Predicting Lymph Node Invasion Research Articles

A novel model Integrating clinical, mp-MRI, and epigenomic features to predict lymph node invasion in prostate cancer patients undergoing radical prostatectomy and pelvic lymph node dissection

A novel model Integrating clinical, mp-MRI, and epigenomic features to predict lymph node invasion in prostate cancer patients undergoing radical prostatectomy and pelvic lymph node dissection

Management of Bladder Cancer Patients with Clinical Evidence of Lymph Node Invasion (cN+).

The purpose of this review is to present the current knowledge about the diagnostic and treatment options for bladder cancer (BCa) patients with clinically positive lymph nodes (cN+). This review shows compaction of CT and MRI performance in preoperative prediction of lymph node invasion (LNI) in BCa patients, along with other diagnostic methods. Most scientific societies do not distinguish cN+ patients in their guidelines; recommendations concern muscle-invasive bladder cancer (MIBC) and differ between associations. The curative treatment that provides the best long-term survival in cN+ patients is a multimodal approach, with a combination of neoadjuvant chemotherapy (NAC) and radical cystectomy (RC) with extended pelvic lymph node dissection (ePLND). The role of adjuvant chemotherapy (AC) remains uncertain; however, emerging evidence indicates comparable outcomes to NAC. Therefore, in cN+ patients who have not received NAC, AC should be implemented. The response to ChT is a crucial prognostic factor for cN+ patients. Recent studies demonstrated the growing importance of immunotherapy, especially in ChT-ineligible patients. Moreover, immunotherapy can be suitable as adjuvant therapy in selected cases. In cN+ patients, the extended template of PLND should be utilized, with the total resected node count being less important than the template. This review is intended to draw special attention to cN+ BCa patients, as the oncological outcomes are significantly worse for this group.

MP57-06 EXTERNAL VALIDATION AND COMPARISON OF TWO NOMOGRAMS PREDICTING THE PROBABILITY OF LYMPH NODE INVASION IN PATIENTS SUBJECTED TO ROBOTIC RADICAL PROSTATECTOMY AND CONCOMITANT LYMPH NODE DISSECTION

