Role of Bilateral Pelvic Lymphadenectomy in Prostatic Cancer

1997 ◽  
Vol 64 (3) ◽  
pp. 348-350
Author(s):  
A. Fandella ◽  
L. Maccatrozzo ◽  
F. Merlo ◽  
L. Faggiano ◽  
P. Cecchin ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Lymph Node ◽  
Clinical Stage ◽  
Specific Antigen ◽  
Elevated Serum ◽  
Lymph Node Involvement ◽  
Radical Retropubic Prostatectomy ◽  
Low Risk ◽  
Retropubic Prostatectomy ◽  
Node Involvement

Objectives: to identify a group of patients with prostate cancer for whom open staging pelvic lymph node dissection (PLND) could be superfluous. Methods: the medical records of all patients presenting with prostate cancer from January 1992 to December 1996 were reviewed. A total of 118 patients with clinically localized disease were selected to undergo radical retropubic prostatectomy (RRP) preceded by open PLND. Final nodal status was correlated with the value of the preoperative serum prostate specific antigen (PSA) concentration, clinical stage (TNM), and grading (by OMS) to evaluate the predictivity of nodal involvement. We identified 3 groups: PSA <10 ng/ml, T1–2, G1-2, = 1st very low risk, PSA 10 −15, T1-2 - G1-2 = 2nd low risk, PSA <15 T3 or G3 or PSA >15 every T and G = 3rd high risk. Results: overall, only 21 patients (18%) had lymph node metastases. Lymph node involvement was significantly correlated with elevated serum PSA values, high grading, and advanced clinical stage. 35 patients belonged to the first 2 groups, presenting with low PSA and favorable clinical stage and grade, none with lymph node involvement. These patients could have avoided PLND with a very low risk of missing something. Conclusions: open staging PLND may no longer be justified on a routine basis in patients undergoing radical retropubic prostatectomy.

scholarly journals First Nomogram Predicting the Probability of Lymph Node Involvement in Prostate Cancer Patients Undergoing Radioisotope Guided Sentinel Lymph Node Dissection

2015 ◽  
Vol 95 (4) ◽  
pp. 422-428 ◽  
Author(s):  
Alexander Winter ◽  
Thomas Kneib ◽  
Martin Rohde ◽  
Rolf-Peter Henke ◽  
Friedhelm Wawroschek
Keyword(s):  
Prostate Cancer ◽  
Lymph Node ◽  
Lymph Node Dissection ◽  
Predictive Accuracy ◽  
Clinical Stage ◽  
Specific Antigen ◽  
Lymph Node Involvement ◽  
High Rate ◽  
Node Dissection ◽  
Node Involvement

Introduction: Existing nomograms predicting lymph node involvement (LNI) in prostate cancer (PCa) are based on conventional lymphadenectomy. The aim of the study was to develop the first nomogram for predicting LNI in PCa patients undergoing sentinel guided pelvic lymph node dissection (sPLND). Materials and Methods: Analysis was performed on 1,296 patients with PCa who underwent radioisotope guided sPLND and retropubic radical prostatectomy (2005-2010). Median prostate specific antigen (PSA): 7.4 ng/ml (IQR 5.3-11.5 ng/ml). Clinical T-categories: T1: 54.8%, T2: 42.4%, T3: 2.8%. Biopsy Gleason sums: ≤6: 55.1%, 7: 39.5%, ≥8: 5.4%. Multivariate logistic regression models tested the association between all of the above predictors and LNI. Regression-based coefficients were used to develop a nomogram for predicting LNI. Accuracy was quantified using the area under the curve (AUC). Results: The median number of LNs removed was 10 (IQR 7-13). Overall, 17.8% of patients (n = 231) had LNI. The nomogram had a high predictive accuracy (AUC of 82%). All the variables were statistically significant multivariate predictors of LNI (p = 0.001). Univariate predictive accuracy for PSA, Gleason sum and clinical stage was 69, 75 and 69%, respectively. Conclusions: The sentinel nomogram can predict LNI at a sPLND very accurately and, for the first time, aid clinicians and patients in making important decisions on the indication of a sPLND. The high rate of LN+ patients underscores the sensitivity of sPLND.

scholarly journals Case: Spontaneous regression of post-radical prostatectomy prostate-specific antigen elevation without adjuvant therapy in a patient with lymph node metastasis

2017 ◽  
Vol 11 (7) ◽  
pp. E315-7
Author(s):  
Taehyoung Lee ◽  
Yanbo Guo ◽  
Saahil Vij ◽  
Rahul Bansal ◽  
Nathan C. Wong ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Lymph Node ◽  
Radical Prostatectomy ◽  
Adjuvant Therapy ◽  
Gleason Score ◽  
Clinical Stage ◽  
Specific Antigen ◽  
Surgical Margin ◽  
Lymph Node Involvement ◽  
Complete Regression

Prostate cancer remains the most frequently diagnosed cancer among men. The combination of clinical stage, serum prostatespecific antigen (PSA), and Gleason score (biopsy) assists in predictive assessment of pathological stage and prognosis. Furthermore, pathological criteria, including Gleason score, surgical margin status, extracapsular extension, seminal vesicle invasion, and lymph node involvement, provide prognostication in patients undergoing radical prostatectomy (RP). In this paper, we present a case of a patient with high-risk prostate cancer with persistent PSA elevation post-RP who experiences a complete regression of PSA without any adjuvant therapy. To the authors’ knowledge, such a finding has not been described in the literature previously.

scholarly journals Artificial Intelligence Combined With Big Data to Predict Lymph Node Involvement in Prostate Cancer: A Population-Based Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Liwei Wei ◽  
Yongdi Huang ◽  
Zheng Chen ◽  
Hongyu Lei ◽  
Xiaoping Qin ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Big Data ◽  
Lymph Node ◽  
False Negative ◽  
Specific Antigen ◽  
Lymph Node Involvement ◽  
Net Benefit ◽  
Cutoff Value ◽  
Model Based ◽  
Node Involvement

BackgroundA more accurate preoperative prediction of lymph node involvement (LNI) in prostate cancer (PCa) would improve clinical treatment and follow-up strategies of this disease. We developed a predictive model based on machine learning (ML) combined with big data to achieve this.MethodsClinicopathological characteristics of 2,884 PCa patients who underwent extended pelvic lymph node dissection (ePLND) were collected from the U.S. National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. Eight variables were included to establish an ML model. Model performance was evaluated by the receiver operating characteristic (ROC) curves and calibration plots for predictive accuracy. Decision curve analysis (DCA) and cutoff values were obtained to estimate its clinical utility.ResultsThree hundred and forty-four (11.9%) patients were identified with LNI. The five most important factors were the Gleason score, T stage of disease, percentage of positive cores, tumor size, and prostate-specific antigen levels with 158, 137, 128, 113, and 88 points, respectively. The XGBoost (XGB) model showed the best predictive performance and had the highest net benefit when compared with the other algorithms, achieving an area under the curve of 0.883. With a 5%~20% cutoff value, the XGB model performed best in reducing omissions and avoiding overtreatment of patients when dealing with LNI. This model also had a lower false-negative rate and a higher percentage of ePLND was avoided. In addition, DCA showed it has the highest net benefit across the whole range of threshold probabilities.ConclusionsWe established an ML model based on big data for predicting LNI in PCa, and it could lead to a reduction of approximately 50% of ePLND cases. In addition, only ≤3% of patients were misdiagnosed with a cutoff value ranging from 5% to 20%. This promising study warrants further validation by using a larger prospective dataset.

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