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.

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.

Sentinel and Non-Sentinel Lymph Node Metastasis Prediction and Validation of the MSKCC Nomograms for Indian Breast Cancer Patients

Background: Sentinel lymph node (SLN) biopsy accurately stages the axilla, but is time consuming and resource intensive. Nomograms and scoring systems have been developed, based on clinical and pathologic data available before surgery, to attempt to predict the likelihood of lymph node metastasis before surgery. As the management of the axilla in patients with low nodal burden changes, it is also important to predict whether there will be further axillary disease in patients with a positive SLN. Aim: To explore the risk factors for SLN and non-SLN metastasis in Indian women with breast cancer, by analysis of clinical and pathologic data. To assess the validity and clinical utility of two MSKCC nomograms that predicts axillary lymph node status for Western patients. Methods: Clinical data, and pathologic data available from core biopsy, for a consecutive series of women having SLNB was analyzed, and was plotted on two MSKCC nomograms. Univariate analysis was done by χ2 and Fischer exact tests and multivariate analysis was done by logistic regression method. A receiver-operating characteristic (ROC) curve was drawn and predictive accuracy was assessed by calculating the area under the ROC curve (AUC). Results: 34% (89 out of 256) of our patients had SLN positivity. When correlated with SLN metastasis by univariate analysis, LVI (χ2 = 80, P ≤ 0.001), PNI (χ2 = 13.36, P ≤ 0.001), ER+ (χ2 = 6.85, P = 0.009), PR+ (χ2 = 7.1, P = 0.008) and age ( P = 0.03) were significant. However, multivariate analysis showed that age (OR=1.04, P = 0.007) and LVI (OR=0.07, P ≤ 0.001) were identified as independent predictors for SLN metastasis. The area under the ROC curve was 0.772 and it fairly correlated with MSKCC nomogram. Patients with MSKCC scores lower than 38% had a frequency of SLN metastasis of 7.7% (5/65) and this cut-off could be used as a guide for not doing frozen section analysis in this subgroup. Further axillary dissection showed 41% (38 out of 92) had non-sentinel nodes positive. When correlated with non-SLN metastasis by univariate analysis, LVI (χ2 = 8.8, P = 0.003), PNI (χ2 = 6.85, P = 0.009), and extracapsular extension (χ2 = 4.18, P = 0.04) were significant. Number of SLN negative ( P = 0.01), SLN ratio (number of SLN positive/total number of SLN removed) ( P = 0.01) and size of SLN metastasis ( P = 0.002) were significant. However, multivariate analysis showed that only size of SLN metastasis (OR=0.845, P = 0.02) was identified as independent predictor for non-SLN metastasis. The area under the ROC curve was 0.66 and it poorly correlated with MSKCC nomogram. Conclusion: The MSKCC nomogram can provide a fairly accurate prediction of the probability of SLN metastasis, but is not for non-SLN metastasis. An institutional nomogram for non-SLN metastasis, including additional factors such as size of SLN metastasis, may improve prediction.

CK19 combined with contrast-enhanced ultrasound: A prediction model for non-sentinel lymph node involvement in early breast cancer.

11539 Background: According to Z0011 and AMAROS trials, patients with breast cancer stage cT1~2cN0 and sentinel lymphnode (SLN) 1~2 involvement can avoid axillary lymphnode dissection (ALND). But the risk of non-sentinel lymphnode (nSLN) involvement in those early stage patients is still unclear and it is difficult to predicting the risk before surgery. Our previous study showed CK19 mRNA in peripheral blood had predicative value of nSLN involvement. Also, contrast-enhanced ultrasound (CEUS) is a new effective method exmaming axillary lymph node. We aim to establish a prediction model for nSLN involvement in early breast cancer using CK19 combined with CEUS score. Methods: We identified 119 cases diagnosed early breast cancer (stage cT1~2cN0 and 1~2 SLNs involvement as in Z0011 and AMAROS trials) from Oct 2015 to Nov 2016 in Zhejiang Cancer Hospital. The CK19 mRNA of peripheral blood by RT-PCR and CEUS score of axillary lymph nodes were acquired before surgery. We used logistic regression analysis for filtering out valuable predictive clinical parameters and establishing formulas to calculate the probability of nSLN involvement. Our model was compared with Memorial Sloan Kettering Cancer Center (MSKCC) nomogram, which is one of the most reliable and validated methods for predicting of nSLN. Results: The histological grade, CK19 and CEUS score were screened by logistic regression analysis into the formula to calculate the probability of nSLN involvement. The sensitivity, specificity, total accuracy of this model was 89.13%, 80.82% and 84.03%, respectively. The false negative rate was 10.87%. The model had high quality of consistency (Kappa 0.675, p < 0.01) and goodness of fit (likelihood-ratio test, -2log liklihood = 84.607). The area under curve (AUC) of ROC was significantly higher (P < 0.01) in our model (0.914, 95%CI, 0.863~0.965) than in MSKCC nomogram (0.563, 95%CI, 0.459~0.667). Conclusions: The prediction model based on CK19 and CEUS score has satisfying sensitivity, specificity and accuracy, more effective than MSKCC nomogram. It is a valuable model of evaluating the risk of nSLN involvement in early breast cancer before surgery, picking out the patients who can truly avoid ALND.