You have accessJournal of UrologyCME1 May 2022MP57-06 EXTERNAL VALIDATION AND COMPARISON OF TWO NOMOGRAMS PREDICTING THE PROBABILITY OF LYMPH NODE INVASION IN PATIENTS SUBJECTED TO ROBOTIC RADICAL PROSTATECTOMY AND CONCOMITANT LYMPH NODE DISSECTION Nicola Frego, Paciotti Marco, Davide Maffei, Pier Paolo Avolio, Roberto Contieri, Alessandro Uleri, Massimo Lazzeri, Alberto Saita, Paolo Casale, Nicolò Maria Buffi, and Giovanni Lughezzani Nicola FregoNicola Frego More articles by this author , Paciotti MarcoPaciotti Marco More articles by this author , Davide MaffeiDavide Maffei More articles by this author , Pier Paolo AvolioPier Paolo Avolio More articles by this author , Roberto ContieriRoberto Contieri More articles by this author , Alessandro UleriAlessandro Uleri More articles by this author , Massimo LazzeriMassimo Lazzeri More articles by this author , Alberto SaitaAlberto Saita More articles by this author , Paolo CasalePaolo Casale More articles by this author , Nicolò Maria BuffiNicolò Maria Buffi More articles by this author , and Giovanni LughezzaniGiovanni Lughezzani More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002640.06AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Current guidelines recommend an extended Pelvic Lymph Node Dissection (ePLND) in Prostate Cancer (PCa) patients with a non negligible risk of Lymph Node Invasion (LNI), according to available nomograms. The Briganti 2012 nomogram has been recently updated in Briganti 2019, to include information on Magnetic Resonance Imaging (MRI) and targeted biopsy (TB). The aim of the study is to validate and compare Briganti 2012 and 2019 nomograms in an external cohort of contemporary patients. METHODS: We retrospectively collected data of consecutive patients treated with Robot Assisted Radical Prostatectomy and ePLND at our center, between December 2014 and August 2021. Data included clinical, MRI, and pathological characteristics. The risk of LNI was calculated with the published regression coefficients. The Receiver Operator Characteristics (ROC) curve and Area Under the Curve (AUC) were used to evaluate the accuracy, while deLong method was used to compare models' accuracy. Calibration plot was used to evaluate the extend of over and under estimation of the models. Multivariate Logistic Regression Analysis (MVA) was performed to evaluate potential predictive factors of LNI. RESULTS: Overall, we analyzed data on 842 patients. Of them, 377 (44.8%) underwent MRI and TB. Median age at surgery was 64 years (IQR 59-69) and median tPSA was 7.04 ng/mL (IQR 5.11-10.4). Extra Capsular Extension and Seminal Vesicles Invasion (SVI) at MRI were observed in 15.4% and 5.4% of patients, respectively. The median primary and secondary GS were 3 and 4, respectively. The median percentage of positive cores was 35% (IQR 21.4-50). Finally, median Gleason Group at TB was 2 (IQR 2-3) and the median percentage of clinically significant random cores was 21% (IQR 0-42). A median of 13 (IQR 9-18) nodes per patient were removed, and 85 (10.1%) patients had LNI at final pathology. The AUCs for Briganti 2012 and 2019 were 0.84 and 0.79, respectively. The calibration plots showed a good correlation between the predicted probabilities and the observed proportion of LNI for both models. In patients with both SB and TB, Briganti 2012 accuracy was 0.815, and no significant difference was found between the two nomograms (p = 0.66). At MVA only PSA >20 ng/mL and the evidence of SVI at MRI were independent predictors of LNI. CONCLUSIONS: The direct comparison of the two nomograms showed that Briganti 2019 nomogram was no more accurate than Briganti 2012, and that the role of MRI and MRI TB may be negligible in the prediction of LNI. Source of Funding: None © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e983 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Nicola Frego More articles by this author Paciotti Marco More articles by this author Davide Maffei More articles by this author Pier Paolo Avolio More articles by this author Roberto Contieri More articles by this author Alessandro Uleri More articles by this author Massimo Lazzeri More articles by this author Alberto Saita More articles by this author Paolo Casale More articles by this author Nicolò Maria Buffi More articles by this author Giovanni Lughezzani More articles by this author Expand All Advertisement PDF DownloadLoading ...

PD59-07 NEGLECTED LYMPH NODAL METASTASES IN PATIENTS WITH RENAL CANCER: WHEN TO EXTEND THE ANATOMICAL TEMPLATE OF LYMPH NODE DISSECTION DURING NEPHRECTOMY

PD59-07 NEGLECTED LYMPH NODAL METASTASES IN PATIENTS WITH RENAL CANCER: WHEN TO EXTEND THE ANATOMICAL TEMPLATE OF LYMPH NODE DISSECTION DURING NEPHRECTOMY

Biomarkers Associated with Lymph Nodal Metastasis in Endometrioid Endometrial Carcinoma.

Simple SummaryIn endometrial cancer, lymph node invasion assessed through surgical lymphadenectomy or sentinel lymph node biopsy is a determinant factor for the prognosis and planification of adjuvant treatment. Those surgical procedures are associated with short- and long-term complications. Recent advances in molecular characterization of endometrial cancer have provided important insights into the biological nature of tumors but have not improved the pre-operative prediction of LND. This study is a description of the transcriptomic landscape associated with lymph node metastases in endometroid endometrial carcinomas. A 54-genes expression signature was generated at analysis of the primary tumor. Differential gene expression was found between patients with and without lymph node metastasis, with an 87% accuracy. Our findings provide a basis for the development of a gene expression-based signature that can be used to pre-operatively select patients for whom surgical assessment of lymph node status is of little value, and, consequently, an unfavorable risk–benefit balance.Introduction. Lymph node metastasis is determinant in the prognosis and treatment of endometrioid endometrial cancer (EEC) but the risk–benefit balance of surgical lymph node staging remains controversial. Objective. Describe the pathways associated with lymph node metastases in EEC detected by whole RNA sequencing. Methods. RNA-sequencing was performed on a retrospective series of 30 non-metastatic EEC. N+ and N− patients were matched for tumoral size, tumoral grade and myometrial invasion. Results. Twenty-eight EECs were analyzable (16 N+ and 12 N−). Bioinformatics Unsupervised analysis revealed three patterns of expression, enriched in N+, mix of N+/N− and enriched in N−, respectively. The cluster with only N+ patient overexpressed extra cellular matrix, epithelial to mesenchymal and smooth muscle contraction pathways with respect to the N− profile. Differential expression analysis between N+ and N− was used to generate a 54-genes signature with an 87% accuracy. Conclusion. RNA-expression analysis provides a basis to develop a gene expression-based signature that could pre-operatively predict lymph node invasion.

