: OR: 1.204, 95% CI: 0.609-2.381) after RP. Finally, PNI was an independent predictor of EPE in the transition zone (OR: 11.235, 95% CI: 2.779-45.428) but not in the non-transition zone (OR: 1.942, 95% CI: 0.920-4.098).

CONCLUSION

PNI and higher GG may indicate upstaging of tumors in patients with intraprostatic carcinoma. These two factors are associated with PSM in locations other than the apex of the prostate. Importantly, cancer in the transition zone of the prostate is more likely to spread externally through nerve invasion than cancer in the non-transition zone.

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.

Development and external validation of a multivariable [68Ga]Ga-PSMA-11 PET-based prediction model for lymph node involvement in men with intermediate or high-risk prostate cancer

PURPOSE

To develop and evaluate a lymph node invasion (LNI) prediction model for men staged with [68Ga]Ga-PSMA-11 PET.

METHODS

A consecutive sample of intermediate to high-risk prostate cancer (PCa) patients undergoing [68Ga]Ga-PSMA-11 PET, extended pelvic lymph node dissection (ePLND), and radical prostatectomy (RP) at two tertiary referral centers were retrospectively identified. The training cohort comprised 173 patients (treated between 2013 and 2017), the validation cohort 90 patients (treated between 2016 and 2019). Three models for LNI prediction were developed and evaluated using cross-validation. Optimal risk-threshold was determined during model development. The best performing model was evaluated and compared to available conventional and multiparametric magnetic resonance imaging (mpMRI)-based prediction models using area under the receiver operating characteristic curves (AUC), calibration plots, and decision curve analysis (DCA).

RESULTS

A combined model including prostate-specific antigen, biopsy Gleason grade group, [68Ga]Ga Ga-PSMA-11 positive volume of the primary tumor, and the assessment of the [68Ga]Ga-PSMA-11 report N-status yielded an AUC of 0.923 (95% CI 0.863-0.984) in the external validation. Using a cutoff of  ≥ 17%, 44 (50%) ePLNDs would be spared and LNI missed in one patient (4.8%). Compared to conventional and MRI-based models, the proposed model showed similar calibration, higher AUC (0.923 (95% CI 0.863-0.984) vs. 0.700 (95% CI 0.548-0.852)-0.824 (95% CI 0.710-0.938)) and higher net benefit at DCA.

CONCLUSIONS

Our results indicate that information from [68Ga]Ga-PSMA-11 may improve LNI prediction in intermediate to high-risk PCa patients undergoing primary staging especially when combined with clinical parameters. For better LNI prediction, future research should investigate the combination of information from both PSMA PET and mpMRI for LNI prediction in PCa patients before RP.

Extracapsular extension of transitional zone prostate cancer miss-detected by multiparametric magnetic resonance imaging

OBJECTIVES

To demonstrate the importance of extracapsular extension (ECE) of transitional zone (TZ) prostate cancer (PCa), examine the causes of its missed detection by Mp-MRI, and develop a new predictive model by integrating multi-level clinical variables.

MATERIALS AND METHODS

This retrospective study included 304 patients who underwent laparoscopic radical prostatectomy after 12 + X needle transperineal transrectal ultrasound (TRUS)-MRI-guided targeted prostate biopsy from 2018 to 2021 in our center was performed.

RESULTS

In this study, the incidence rates of ECE were similar in patients with MRI lesions in the peripheral zone (PZ) and TZ (P = 0.66). However, the missed detection rate was higher in patients with TZ lesions than in those with PZ lesions (P < 0.05). These missed detections result in a higher positive surgical margin rate (P < 0.05). In patients with TZ lesions, detected MP-MRI ECE may have grey areas: the longest diameters of the MRI lesions were 16.5-23.5 mm; MRI lesion volumes were 0.63-2.51 ml; MRI lesion volume ratios were 2.75-8.86%; PSA were 13.85-23.05 ng/ml. LASSO regression was used to construct a clinical prediction model for predicting the risk of ECE in TZ lesions from the perspective of MRI and clinical features, including four variables: the longest diameter of MRI lesions, TZ pseudocapsule invasion, ISUP grading of biopsy pathology, and number of positive biopsy needles.

CONCLUSIONS

Patients with MRI lesions in the TZ have the same incidence of ECE as those with lesions in the PZ, but a higher missed detection rate.

Added value of shear-wave elastography in the prediction of extracapsular extension and seminal vesicle invasion before radical prostatectomy

The purpose of this study was to analyze the value of transrectal shear-wave elastography (SWE) in combination with multivariable tools for predicting adverse pathological features before radical prostatectomy (RP). Preoperative clinicopathological variables, multiparametric magnetic resonance imaging (mp-MRI) manifestations, and the maximum elastic value of the prostate (Emax) on SWE were retrospectively collected. The accuracy of SWE for predicting adverse pathological features was evaluated based on postoperative pathology, and parameters with statistical significance were selected. The diagnostic performance of various models, including preoperative clinicopathological variables (model 1), preoperative clinicopathological variables + mp-MRI (model 2), and preoperative clinicopathological variables + mp-MRI + SWE (model 3), was evaluated with area under the receiver operator characteristic curve (AUC) analysis. Emax was significantly higher in prostate cancer with extracapsular extension (ECE) or seminal vesicle invasion (SVI) with both P < 0.001. The optimal cutoff Emax values for ECE and SVI were 60.45 kPa and 81.55 kPa, respectively. Inclusion of mp-MRI and SWE improved discrimination by clinical models for ECE (model 2 vs model 1, P = 0.031; model 3 vs model 1, P = 0.002; model 3 vs model 2, P = 0.018) and SVI (model 2 vs model 1, P = 0.147; model 3 vs model 1, P = 0.037; model 3 vs model 2, P = 0.134). SWE is valuable for identifying patients at high risk of adverse pathology.

