Nomogram Predicting Lymph Node Invasion Research Articles

External validation of the Memorial Sloan Kettering Cancer Center preoperative nomogram predicting lymph node invasion in a cohort of high-grade prostate cancer patients.

Commonly used preoperative nomograms predicting clinical and pathological outcomes in prostate cancer (PCa) patients have not been yet validated in high-grade only PCapatients. Our objective is to perform an external validation of the Memorial Sloan Kettering Cancer Center (MSKCC) preoperative nomogram as a predictor of lymph node invasion (LNI) in a cohort of high-grade PCa patients. We included patients with high-grade PCa (Gleason ≥8) treated at our institution between 2011 and 2020 with radical prostatectomy and pelvic lymph node dissection without receiving neoadjuvant or adjuvant therapy. The area under the curve (AUC) of the receiver operator characteristic (ROC) was used to quantify the accuracy of the model to predict LNI. A calibration plot was used to evaluate the model's precision, and a decision curve analysiswas computed to evaluate the net benefit associated with its use. This study was approved by our institution's ethics board. A total of 242 patients with a median age of 66 (60-71) years were included. LNI was observed in 70 (29%) patients with a mean of 16 (median = 15; range = 2-42) resected nodes. The MSKCC nomogram discriminative accuracy, as evaluated by the AUC-ROC was 79.0% (CI: [0.727-0.853]). The MSKCC preoperative nomogram is a good predictor of LNI and a useful tool associated with net clinical benefit in this patient 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

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.

1113 - External validation of the new Briganti nomogram predicting lymph node invasion in patients with intermediate and high-risk prostate cancer: Results from a multicentric European cohort

1113 - External validation of the new Briganti nomogram predicting lymph node invasion in patients with intermediate and high-risk prostate cancer: Results from a multicentric European cohort

External validation of the Briganti nomogram predicting lymph node invasion in patients with intermediate and high-risk prostate cancer diagnosed with magnetic resonance imaging-targeted and systematic biopsies: A European multicenter study

ObjectiveTo validate a nomogram predicting lymph node invasion (LNI) in prostate cancer patients undergoing radical prostatectomy taking into consideration multiparametric-magnetic resonance imaging (mp-MRI) parameters and targeted biopsies in a western European cohort. Patients and MethodsA total of 473 men diagnosed by targeted biopsies, using software-based MRI-ultrasound image fusion system, and operated by radical prostatectomy with extended pelvic lymph node dissection across 11 Europeans centers between 2012 and 2019 were identified. Area under the curve of the receiver operator characteristic curve, calibration plot and decision curve analysis were used to evaluated the performance of the model. ResultsOverall, 56 (11.8%) patients had LNI on final pathologic examination with a median (IQR) of 13 (9–18) resected nodes. Significant differences (all P < 0.05) were found between patients with and without LNI in terms of preoperative PSA, clinical stage at DRE and mp-MRI, maximum diameter of the index lesion, PI-RADS score, Grade Group on systematic and targeted biopsies, total number of dissected lymph nodes, final pathologic staging and Grade Group. External validation of the prediction model showed a good accuracy with an area under the curve calculated as 0.8 (CI 95% 0.75–0.86). Graphic analysis of calibration plot and decision curve analysis showed a slight underestimation for predictive probability for LNI between 3% and 22% and a high net benefit. A cut-off at 7% was associated with a risk of missing LNI in 2.6%, avoiding unnecessary surgeries in 55.9%. ConclusionsWe report an external validation of the nomogram predicting LNI in patients treated with extended pelvic lymph node dissection in a western European cohort and a cut-off at 7% seems appropriate.

External Validation of the 2019 Briganti Nomogram for the Identification of Prostate Cancer Patients Who Should Be Considered for an Extended Pelvic Lymph Node Dissection