Multiparametric MRI-based radiomics model to predict pelvic lymph node invasion for patients with prostate cancer.

To identify which patient with prostate cancer (PCa) could safely avoid extended pelvic lymph node dissection (ePLND) by predicting lymph node invasion (LNI), via a radiomics-based machine learning approach. An integrative radiomics model (IRM) was proposed to predict LNI, confirmed by the histopathologic examination, integrating radiomics features, extracted from prostatic index lesion regions on MRI images, and clinical features via SVM. The study cohort comprised 244 PCa patients with MRI and followed by radical prostatectomy (RP) and ePLND within 6months between 2010 and 2019. The proposed IRM was trained in training/validation set and evaluated in an internal independent testing set. The model's performance was measured by area under the curve (AUC), sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). AUCs were compared via Delong test with 95% confidence interval (CI), and the rest measurements were compared via chi-squared test or Fisher's exact test. Overall, 17 (10.6%) and 14 (16.7%) patients with LNI were included in training/validation set and testing set, respectively. Shape and first-order radiomics features showed usefulness in building the IRM. The proposed IRM achieved an AUC of 0.915 (95% CI: 0.846-0.984) in the testing set, superior to pre-existing nomograms whose AUCs were from 0.698 to 0.724 (p < 0.05). The proposed IRM could be potentially feasible to predict the risk of having LNI for patients with PCa. With the improved predictability, it could be utilized to assess which patients with PCa could safely avoid ePLND, thus reduce the number of unnecessary ePLND. • The combination of MRI-based radiomics features with clinical information improved the prediction of lymph node invasion, compared with the model using only radiomics features or clinical features. • With improved prediction performance on predicting lymph node invasion, the number of extended pelvic lymph node dissection (ePLND) could be reduced by the proposed integrative radiomics model (IRM), compared with the existing nomograms.

External Validation and Comparison of Two Nomograms Predicting the Probability of Lymph Node Involvement in Patients subjected to Robot-Assisted Radical Prostatectomy and Concomitant Lymph Node Dissection: A Single Tertiary Center Experience in the MRI-Era.

IntroductionTo externally validate and directly compare the performance of the Briganti 2012 and Briganti 2019 nomograms as predictors of lymph node invasion (LNI) in a cohort of patients treated with robot-assisted radical prostatectomy (RARP) and extended pelvic lymph node dissection (ePLND).Materials and MethodsAfter the exclusion of patients with incomplete biopsy, imaging, or clinical data, 752 patients who underwent RARP and ePLND between December 2014 to August 2021 at our center, were included. Among these patients, 327 (43.5%) had undergone multi-parametric MRI (mpMRI) and mpMRI-targeted biopsy. The preoperative risk of LNI was calculated for all patients using the Briganti 2012 nomogram, while the Briganti 2019 nomogram was used only in patients who had performed mpMRI with the combination of targeted and systematic biopsy. The performances of Briganti 2012 and 2019 models were evaluated using the area under the receiver-operating characteristics curve analysis, calibrations plot, and decision curve analysis.ResultsA median of 13 (IQR 9–18) nodes per patient was removed, and 78 (10.4%) patients had LNI at final pathology. The area under the curves (AUCs) for Briganti 2012 and 2019 were 0.84 and 0.82, respectively. The calibration plots showed a good correlation between the predicted probabilities and the observed proportion of LNI for both models, with a slight tendency to underestimation. The decision curve analysis (DCA) of the two models was similar, with a slightly higher net benefit for Briganti 2012 nomogram. In patients receiving both systematic- and targeted-biopsy, the Briganti 2012 accuracy was 0.85, and no significant difference was found between the AUCs of 2012 and 2019 nomograms (p = 0.296). In the sub-cohort of 518 (68.9%) intermediate-risk PCa patients, the Briganti 2012 nomogram outperforms the 2019 model in terms of accuracy (0.82 vs. 0.77), calibration curve, and net benefit at DCA.ConclusionThe direct comparison of the two nomograms showed that the most updated nomogram, which included MRI and MRI-targeted biopsy data, was not significantly more accurate than the 2012 model in the prediction of LNI, suggesting a negligible role of mpMRI in the current population.