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.

Combining prostate health index and multiparametric magnetic resonance imaging may better predict extraprostatic extension after radical prostatectomy

BACKGROUND

In patients undergoing radical prostatectomy (RP) for prostate cancer (PCa), preoperative prediction of extraprostatic extension (EPE) can facilitate patient selection for nerve-sparing procedures. Since both multiparametric magnetic resonance imaging (mpMRI) and prostate health index (PHI) have shown promise for the diagnosis and prognostication of PCa, we investigated whether a combination of mpMRI and PHI evaluations can improve the prediction of EPE after RP.

METHODS

Patients diagnosed with PCa and treated with RP were prospectively enrolled between February 2017 and July 2019. Preoperative blood samples were analyzed for PHI (defined as [p2PSA/fPSA] × √tPSA), and mpMRI examinations were performed and interpreted by a single experienced uroradiologist retrospectively. The area under the receiver operating characteristic curve (ROC) was used to determine the performance of mpMRI, PHI, and their combination in predicting EPE after RP.

RESULTS

A total of 163 patients were included for analysis. The pathological T stage was T3a or more in 59.5%. Overall staging accuracy of mpMRI for EPE was 72.4% (sensitivity and specificity: 73.2% and 71.2%, respectively). The area under the ROC of the combination of mpMRI and PHI in predicting EPE (0.785) was higher than those of mpMRI alone (0.717; p = 0.0007) and PHI alone (0.722; p = 0.0236). mpMRI showed false-negative non-EPE results in 26 patients (16%), and a PHI threshold of >40 could avoid undiagnosed EPE before RP in 21 of these 26 patients.

CONCLUSION

The combination of PHI and mpMRI may better predict the EPE preoperatively, facilitating preoperative counseling and tailoring the need for nerve-sparing RP.

Selecting lymph node-positive patients for adjuvant therapy after radical prostatectomy and extended pelvic lymphadenectomy: An outcome analysis of 100 node-positive patients managed without adjuvant therapy

Objective

The aim of the study is to evaluate the effect of deferred androgen deprivation therapy on biochemical recurrence (BCR) and other survival parameters in node-positive prostate cancer patients after robot-assisted radical prostatectomy with bilateral extended pelvic lymph node dissection (RARP + EPLND).

Materials and methods

Of the 453 consecutive RARP procedures performed from 2011 to 2018, 100 patients with no prior use of androgen deprivation therapy were found to be lymph node (LN) positive and were observed, with initiation of salvage treatment at the time of BCR only. Patients were divided into 1 or 2 LNs (67)-and more than 2 LNs (33)-positive groups to assess survival outcomes.

Results

At a median follow-up of 21 months (1-70 months), the LN group (p < 0.000), preoperative prostate-specific antigen (PSA, p = 0.013), tumor volume (TV, p = 0.031), and LND (p = 0.004) were significantly associated with BCR. In multivariate analysis, only the LN group (p = 0.035) and PSA level (p = 0.026) were statistically significant. The estimated BCR-free survival rates in the 1/2 LN group were 37.6% (27%-52.2%), 26.5% (16.8%-41.7%), and 19.9% (9.6%-41.0%) at 1, 3, and 5 years, respectively, with a hazard of developing BCR of 0.462 (0.225-0.948) compared with the more than 2 LN-positive group. Estimated 5-year overall survival, cancer-specific, metastasis-free, and local recurrence-free survival rates were 88.4% (73.1%-100%), 89.5% (74%-100%), 65.1% (46.0%-92.1%), and 94.8% (87.2%-100.0%), respectively, for which none of the factors were significant. Based on cutoff values for PSA, TV, and LND of 30 ng/mL, 30%, and 10%, respectively, the 1/2 LN group was substratified, wherein the median BCR-free survival for the low- and intermediate-risk groups was 40 and 12 months, respectively.

Conclusions

Nearly one fourth and one fifth of 1/2 node-positive patients were BCR-free at 3 and 5 years after RARP + EPLND. Further substratification using PSA, TV, and LN density may help in providing individualized care regarding the initiation of adjuvant therapy.

Multiparametric MRI for Staging of Prostate Cancer: A Multicentric Analysis of Predictive Factors to Improve Identification of Extracapsular Extension before Radical Prostatectomy

Simple Summary

In this multicentric study, we tested the accuracy of multiparametric magnetic resonance imaging (mpMRI) in detecting extracapsular extension (ECE) out of the prostate in order to plan surgical sparing of neurovascular bundles in radical prostatectomy. Univariate and multivariate logistic regression analyses were performed to identify other risk factors for ECE. We found that it has a good ability to exclude extracapsular extension but a poor ability to identify it correctly. Risk factors other than mpMRI that predicted ECE were as follows: prostatic specific antigen, digital rectal examination, ratio of positive cores, and biopsy grade group. We suggest that using mpMRI exclusively should not be recommended to decide on surgical approaches.