The 2019 Briganti nomogram was developed to calculate the risk of lymph node invasion (LNI) and identify prostate cancer (PCa) patients diagnosed with magnetic resonance imaging (MRI)-targeted biopsy who should be considered for an extended pelvic lymph node dissection (ePLND). Since its implementation is still limited by lack of a formal external validation, we aimed to validate this tool in a large contemporary cohort. We identified 487 patients diagnosed using MRI-targeted with concomitant systematic biopsy who underwent radical prostatectomy (RP) and an anatomically defined ePLND at six centers. The external validity of the 2019 Briganti nomogram for estimating LNI risk was assessed via calibration, discrimination, and decision curve analyses (DCAs). A total of 38 (8%) patients had LNI at final pathology. The median number of nodes removed was 18 (interquartile range 14–24). On external validation, the 2019 Briganti nomogram had an area under the receiver operating characteristic curve (AUC) of 79%. Although there was some miscalibration, this was at predicted probabilities >35% and therefore outside the clinically relevant range. DCA demonstrated that the 2019 Briganti nomogram improved clinical risk prediction against LNI threshold probabilities of ≤30%. For a 7% cutoff, 273 (56%) ePLNDs would be spared and only 2.6% LNIs would be missed. The 2019 Briganti nomogram was characterized by higher AUC compared to the 2012 and 2017 Briganti nomograms and the Memorial Sloan Kettering Cancer Center risk calculator (79% vs 75% vs 65% vs 74%) and demonstrated the highest net benefit on DCA. This first multi-institutional validation of the 2019 Briganti nomogram in predicting LNI in PCa patients diagnosed with MRI-targeted biopsy confirms the highest AUC, better calibration and the highest net benefit compared with available tools and should be adopted to identify candidates for ePLND among men diagnosed with MRI-targeted biopsy. Patient summaryWe performed the first multi-institutional validation of the first nomogram predicting lymph node invasion specifically developed using data from prostate cancer patients diagnosed with magnetic resonance imaging (MRI)-targeted biopsy. This nomogram exhibited excellent characteristics on external validation compared with available tools and should be adopted to identify candidates for extended pelvic lymph node dissection among men diagnosed with MRI-targeted biopsy.

SC120 - A novel nomogram predicting lymph node invasion among patients with clinically localized prostate cancer: The importance of extracapsular extension at preoperative magnetic resonance imaging

SC120 - A novel nomogram predicting lymph node invasion among patients with clinically localized prostate cancer: The importance of extracapsular extension at preoperative magnetic resonance imaging

Sentinel lymph node surgery in prostate cancer using magnetic particles.

Superparamagnetic iron oxide nanoparticles (SPIONs) are tested to identify sentinel lymph nodes (SLNs) to exploit the advantages of targeted pelvic SLN dissection (sPLND), while circumventing the disadvantages of established radioactive labeling. Here we review recent studies about sPLND in prostate cancer (PCa), including the first results of SLN detection using intraprostatic SPION-injection. A recent systematic literature review reveals that the diagnostic accuracy of sPLND is comparable with extended PLND (ePLND). sPLND combined with ePLND achieve better node removal by increasing the number of affected nodes. The first sentinel-based nomogram predicting lymph node invasion is established. A sentinel-nomogram update provides comparative predictions relative to ePLND models. sPLND using a magnetometer and SPIONs as a tracer is successful whenever applied to PCa, and SLN identification using MRI after intraprostatic injection of SPIONs is feasible. SLNs are present in an unexpectedly high number outside the ePLND template. SLN detection outside the ePLND template and the increased diagnostic value of sPLND compared with ePLND supports the individualized extension of PLND using sPLND in PCa. SPION-MRI, combined with a hand-held magnetometer, provides a nonradioactive technique for preoperative and intraoperative SLN localization. Further studies are required to evaluate the effects of sPLND on oncological outcomes.

693 External validation of the updated nomogram predicting lymph node invasion in patients with prostate cancer undergoing extended pelvic lymph node dissection

693 External validation of the updated nomogram predicting lymph node invasion in patients with prostate cancer undergoing extended pelvic lymph node dissection

External Validation of the Updated Nomogram Predicting Lymph Node Invasion in Patients with Prostate Cancer Undergoing Extended Pelvic Lymph Node Dissection

Introduction: The aim of our study was to determine the validity of the updated nomogram [Briganti et al.: Eur Urol 2012;61:480-487] as a prediction tool for pelvic lymph node invasion (LNI) in the current era by using a large multicentric population of men who underwent extended pelvic lymph node dissection (ePLND) at the time of radical prostatectomy (RP) at tertiary referral centers. Patients and Methods: Between 2000 and 2011, 896 consecutive patients underwent RP and ePLND at two tertiary referral centers for clinically localized prostate cancer (PCa). Uni- and multivariable logistic regression models predicting the presence of LNI at ePLND were built in. Covariates consisted of preoperative PSA, clinical stage, primary and secondary biopsy Gleason grade with or without percentage of positive cores. Patients' data were entered into a logistic model formula derived from the original publication of Briganti. The nomogram was assessed by comparing its predicted probability of LNI with the actual presence of LNI. The area under the curve was used to quantify its predictive accuracy. Results: Mean preoperative PSA, clinical and pathological stage, primary and secondary biopsy and pathological Gleason grade, such as mean number of total cores, positive cores and percentage of positive cores differed significantly between LNI-positive and LNI-negative patients (all p < 0.001 except for number of total cores, p = 0.019). The mean number of lymph nodes removed was 14.8, and LNI was found in 101 patients (11.8%). In the univariate analysis the percentage of positive cores was the most accurate predictor of LNI (72%), followed by PSA (69%), primary biopsy Gleason grade (64%), clinical stage (60%), and secondary biopsy Gleason grade (59%). The predictions of the nomogram were virtually perfect when the predicted probability was ≤20%. We tested the performance characteristics of various Briganti nomogram-derived cut-offs (1-14%) for discriminating between patients with and without LNI. In our population, 41.6% of patients were classified below the 5% cut-off proposed in the original Briganti et al. report. In the multivariate analysis these variables remained statistically significant predictors for the presence of lymph node metastases. The predictive accuracy of the full model reached 79%. Conclusions: The updated nomogram predicting LNI in patients with PCa undergoing ePLND has been externally validated, demonstrating excellent accuracy and calibration characteristics and a general applicability for predicting the presence of LNI.