A0658 - Impact of the time elapsed between prostate biopsy and surgery on the accuracy of the Briganti nomogram predicting lymph node invasion in men with clinically localized prostate cancer

A0658 - Impact of the time elapsed between prostate biopsy and surgery on the accuracy of the Briganti nomogram predicting lymph node invasion in men with clinically localized prostate cancer

Preoperative Predictors of Lymph Node Invasion and Biochemical Recurrence in High-risk Prostate Cancer.

To evaluate the preoperative predictors of pathological lymph node (LN) metastasis and prognostic factors for postoperative biochemical recurrence (BCR) in robot-assisted radical prostatectomy with extended pelvic LN dissection in patients with D'Amico high-risk prostate cancer (PCa). Overall, 107 patients with D'Amico high-risk PCa underwent robot-assisted radical prostatectomy with extended pelvic LN dissection without neoadjuvant or adjuvant therapy. BCR was defined as a prostate-specific antigen (PSA) level ≥0.2 ng/ml. Moreover, BCR-free survival rates were determined using Kaplan-Meier analysis. Logistic regression analysis was used to evaluate preoperative predictors of pathological LN metastasis. Cox regression analysis was used to evaluate the effects of preoperative and pathologic variables on BCR. The median follow-up was 21 months, and the 5-year BCR-free survival rate was 59.8%. The positive LN rate was 21.5%. In multivariate analysis, the percentage of positive cores was a significant preoperative predictor of positive LNs. Patients with >50% positive cores (p=0.004) and PSA density (PSAD) >0.5 ng/ml/cc (p=0.005) had a high risk of having ≥3 positive LNs. In multivariate analysis, PSAD >0.5% was a significant preoperative predictor of BCR. Among the postoperative predictors, the number of positive LNs was significantly associated with BCR. Patients with ≥3 positive LNs (n=7) had significantly lower BCR-free survival rates than patients with one or two positive LNs (n=16) (p<0.001). Patients with >50% positive cores and PSAD >0.5 ng/ml/cc had a risk for a high number of positive LNs (≥3) that was strongly associated with shorter BCR-free survival (p<0.001). The percentage of positive cores may be useful as a preoperative predictor of pathological LN metastasis in patients with high-risk PCa. Patients with >50% positive cores and PSAD >0.5 ng/ml/cc were found to have a high risk for ≥3 positive LNs and shorter BCR-free survival.

SC284 - Development and external validation of a novel tool predicting lymph node invasion in patients with prostate cancer detected at targeted-biopsies

SC284 - Development and external validation of a novel tool predicting lymph node invasion in patients with prostate cancer detected at targeted-biopsies

MP32-12 PREDICTING PROSTATE CANCER LYMPH NODE INVASION AND BIOCHEMICAL RECURRENCE: A HEAD TO HEAD COMPARISON OF RISK STRATIFICATION TOOLS IN TWO ETHNICALLY DIFFERENT FRENCH POPULATIONS

INTRODUCTION AND OBJECTIVE:Several risk classification tools are available to predict lymph node invasion (LNI) prior to radical prostatectomy while others predict biochemical recurrence (BCR) in p...

P0840 - Preoperative prediction of lymph node invasion in prostate cancer: Ga-68 PSMA PET or nomograms?