Abstract

The correct identification of extracapsular extension (ECE) of prostate cancer (PCa) on multiparametric magnetic resonance imaging (mpMRI) is crucial for surgeons in order to plan the nerve-sparing approach in radical prostatectomy. Nerve-sparing strategies allow for better outcomes in preserving erectile function and urinary continence, notwithstanding this can be penalized with worse oncologic results. The aim of this study was to assess the ability of preoperative mpMRI to predict ECE in the final prostatic specimen (PS) and identify other possible preoperative predictive factors of ECE as a secondary end-point. We investigated a database of two high-volume hospitals to identify men who underwent a prostate biopsy with a pre-biopsy mpMRI and a subsequent RP. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of mpMRI in predicting ECE were calculated. A univariate analysis was performed to find the association between image staging and pathological staging. A multivariate logistic regression was performed to investigate other preoperative predictive factors. A total of 1147 patients were selected, and 203 out of the 1147 (17.7%) patients were classified as ECE according to the mpMRI. ECE was reported by pathologists in 279 out of the 1147 PS (24.3%). The PPV was 0.58, the NPV was 0.72, the sensitivity was 0.32, and the specificity was 0.88. The multivariate analysis found that PSA (OR 1.057, C.I. 95%, 1.016–1.100, p = 0.006), digital rectal examination (OR 0.567, C.I. 95%, 0.417–0.770, p = 0.0001), ratio of positive cores (OR 9.687, C.I. 95%, 3.744–25.006, p = 0.0001), and biopsy grade in prostate biopsy (OR 1.394, C.I. 95%, 1.025–1.612, p = 0.0001) were independent factors of ECE. The mpMRI has a great ability to exclude ECE, notwithstanding that low sensitivity is still an important limitation of the technique.

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

OBJECTIVES

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.

METHODS

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.

RESULTS

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 et al. 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%.

CONCLUSION

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.

Prognostic Genomic Tissue-Based Biomarkers in the Treatment of Localized Prostate Cancer

In localized prostate cancer clinicopathologic variables have been used to develop prognostic nomograms quantifying the probability of locally advanced disease, of pelvic lymph node and distant metastasis at diagnosis or the probability of recurrence after radical treatment of the primary tumor. These tools although essential in daily clinical practice for the management of such a heterogeneous disease, which can be cured with a wide spectrum of treatment strategies (i.e., active surveillance, RP and radiation therapy), do not allow the precise distinction of an indolent instead of an aggressive disease. In recent years, several prognostic biomarkers have been tested, combined with the currently available clinicopathologic prognostic tools, in order to improve the decision-making process. In the following article, we reviewed the literature of the last 10 years and gave an overview report on commercially available tissue-based biomarkers and more specifically on mRNA-based gene expression classifiers. To date, these genomic tests have been widely investigated, demonstrating rigorous quality criteria including reproducibility, linearity, analytical accuracy, precision, and a positive impact in the clinical decision-making process. Albeit data published in literature, the systematic use of these tests in prostate cancer is currently not recommended due to insufficient evidence.

Integration of magnetic resonance imaging into prostate cancer nomograms

The decision whether to undergo prostate biopsy must be carefully weighed. Nomograms have widely been utilized as risk calculators to improve the identification of prostate cancer by weighing several clinical factors. The recent inclusion of multiparametric magnetic resonance imaging (mpMRI) findings into nomograms has drastically improved their nomogram's accuracy at identifying clinically significant prostate cancer. Several novel nomograms have incorporated mpMRI to aid in the decision-making process in proceeding with a prostate biopsy in patients who are biopsy-naïve, have a prior negative biopsy, or are on active surveillance. Furthermore, novel nomograms have incorporated mpMRI to aid in treatment planning of definitive therapy. This literature review highlights how the inclusion of mpMRI into prostate cancer nomograms has improved upon their performance, potentially reduce unnecessary procedures, and enhance the individual risk assessment by improving confidence in clinical decision-making by both patients and their care providers.

Development of a Radiomic-Based Model Predicting Lymph Node Involvement in Prostate Cancer Patients

Simple Summary

In patients with prostate cancer, lymph node involvement is a risk factor of relapse. Current guidelines recommend extended lymph node dissection to better stage the disease. However, such a surgical procedure is associated with a higher morbidity than limited lymph node dissection. A better selection of patients is thus essential. Radiomics features are quantitative features automatically extracted from medical imaging. Combining clinical and radiomics features, a machine learning-based model seemed to provide added predictive performance compared to state of the art models regarding the risk prediction of lymph-node involvement in prostate cancer patients.

Abstract

Significant advances in lymph node involvement (LNI) risk modeling in prostate cancer (PCa) have been achieved with the addition of visual interpretation of magnetic resonance imaging (MRI) data, but it is likely that quantitative analysis could further improve prediction models. In this study, we aimed to develop and internally validate a novel LNI risk prediction model based on radiomic features extracted from preoperative multimodal MRI. All patients who underwent a preoperative MRI and radical prostatectomy with extensive lymph node dissection were retrospectively included in a single institution. Patients were randomly divided into the training (60%) and testing (40%) sets. Radiomic features were extracted from the index tumor volumes, delineated on the apparent diffusion coefficient corrected map and the T2 sequences. A ComBat harmonization method was applied to account for inter-site heterogeneity. A prediction model was trained using a neural network approach (Multilayer Perceptron Network, SPSS v24.0©) combining clinical, radiomic and all features. It was then evaluated on the testing set and compared to the current available models using the Receiver Operative Characteristics and the C-Index. Two hundred and eighty patients were included, with a median age of 65.2 y (45.3–79.6), a mean PSA level of 9.5 ng/mL (1.04–63.0) and 79.6% of ISUP ≥ 2 tumors. LNI occurred in 51 patients (18.2%), with a median number of extracted nodes of 15 (10–19). In the testing set, with their respective cutoffs applied, the Partin, Roach, Yale, MSKCC, Briganti 2012 and 2017 models resulted in a C-Index of 0.71, 0.66, 0.55, 0.67, 0.65 and 0.73, respectively, while our proposed combined model resulted in a C-Index of 0.89 in the testing set. Radiomic features extracted from the preoperative MRI scans and combined with clinical features through a neural network seem to provide added predictive performance compared to state of the art models regarding LNI risk prediction in PCa.