National Comprehensive Cancer Network Practice Guidelines 2011: Need for More Accurate Recommendations for Pelvic Lymph Node Dissection in Prostate Cancer

The 2011 NCCN (National Comprehensive Cancer Network) guidelines for prostate cancer recommend pelvic lymph node dissection at radical prostatectomy in all individuals with a nomogram predicted lymph node invasion probability of 2% or greater. We examined the ability of these guidelines to correctly predict lymph node invasion in patients treated with extended pelvic lymph node dissection. We examined 3,064 consecutive patients treated with radical prostatectomy and extended pelvic lymph node dissection between 2000 and 2010. We formally validated the NCCN guideline nomogram using discrimination, calibration and decision curve analysis as benchmarks. Moreover the performance characteristics of the 2% nomogram cutoff as well as other cutoff values (range 1% to 10%) were tested. Overall 10.0% of patients had lymph node invasion. The discrimination accuracy of the NCCN guideline nomogram was 79.8%, with a maximum underestimation of the lymph node invasion risk of 41.2%. On decision curve analysis the NCCN nomogram fared better than not performing pelvic lymph node dissection in all patients. However, in the prediction range between 0% and 9% the nomogram did not fare better than performing pelvic lymph node dissection in all patients. The use of the 2% cutoff would allow the avoidance of 49.3% of pelvic lymph node dissections, at the cost of missing 20.3% of patients with lymph node invasion. The NCCN nomogram tends to significantly underestimate the real lymph node invasion rate. Moreover the use of the currently recommended cutoff of 2% to trigger pelvic lymph node dissection might not be appropriate.

Updated Nomogram Predicting Lymph Node Invasion in Patients with Prostate Cancer Undergoing Extended Pelvic Lymph Node Dissection: The Essential Importance of Percentage of Positive Cores

BackgroundFew predictive models aimed at predicting the presence of lymph node invasion (LNI) in patients with prostate cancer (PCa) treated with extended pelvic lymph node dissection (ePLND) are available to date. ObjectiveUpdate a nomogram predicting the presence of LNI in patients treated with ePLND at the time of radical prostatectomy (RP). Design, setting, and participantsThe study included 588 patients with clinically localised PCa treated between September 2006 and October 2010 at a single tertiary referral centre. InterventionAll patients underwent RP and ePLND invariably including removal of obturator, external iliac, and hypogastric nodes. MeasurementsProstate-specific antigen, clinical stage, and primary and secondary biopsy Gleason grade as well as percentage of positive cores were included in univariable (UVA) and multivariable (MVA) logistic regression models predicting LNI and formed the basis for the regression coefficient-based nomogram. The area under the curve (AUC) method was used to quantify the predictive accuracy (PA) of the model. Results and limitationsThe mean number of lymph nodes removed and examined was 20.8 (median: 19; range: 10–52). LNI was found in 49 of 588 patients (8.3%). All preoperative PCa characteristics differed significantly between LNI-positive and LNI-negative patients (all p<0.001). In UVA predictive accuracy analyses, percentage of positive cores was the most accurate predictor of LNI (AUC: 79.5%). At MVA, clinical stage, primary biopsy Gleason grade, and percentage of positive cores were independent predictors of LNI (all p ≤ 0.006). The updated nomogram demonstrated a bootstrap-corrected PA of 87.6%. Using a 5% nomogram cut-off, 385 of 588 patients (65.5%) would be spared ePLND. and LNI would be missed in only 6 patients (1.5%). The sensitivity, specificity, and negative predictive value associated with the 5% cut-off were 87.8%, 70.3%, and 98.4%, respectively. The relatively low number of patients included as well as the lack of an external validation represent the main limitations of our study. ConclusionsWe report the first update of a nomogram predicting the presence of LNI in patients treated with ePLND. The nomogram maintained high accuracy, even in more contemporary patients (87.6%). Because percentage of positive cores represents the foremost predictor of LNI, its inclusion should be mandatory in any LNI prediction model. Based on our model, those patients with a LNI risk<5% might be safely spared ePLND.