P0840 - Preoperative prediction of lymph node invasion in prostate cancer: Ga-68 PSMA PET or nomograms?

P1212 - Development and external validation of a novel tool predicting lymph node invasion in patients with prostate cancer detected at targeted-biopsies

P1212 - Development and external validation of a novel tool predicting lymph node invasion in patients with prostate cancer detected at targeted-biopsies

A Genomic-Clinicopathologic Nomogram for the Prediction of Lymph Node Invasion in Prostate Cancer.

Background Lymph node status is important for treatment decision making in prostate cancer (PCa). We aimed to develop a genomic-clinicopathologic nomogram for the prediction of lymph node invasion (LNI) in PCa. Methods Differentially expressed genes between LNI and non-LNI PCa samples were identified in the Cancer Genome Atlas database. Univariate Cox regression analysis and minimum redundancy maximum relevance were performed for gene selection. The synthetic minority oversampling technique (SMOTE) was conducted to balance the minority group (LNI group). Machine learning models were constructed in the training set and assessed in the validation set. Univariable logistic regression and multivariable logistic regression were applied to build a nomogram. Furthermore, the RNA-sequence data from our center were used to validate the expression levels of hub genes between five matched primary PCa and the corresponding LNI samples. Results The 37-gene-based support vector machine (SVM) model had the optimal synthesized performance in the SMOTE-balanced training (area under the curve (AUC): 0.947) and validation (AUC: 0.901) sets. Incorporating the SVM-based risk score and the Gleason grade, the genomic-clinicopathologic nomogram demonstrated good prediction and calibration both in the SMOTE-balanced training (AUC: 0.946) and validation (AUC: 0.910) sets. The dysregulated expression of hub genes between PCa and LNI samples was also validated. Conclusion The proposed nomogram combining the 37-gene-based SVM model with the Gleason grade had the potential to preoperatively predict LNI in PCa. Some of the hub genes should be prioritized for functional studies and mechanistic analyses.

External Validation of the Briganti Nomogram to Predict Lymph Node Invasion in Prostate Cancer-Setting a New Threshold Value.

(1) Introduction: The study aimed to test and validate the performance of the 2012 Briganti nomogram as a predictor for pelvic lymph node invasion (LNI) in men who underwent radical prostatectomy (RP) with extended pelvic lymph node dissection (PLND) to examine their performance and to analyse the therapeutic impact of using a different nomogram cut-off. (2) Material and Methods: The study group consisted of 222 men with clinically localized prostate cancer (PCa) who underwent RP with ePLND between 01/2012 and 10/2018. Measurements included: preoperative PSA, clinical stage (CS), primary and secondary biopsy Gleason pattern, and the percentage of positive cores. The area under the curve (AUC) of the receiver operator characteristic analysis was appointed to quantify the accuracy of the primary nomogram model to predict LNI. The extent of estimation associated with the use of this model was graphically depicted using calibration plots. (3) Results: The median number of removed lymph nodes was 16 (IQR 12–21). A total of 53 of 222 patients (23.9%) had LNI. Preoperative clinical and biopsy characteristics differed significantly (all p < 0.005) between men with and without LNI. A nomogram-derived cut-off of 7% could lead to a reduction of 43% (95/222) of lymph node dissection while omitting 19% (10/53) of patients with LNI. The sensitivity, specificity, and negative predictive value associated with the 7% cut-off were 81.1%, 50.3%, and 96.3%, respectively. (4) Conclusions: The analysed nomogram demonstrated high accuracy for LNI prediction. A nomogram-derived cut-off of 7% confirmed good performance characteristics within the first external validation cohort from Poland.

Head-to-Head Comparison of Two Nomograms Predicting Probability of Lymph Node Invasion in Prostate Cancer and the Therapeutic Impact of Higher Nomogram Threshold.