Selection of patients for nerve sparing surgery in robot‐assisted radical prostatectomy

Context

Robot‐assisted radical prostatectomy (RARP) has become the standard surgical procedure for localized prostate‐cancer (PCa). Nerve‐sparing surgery (NSS) during RARP has been associated with improved erectile function and continence rates after surgery. However, it remains unclear what are the most appropriate indications for NSS.

Objective

The objective of this study is to systematically review the available parameters for selection of patients for NSS. The weight of different clinical variables, multiparametric magnetic‐resonance‐imaging (mpMRI) findings, and the impact of multiparametric‐nomograms in the decision‐making process on (side‐specific) NSS were assessed.

Evidence acquisition

This systematic review searched relevant databases and included studies performed from January 2000 until December 2020 and recruited a total of 15 840 PCa patients. Studies were assessed that defined criteria for (side‐specific) NSS and associated them with oncological safety and/or functional outcomes. Risk of bias assessment was performed.

Evidence synthesis

Nineteen articles were eligible for full‐text review. NSS is primarily recommended in men with adequate erectile function, and with low‐risk of extracapsular extension (ECE) on the side‐of NSS. Separate clinical and radiological variables have low accuracy for predicting ECE, whereas nomograms optimize the risk‐stratification and decision‐making process to perform or to refrain from NSS when oncological safety (organ‐confined disease, PSM rates) and functional outcomes (erectile function and continence rates) were assessed.

Conclusions

Consensus exists that patients who are at high risk of ECE should refrain from NSS. Several multiparametric preoperative nomograms were developed to predict ECE with increased accuracy compared with single clinical, pathological, or radiological variables, but controversy exists on risk thresholds and decision rules on a conservative versus a less‐conservative surgical approach. An individual clinical judgment on the possibilities of NSS set against the risks of ECE is warranted.

Patient summary

NSS is aimed at sparing the nerves responsible for erection. NSS may lead to unfavorable tumor control if the risk of capsule penetration is high. Nomograms predicting extraprostatic tumor‐growth are probably most helpful.

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.

The prognostic value of digital rectal exam for the existence of advanced pathologic features after prostatectomy

BACKGROUND

Accurate staging at the time of prostate cancer diagnosis is fundamental to risk stratification and management counseling. Digital rectal exam (DRE) is foundational in clinical staging of prostate cancer, even with a known limited interexaminer agreement and poor sensitivity for detecting extraprostatic disease. We sought to evaluate the prognostic value of DRE for the presence of advanced pathologic features (APFs) following radical prostatectomy (RP).

METHODS

All patients undergoing RP as primary treatment for clinically localized prostate cancer in the National Cancer Database between 2008 and 2014 were identified. Patients with additional malignancies, prior treatment with radiation or systemic therapy, incongruent clinical staging and DRE findings or without fully evaluable clinical staging were excluded. The primary outcome was the presence of postsurgical APFs, defined as positive surgical margins, nodal disease, or pathologic stage T3 or greater. Multivariable logistic regression analysis was performed to account for prostate-specific antigen (PSA), biopsy grade group, percent of positive biopsy cores, and clinical stage.

RESULTS

In total, 91,525 patients consisting of 69,182 cT1, 20,641 cT2, and 1702 cT3-T4 were included. The average age was 61.1 ± 7.0 years, and the average PSA was 8.6 ± 10.3 ng/ml. On multivariable analysis, cT3 and T4 were associated with the presence of APFs (odds ratio [OR] 11.12, p < .01 and 5.28, p = .04), however, cT2 was only slightly associated with the presence of APFs when compared with cT1 (OR 1.15, p < .01). Furthermore, cT2 was associated with more node-positive disease (OR 1.63, p < .01), positive margins (OR 1.06, p < .01), and more than or equal to pT3 disease (OR 1.22, p < .01).

CONCLUSIONS

Overall, advanced clinical stage as assessed by DRE was independently associated with an increasing risk of APFs. For individual APFs, the greatest effect is noticed between clinical stage and nodal positivity and less so between clinical stage and positive margins. DRE continues to hold value, particularly for patients with locally advanced disease and potential lymph node disease.

Elective nodal radiotherapy in prostate cancer

In patients with prostate cancer who have a high risk of pelvic nodal disease, the use of elective whole pelvis radiotherapy is still controversial. Two large, randomised, controlled trials (RTOG 9413 and GETUG-01) did not show a benefit of elective whole pelvis radiotherapy over prostate-only radiotherapy. In 2020, the POP-RT trial established the role of elective whole pelvis radiotherapy in patients who have more than a 35% risk of lymph node invasion (known as the Roach formula). POP-RT stressed the importance of patient selection. In patients with cN1 (clinically node positive) disease or pN1 (pathologically node positive) disease, the addition of whole pelvis radiotherapy to androgen deprivation therapy significantly improved survival compared with androgen deprivation therapy alone, as shown in large, retrospective studies. This patient population might increase in the future because use of the more sensitive prostate-specific membrane antigen PET-CT will become the standard staging procedure. Additionally, the SPORTT trial suggested a benefit of whole pelvis radiotherapy in biochemical recurrence-free survival in the salvage setting. A correct definition of the upper field border, which should include the bifurcation of the abdominal aorta, is key in the use of pelvic radiotherapy. As a result of using modern radiotherapy technology, severe late urinary and intestinal toxic effects are rare and do not seem to increase compared with prostate-only radiotherapy.