Introduction: The aim of the study was to compare the performance of the 2012 Briganti and Memorial Sloan Kettering Cancer Center (MSKCC) nomograms as a predictor for pelvic lymph node invasion (LNI) in men who underwent radical prostatectomy (RP) with pelvic lymph node dissection (PLND), to examine their performance and to analyse the therapeutic impact of using 7% nomogram cut-off. Materials and Methods: The study cohort consisted of 807 men with clinically localised prostate cancer (PCa) who underwent open RP with PLND between 2001 and 2019. The area under the curve (AUC) of the receiver operator characteristic analysis was used to quantify the accuracy of the 2012 Briganti and MSKCC nomograms to predict LNI. Calibration plots were used to visualise over or underestimation by the models and a decision curve analysis (DCA) was performed to evaluate the net benefit associated with the used nomograms. Results: A total of 97 of 807 patients had LNI (12%). The AUC of 2012 Briganti and MSKCC nomogram was 80.6 and 79.2, respectively. For the Briganti nomogram using the cut-off value of 7% would lead to reduce PLND in 47% (379/807), while missing 3.96% (15/379) cases with LNI. For the MSKCC nomogram using the cut-off value of 7% a PLND would be omitted in 44.5% (359/807), while missing 3.62% (13/359) of cases with LNI. Conclusions: Both analysed nomograms demonstrated high accuracy for prediction of LNI. Using a 7% nomogram cut-off would allow the avoidance up to 47% of PLNDs, while missing less than 4% of patients with LNI.

A novel nomogram predicting lymph node invasion among patients with prostate cancer: The importance of extracapsular extension at multiparametric magnetic resonance imaging

PurposeTo develop a novel risk tool that allows the prediction of lymph node invasion (LNI) among patients with prostate cancer (PCa) treated with robot-assisted radical prostatectomy (RARP) and extended pelvic lymph node dissection (ePLND). MethodsWe retrospectively identified 742 patients treated with RARP + ePLND at a single center between 2012 and 2018. All patients underwent multiparametric magnetic resonance imaging (mpMRI) and were diagnosed with targeted biopsies. First, the nomogram published by Briganti et al. was validated in our cohort. Second, three novel multivariable logistic regression models predicting LNI were developed: (1) a complete model fitted with PSA, ISUP grade groups, percentage of positive cores (PCP), extracapsular extension (ECE), and Prostate Imaging Reporting and Data System (PI-RADS) score; (2) a simplified model where ECE score was not included (model 1); and (3) a simplified model where PI-RADS score was not included (model 2). The predictive accuracy of the models was assessed with the receiver operating characteristic-derived area under the curve (AUC). Calibration plots and decision curve analyses were used. ResultsOverall, 149 patients (20%) had LNI. In multivariable logistic regression models, PSA (OR: 1.03; P= 0.001), ISUP grade groups (OR: 1.33; P= 0.001), PCP (OR: 1.01; P= 0.01), and ECE score (ECE 4 vs. 3 OR: 2.99; ECE 5 vs. 3 OR: 6.97; P< 0.001) were associated with higher rates of LNI. The AUC of the Briganti et al. model was 74%. Conversely, the AUC of model 1 vs. model 2 vs. complete model was, respectively, 78% vs. 81% vs. 81%. Simplified model 1 (ECE score only) was then chosen as the best performing model. A nomogram to calculate the individual probability of LNI, based on model 1 was created. Setting our cut-off at 5% we missed only 2.6% of LNI patients. ConclusionsWe developed a novel nomogram that combines PSA, ISUP grade groups, PCP, and mpMRI-derived ECE score to predict the probability of LNI at final pathology in RARP candidates. The application of a nomogram derived cut-off of 5% allows to avoid a consistent number of ePLND procedures, missing only 2.6% of LNI patients. External validation of our model is needed.

Predictors of Lymph Node Invasion in Patients with Clinically Localized Prostate Cancer Who Undergo Radical Prostatectomy and Extended Pelvic Lymph Node Dissection: The Role of Obesity

Objective: In patients with intermediate- and high-risk localized prostate cancer (PCa), improving the detection of occult lymph node metastases could play a pivotal role for therapeutic counseling and planning. The recent literature shows that several clinical factors may be related to PCa aggressiveness. The aim of this study is to investigate the potential associations between clinical factors and the risk of multiple lymph node invasion (LNI) in patients with intermediate- and high-risk localized PCa (cT1/2, cN0, and ISUP grading group >2 and/or prostate-specific antigen (PSA) >10 ng/mL) who underwent radical prostatectomy (RP) and extended pelvic lymph node dissection (ePLND). Materials and Methods: In a period ranging from January 2014 to December 2018, 880 consecutive patients underwent RP with ePLND for PCa. Among these, 481 met the inclusion criteria and were selected. Data were prospectively collected within an institutional dataset and retrospectively analyzed. Age (years), body mass index (BMI; kg/m²), PSA (ng/mL), prostate volume (mL), and biopsy positive cores (BPC; %) were recorded for each case. BMI and BPC were considered continuous and categorical variables, respectively. The logistic regression models evaluated the association of clinical factors with the risk of nodal metastases. Results: LNI was detected in 73/418 patients (15.2%) of whom 40/418 (8.3%) harbored multiple LNI (median 2, IQR: 3–4). On multivariate analysis, BMI was independently associated with the risk of multiple LNI in the pathological specimen when compared with patients without LNI (OR = 1.147; p = 0.018), as well as the percentage of biopsy positive cores (OR = 1.028; p < 0.0001) and European Association of Urology high-risk class (OR = 5.486; p < 0.0001). BMI was the only predictor of multiple LNI when compared with patients with 1 positive node (OR = 1.189, p = 0.027). Conclusions: In intermediate- and high-risk localized PCa, BMI was an independent predictor of the risk of multiple lymph node metastases. The inclusion of BMI within LNI risk calculators could be helpful, and a detailed counseling in obese patients should be required.

Prediction of the Need for an Extended Lymphadenectomy at the Time of Radical Cystectomy in Patients with Bladder Cancer

BackgroundA prospective randomized trial (LEA AUO AB 25/02) found no survival benefit in extended compared with limited pelvic lymph node dissection (PLND) templates in bladder cancer (BCa) patients treated with radical cystectomy (RC). However, the rate of lymph node invasion (LNI) in the standard and extended templates was lower than estimated. ObjectiveTo assess the accuracy of preoperative clinical and pathological parameters to predict LNI and to develop a model to preoperatively select candidates for the extended PLND templates. Design, setting, and participantsA total of 903 BCa patients treated at a single institution were retrospectively identified. The primary outcome was to identify preoperatively the risk of LNI to tailor the type of PLND. The extended PLND templates consisted in the removal of pelvic lymph nodes together with the common iliac, presacral, para-aortocaval, interaortocaval, and paracaval sites up to the inferior mesenteric artery. InterventionA total of 903 BCa patients were treated with RC and bilateral extended PLND templates. Outcome measurements and statistical analysisSeveral models predicting LNI were evaluated using the area under the receiver operating characteristic curve (AUC), calibration plots and decision curve analyses. A nomogram predicting LNI in the extended pattern was developed and validated internally. Results and limitationsOverall, 55 patients (6.1%) had LNI in the extended PLND templates at RC. The median number of nodes removed was 19 (interquartile range: 13–26). A model including age, clinical T stage, clinical node stage, lymphovascular invasion, and presence of carcinoma in situ at the last transurethral resection before RC was developed. The AUC of this model is 73%. Using a cutoff of 3%, 108 extended PLNDs (12%) would be spared and only two LNIs (3%) would be missed. The main limitations of our model are the retrospective nature of the data, lack of external validation, and low rate of LNI. ConclusionsThis is the first proposed model to predict LNI in the extended PLND templates. This model might help urologists identify which patients might benefit from an extended PLND at the time of RC, reserving a standard PLND for all the others. Patient summaryWe developed the first nomogram to predict lymph node invasion (LNI) in the extended pelvic lymph node dissection templates in bladder cancer patients treated with radical cystectomy. The adoption of our model to identify candidates for the extended pelvic lymph node dissection templates could avoid up to 12% of these procedures at the cost of missing only 3% of patients with LNI.

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

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. A total of 14921 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, 12009 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. 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. 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.