Clinical parameters and nomograms for predicting lymph node metastasis detected with 68 Ga-PSMA-PET/CT in prostate cancer patients candidate to definitive radiotherapy

BACKGROUND

Defining the extent of disease spread with imaging modalities is crucial for therapeutic decision-making and definition of treatment. This study aimed to investigate whether clinical parameters and nomograms predict prostate-specific membrane antigen (PSMA)-positive lymph nodes in treatment-naïve nonmetastatic prostate cancer (PC) patients.

MATERIALS AND METHODS

The clinical data of 443 PC patients (83.3% high-risk and 16.7% intermediate-risk) were retrospectively analyzed. Receiver operating characteristic (ROC) curves with areas under the curve (AUC) were generated to evaluate the accuracy of clinical parameters (prostate-specific antigen [PSA], T stage, Gleason score [GS], International Society of Urological Pathology [ISUP] grade) and nomograms (Roach formula [RF], Yale formula [YF], and a new formula [NF]) in predicting lymph node metastasis. The AUCs of the various parameters and clinical nomograms were compared using ROC and precision-recall (PR) curves.

RESULTS

A total of 288 lymph node metastases were identified in 121 patients (27.3%) using ⁶⁸ Ga-PSMA-11-positron emission tomography (PET)/computed tomography (CT). Most PSMA-avid lymph node metastases occurred in external or internal iliac lymph nodes (142; 49.3%). Clinical T stage, PSA, GS, and ISUP grade were significantly associated with PSMA-positive lymph nodes according to univariate logistic regression analysis. The PSMA-positive lymph nodes were more frequently detected in patients with PSA >20 ng/ml, GS ≥7 or high risk disease compared to their counterparts. The clinical T stage, serum PSA level, GS, and ISUP grade showed similar accuracy in predicting PSMA-positive metastasis, with AUC values ranging from 0.675 to 0.704. The median risks for PSMA-positive lymph nodes according to the RF, YF, and NF were 31.3% (range: 12.3%-100%), 22.3% (range: 4.7%-100%), and 40.5% (range: 12.3%-100%), respectively. The AUC values generated from ROC and PR curve analyses were similar for all clinical nomograms, although the RF and YF had higher accuracy compared to NF.

CONCLUSION

The clinical T stage, PSA, GS, and ISUP grade are independent predictors of PSMA-positive lymph nodes. The RF and YF can be used to identify patients who can benefit from ⁶⁸ Ga-PSMA-11 PET/CT for the detection of lymph node metastasis. Together with nomograms, ⁶⁸ Ga-PSMA-11 PET/CT images help to localize PSMA-positive lymph node metastases and can thus assist in surgery and radiotherapy planning.

External validation of the Briganti 2019 nomogram to identify candidates for extended pelvic lymph node dissection among patients with high-risk clinically localized prostate cancer

Background

We aimed to establish an external validation of the Briganti 2019 nomogram in a Japanese cohort to preoperatively evaluate the probability of lymph node invasion in patients with high-risk, clinically localized prostate cancer.

Methods

The cohort consisted of 278 patients with prostate cancer diagnosed using magnetic resonance imaging-targeted biopsy who underwent radical prostatectomy and extended pelvic lymph node dissection from 2012 to 2020. Patients were rated using the Briganti 2019 nomogram, which evaluates the probability of lymph node invasion. We used the area under curve of the receiver operating characteristic analysis to quantify the accuracy of the nomogram.

Results

Nineteen (6.8%) patients had lymph node invasion. The median number of lymph nodes removed was 18. The area under the curve for the Briganti 2019 was 0.71. When the cutoff was set at 7%, 84 (30.2%) patients with extended pelvic lymph node dissection could be omitted, and only 1 (1.2%) patient with lymph node invasion would be missed. Sensitivity, specificity, and negative predictive values at the 7% cutoff were 94.7, 32.0, and 98.8%, respectively.

Conclusion

This external validation showed that the Briganti 2019 nomogram was accurate, although there may still be scope for individual adjustments.

Integration of clinicopathologic identification and deep transferrable image feature representation improves predictions of lymph node metastasis in prostate cancer

Background

Accurate identification of pelvic lymph node metastasis (PLNM) in patients with prostate cancer (PCa) is crucial for determining appropriate treatment options. Here, we built a PLNM-Risk calculator to obtain a precisely informed decision about whether to perform extended pelvic lymph node dissection (ePLND).

Methods

The PLNM-Risk calculator was developed in 280 patients and verified internally in 71 patients and externally in 50 patients by integrating a set of radiologists’ interpretations, clinicopathological factors and newly refined imaging indicators from MR images with radiomics machine learning and deep transfer learning algorithms. Its clinical applicability was compared with Briganti and Memorial Sloan Kettering Cancer Center (MSKCC) nomograms.

Findings

The PLNM-Risk achieved good diagnostic discrimination with areas under the receiver operating characteristic curve (AUCs) of 0.93 (95% CI, 0.90-0.96), 0.92 (95% CI, 0.84-0.97) and 0.76 (95% CI, 0.62-0.87) in the training/validation, internal test and external test cohorts, respectively. If the number of ePLNDs missed was controlled at < 2%, PLNM-Risk provided both a higher number of ePLNDs spared (PLNM-Risk 59.6% vs MSKCC 44.9% vs Briganti 38.9%) and a lower number of false positives (PLNM-Risk 59.3% vs MSKCC 70.1% and Briganti 72.7%). In follow-up, patients stratified by the PLNM-Risk calculator showed significantly different biochemical recurrence rates after surgery.

Interpretation

The PLNM-Risk calculator offers a noninvasive clinical biomarker to predict PLNM for patients with PCa. It shows improved accuracy of diagnosis support and reduced overtreatment burdens for patients with findings suggestive of PCa.

Funding

This work was supported by the Key Research and Development Program of Jiangsu Province (BE2017756) and the Suzhou Science and Technology Bureau-Science and Technology Demonstration Project (SS201808).

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 impact of positive surgical margins on salvage radiation or androgen deprivation therapy following radical prostatectomy - a nationwide study

BACKGROUND

The extent to which positive surgical margins (PSM) affect the risk of subsequent salvage radiation therapy (sRT) or androgen depletion therapy (ADT) following radical prostatectomy (RP) is not well described. Initiation of additional therapies after RP depend on patient preference, individual factors, local guidelines, and life expectancy. The aim of this study was to analyze differences between margin status in risk of subsequent treatment for PCa following RP in a retrospective population-based cohort from Denmark.

METHODS

Patients who underwent RP were identified in The Danish Prostate Cancer Registry (DaPCaR). Subsequent sRT and ADT were assessed in uni- and multivariate settings and validated with receiver operating characteristic (ROC).

RESULTS

PSM was associated with an increased risk of sRT (HR = 1.85, p < .001) and receiving ADT (HR:1.39, p = .007). Margin status only had a minor impact on the predictive ability for sRT (area under the curve (AUC): p < .001) and no significant impact for subsequent ADT (AUC: p = 1). Significant inter-institutional difference in the association between PSM with sRT or ADT was observed.

CONCLUSION

PSM is associated with the risk of sRT and initiation of ADT, however this association is weak. Our results underline that factors beyond tumor characteristics play a major role for initiation of sRT and ADT.

Prostate cancer

Prostate cancer is a complex disease that affects millions of men globally, predominantly in high human development index regions. Patients with localized disease at a low to intermediate risk of recurrence generally have a favourable outcome of 99% overall survival for 10 years if the disease is detected and treated at an early stage. Key genetic alterations include fusions of TMPRSS2 with ETS family genes, amplification of the MYC oncogene, deletion and/or mutation of PTEN and TP53 and, in advanced disease, amplification and/or mutation of the androgen receptor (AR). Prostate cancer is usually diagnosed by prostate biopsy prompted by a blood test to measure prostate-specific antigen levels and/or digital rectal examination. Treatment for localized disease includes active surveillance, radical prostatectomy or ablative radiotherapy as curative approaches. Men whose disease relapses after prostatectomy are treated with salvage radiotherapy and/or androgen deprivation therapy (ADT) for local relapse, or with ADT combined with chemotherapy or novel androgen signalling-targeted agents for systemic relapse. Advanced prostate cancer often progresses despite androgen ablation and is then considered castration-resistant and incurable. Current treatment options include AR-targeted agents, chemotherapy, radionuclides and the poly(ADP-ribose) inhibitor olaparib. Current research aims to improve prostate cancer detection, management and outcomes, including understanding the fundamental biology at all stages of the disease.

Prostate cancer

Prostate cancer is a complex disease that affects millions of men globally, predominantly in high human development index regions. Patients with localized disease at a low to intermediate risk of recurrence generally have a favourable outcome of 99% overall survival for 10 years if the disease is detected and treated at an early stage. Key genetic alterations include fusions of TMPRSS2 with ETS family genes, amplification of the MYC oncogene, deletion and/or mutation of PTEN and TP53 and, in advanced disease, amplification and/or mutation of the androgen receptor (AR). Prostate cancer is usually diagnosed by prostate biopsy prompted by a blood test to measure prostate-specific antigen levels and/or digital rectal examination. Treatment for localized disease includes active surveillance, radical prostatectomy or ablative radiotherapy as curative approaches. Men whose disease relapses after prostatectomy are treated with salvage radiotherapy and/or androgen deprivation therapy (ADT) for local relapse, or with ADT combined with chemotherapy or novel androgen signalling-targeted agents for systemic relapse. Advanced prostate cancer often progresses despite androgen ablation and is then considered castration-resistant and incurable. Current treatment options include AR-targeted agents, chemotherapy, radionuclides and the poly(ADP-ribose) inhibitor olaparib. Current research aims to improve prostate cancer detection, management and outcomes, including understanding the fundamental biology at all stages of the disease.

Extracapsular extension on multiparametric magnetic resonance imaging better predicts pT3 disease at radical prostatectomy compared to perineural invasion on biopsy

INTRODUCTION

Risk assessment for non-organ-confined prostate cancer (PCa) is important in the surgical planning for radical prostatectomy (RP). Perineural invasion (PNI) on prostate biopsy has been associated with adverse pathological outcomes at prostatectomy. Similarly, the identification of suspected extracapsular extension (ECE) on multiparametric magnetic resonance imaging (mpMRI) has been shown to predict non-organ-confined disease. However, no prior study has compared these factors in predicting adverse pathology at prostatectomy. We evaluated mpMRI ECE and prostate biopsy PNI on multivariable analysis to determine their ability to predict pathological stage at time of RP.

METHODS

We retrospectively investigated the prostatectomy database at our institution to identify men who underwent prostate biopsy with pre-biopsy mpMRI and subsequent RP from 2013-2017. Multivariable regression analysis was performed to compare the association of mpMRI ECE (mECE) and PNI on prostate biopsy on the likelihood of finding pT3 disease on pathology post-prostatectomy.

RESULTS

Of a total 454 RP between 2013 and 2017, 191 patients met our inclusion criteria. Stage pT2 and pT3+ were found in 120 (62.8%) and 71 (37.2%) patients, respectively. Patients with mECE had 4.84 cumulative odds of worse pathological stage on RP (p=0.045) compared to PNI on biopsy, which showed cumulative odds of 2.25 (p=0.048). When controlling only for those patients without PNI, mECE was still found to be a significant predictor of pT3 disease at RP (p=0.030); however, in patients without mECE, PNI was not significant (p=0.062).

CONCLUSIONS

While mECE and biopsy PNI were both associated with worse pathological stage on RP, mECE had significantly higher cumulative odds compared to PNI. The significant predictive ability of mECE adds further clinical value to the use of mpMRI in PCa management. While validation in a larger cohort is required, these factors have important clinical implications with regards to early diagnosis of advanced disease and surgical planning.

PSA-IgM and iXip in the diagnosis and management of prostate cancer: clinical relevance and future potential. A review

The Prostate Specific Antigen (PSA) is the first filter in the diagnosis of prostate cancer. Unfortunately, it is organ-specific but not cancer-specific. In addition, some prostate cancers are not clinically-significant and their diagnosis and treatment may lead to overdiagnosis and overtreatment. For these reasons, other markers have been proposed in the last years, such as PCA3 and PHI, but none of these are currently used in the clinical practice on large scale. In the last decade, PSA-IgM and the algorithm iXip have emerged for the diagnosis of prostate cancer and showed to perform well in decreasing the detection of clinically-insignificant prostate cancer and in reducing the number of unnecessary prostate biopsies. This review focuses on data reported in the literature on PSA-IgM and iXip as well as on the future perspectives of their usage in the clinical practice on large scale.

Value of clinical parameters and MRI with PI-RADSV2 in predicting seminal vesicle invasion of prostate cancer

OBJECTIVE

To investigate the usefulness of magnetic resonance imaging (MRI) with Prostate Imaging Reporting and Data System version 2 (PI-RADS_V2) and clinical parameters in predicting seminal vesicle invasion (SVI).

MATERIAL AND METHODS

In this retrospective study, we identified 569 prostate cancer patients who underwent radical prostatectomy with MRI before surgery. SVI was interpreted with PI-RADS_V2. Clinical parameters such as the prostate-specific antigen (PSA) and Gleason score (GS) were analyzed for the prediction of SVI. Logistic regression models and receiver operating characteristic (ROC) curves were used to evaluate SVI based on clinical parameters and MRI with PI-RADS_V2.

RESULTS

The median age at presentation was 67 years (43-85 years). The median PSA level was 6.1 ng/mL (2.2-72.8 ng/mL). There were 113 patients with a biopsy GS of ≥ 8. A total of 34 patients (6.0%) were interpreted to have SVI by MRI of which 20 were true positive, and 52 patients (9.1%) had true SVI in the final pathologic analysis. In multivariable analysis, PSA (HR: 1.03, 95% CI: 1.00-1.07), biopsy GS ≥ 8 (HR: 4.14, 95% CI: 2.12-8.09), and MRI with PI-RADS_V2 (HR: 14.67, 95% CI: 6.34-33.93) were significantly associated with pathologic SVI. The area under the curve of the model based on the clinical parameters PSA and GS plus MRI (0.862) was significantly larger than that of the model based on clinical parameters alone (0.777, p < 0.001).

CONCLUSIONS

MRI with PI-RADS_V2 using the clinical parameters PSA and GS was effective in predicting SVI.

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

PURPOSE

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.

PATIENTS AND METHODS

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]).

RESULTS

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).

CONCLUSION

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.

Role of secreted extracellular nicotinamide phosphoribosyltransferase (eNAMPT) in prostate cancer progression: Novel biomarker and therapeutic target

BACKGROUND

There remains a serious need to prevent the progression of invasive prostate cancer (PCa). We previously showed that secreted extracellular nicotinamide phosphoribosyltransferase (eNAMPT) is a multifunctional innate immunity regulator via TLR4 ligation which has been implicated in PCa progression. Here we investigate the role of eNAMPT as a diagnostic biomarker and therapeutic target in the progression of PCa.

METHODS

Tumor NAMPT expression and plasma eNAMPT level were evaluated in human subjects with various PCa tumor stages and high risk subjects followed-up clinically for PCa. The genetic regulation of NAMPT expression in PCa cells and the role of eNAMPT in PCa invasion were investigated utilizing in vitro and in vivo models.

FINDINGS

Marked NAMPT expression was detected in human extraprostatic-invasive PCa tissues compared to minimal expression of organ-confined PCa. Plasma eNAMPT levels were significantly elevated in PCa subjects compared to male controls, and significantly greater in subjects with extraprostatic-invasive PCa compared to subjects with organ-confined PCa. Plasma eNAMPT levels showed significant predictive value for diagnosing PCa. NAMPT expression and eNAMPT secretion were highly upregulated in human PCa cells in response to hypoxia-inducible factors and EGF. In vitro cell culture and in vivo preclinical mouse model studies confirmed eNAMPT-mediated enhancement of PCa invasiveness into muscle tissues and dramatic attenuation of PCa invasion by weekly treatment with an eNAMPT-neutralizing polyclonal antibody.

INTERPRETATION

This study suggests that eNAMPT is a potential biomarker for PCa, especially invasive PCa. Neutralization of eNAMPT may be an effective therapeutic approach to prevent PCa invasion and progression.

Optimizing the risk threshold of lymph node involvement for performing extended pelvic lymph node dissection in prostate cancer patients: a cost-effectiveness analysis

BACKGROUND

Extended pelvic lymph node dissection (ePLND) may be omitted in prostate cancer (CaP) patients with a low predicted risk of lymph node involvement (LNI). The aim of the current study was to quantify the cost-effectiveness of using different risk thresholds for predicted LNI in CaP patients to inform decision making on omitting ePLND.

METHODS

Five different thresholds (2%, 5%, 10%, 20%, and 100%) used in practice for performing ePLND were compared using a decision analytic cohort model with the 100% threshold (i.e., no ePLND) as reference. Compared outcomes consisted of quality-adjusted life years (QALYs) and costs. Baseline characteristics for the hypothetical cohort were based on an actual Dutch patient cohort containing 925 patients who underwent ePLND with risks of LNI predicted by the Memorial Sloan Kettering Cancer Center web-calculator. The best strategy was selected based on the incremental cost effectiveness ratio when applying a willingness to pay (WTP) threshold of €20,000 per QALY gained. Probabilistic sensitivity analysis was performed with Monte Carlo simulation to assess the robustness of the results.

RESULTS

Costs and health outcomes were lowest (€4,858 and 6.04 QALYs) for the 100% threshold, and highest (€10,939 and 6.21 QALYs) for the 2% threshold, respectively. The incremental cost effectiveness ratio for the 2%, 5%, 10%, and 20% threshold compared with the first threshold above (i.e., 5%, 10%, 20%, and 100%) were €189,222/QALY, €130,689/QALY, €51,920/QALY, and €23,187/QALY respectively. Applying a WTP threshold of €20.000 the probabilities for the 2%, 5%, 10%, 20%, and 100% threshold strategies being cost-effective were 0.0%, 0.3%, 4.9%, 30.3%, and 64.5% respectively.

CONCLUSION

Applying a WTP threshold of €20.000, completely omitting ePLND in CaP patients is cost-effective compared to other risk-based strategies. However, applying a 20% threshold for probable LNI to the Briganti 2012 nomogram or the Memorial Sloan Kettering Cancer Center web-calculator, may be a feasible alternative, in particular when higher WTP values are considered.

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.

Indications for and complications of pelvic lymph node dissection in prostate cancer: accuracy of available nomograms for the prediction of lymph node invasion

OBJECTIVES

To externally validate the currently available nomograms for predicting lymph node invasion (LNI) in patients with prostate cancer (PCa) and to assess the potential risk of complications of extended pelvic lymph node dissection (ePLND) when using the recommended threshold.

METHODS

A total of 14 921 patients, who underwent radical prostatectomy with ePLND at eight European tertiary referral centres, were retrospectively identified. After exclusion of patients with incomplete biopsy or pathological data, 12 009 were included. Of these, 609 had undergone multiparametic magnetic resonance imaging-targeted biopsies. Among ePLND-related complications we included lymphocele, lymphoedema, haemorrhage, infection and sepsis. The performances of the Memorial Sloan Kettering Cancer Centre (MSKCC), Briganti 2012, Briganti 2017, Briganti 2019, Partin 2016 and Yale models were evaluated using receiver-operating characteristic curve analysis (area under the curve [AUC]), calibration plots, and decision-curve analysis.

RESULTS

Overall, 1158 patients (9.6%) had LNI, with a mean of 17.7 and 3.2 resected and positive nodes, respectively. No significant differences in AUCs were observed between the MSKCC (0.79), Briganti 2012 (0.79), Partin 2016 (0.78), Yale (0.80), Briganti 2017 (0.81) and Briganti 2019 (0.76) models. A direct comparison of older models showed that better discrimination was achieved with the MSKCC and Briganti 2012 nomograms. A tendency for underestimation was seen for all the older models, whereas the Briganti 2017 and 2019 nomograms tended to overestimate LNI risk. Decision-curve analysis showed a net benefit for all models, with a lower net benefit for the Partin 2016 and Briganti 2019 models. ePLND-related complications were experienced by 1027 patients (8.9%), and 12.6% of patients with pN1 disease.

CONCLUSIONS

The currently available nomograms have similar performances and limitations in the prediction of LNI. Miscalibration was present, however, for all nomograms showing a net benefit. In patients with only systematic biopsy, the MSKCC and Briganti 2012 nomograms were superior in the prediction of LNI.

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

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.

Added Value of Biparametric MRI and TRUS-Guided Systematic Biopsies to Clinical Parameters in Predicting Adverse Pathology in Prostate Cancer

Objective

To develop novel models for predicting extracapsular extension (EPE), seminal vesicle invasion (SVI), or upgrading in prostate cancer (PCa) patients using clinical parameters, biparametric magnetic resonance imaging (bp-MRI), and transrectal ultrasonography (TRUS)-guided systematic biopsies.

Patients and Methods

We retrospectively collected data from PCa patients who underwent standard (12-core) systematic biopsy and radical prostatectomy. To develop predictive models, the following variables were included in multivariable logistic regression analyses: total prostate-specific antigen (TPSA), central transition zone volume (CTZV), prostate-specific antigen (PSAD), maximum diameter of the index lesion at bp-MRI, EPE at bp-MRI, SVI at bp-MRI, biopsy Gleason grade group, and number of positive biopsy cores. Three risk calculators were built based on the coefficients of the logit function. The area under the curve (AUC) was applied to determine the models with the highest discrimination. Decision curve analyses (DCAs) were performed to evaluate the net benefit of each risk calculator.

Results

A total of 222 patients were included in this study. Overall, 83 (37.4%), 75 (33.8%), and 107 (48.2%) patients had EPE, SVI, and upgrading at final pathology, respectively. The addition of bp-MRI data improved the discrimination of models for predicting SVI (0.807 vs 0.816) and upgrading (0.548 vs 0.625) but not EPE (0.766 vs 0.763). Similarly, models including clinical parameters, bp-MRI data, and information on systematic biopsies achieved the highest AUC in the prediction of EPE (0.842), SVI (0.913), and upgrading (0.794), and the three corresponding risk calculators yielded the highest net benefit.

Conclusion

We developed three easy-to-use risk calculators for the prediction of adverse pathological features based on patient clinical parameters, bp-MRI data, and information on systematic biopsies. This may be greatly beneficial to urologists in the decision-making process for PCa patients.