OBJECTIVE: A formal validation and head-to-head comparison of the National Comprehensive Cancer Network (NCCN) practice guideline lymph node invasion (LNI) nomogram, Partin tables, and D'Amico risk-classification was conducted for prediction of LNI at radical prostatectomy (RP). METHODS: We focused on 20,877 patients treated with RP and pelvic lymph node dissection (PLND) between 2004 and 2006 within the Surveillance, Epidemiology and End Results database. The discrimination of the 3 tools in predicting histologically confirmed LNI was quantified using the area under the curve (AUC). Calibration plots were used to graphically depict the performance characteristics of the examined tools. In addition, we relied on decision curve analyses to compare the 3 models directly in a head-to-head fashion. RESULTS: Overall, 2.5% of patients had LNI. The NCCN LNI nomogram (AUC 82%) outperformed the Partin tables (73%) and the D'Amico risk-classification (75%) for prediction of LNI. Calibration plots revealed that all 3 tools overestimated the risk of LNI. Partin tables showed the highest net-benefit for probability threshold range between 1% and 4%. Conversely, the NCCN LNI nomogram showed the highest net-benefit for the remaining threshold probabilities. CONCLUSION: The NCCN LNI nomogram had the highest discrimination accuracy. However, using the decision curve analysis, the Partin tables demonstrated the highest net benefit when a threshold probability of LNI is <4%. In contrast, the NCCN LNI nomogram had the highest net benefit when the threshold probability used to perform PLND is greater than 4%. Crown
OBJECTIVE: A formal validation and head-to-head comparison of the National Comprehensive Cancer Network (NCCN) practice guideline lymph node invasion (LNI) nomogram, Partin tables, and D'Amico risk-classification was conducted for prediction of LNI at radical prostatectomy (RP). METHODS: We focused on 20,877 patients treated with RP and pelvic lymph node dissection (PLND) between 2004 and 2006 within the Surveillance, Epidemiology and End Results database. The discrimination of the 3 tools in predicting histologically confirmed LNI was quantified using the area under the curve (AUC). Calibration plots were used to graphically depict the performance characteristics of the examined tools. In addition, we relied on decision curve analyses to compare the 3 models directly in a head-to-head fashion. RESULTS: Overall, 2.5% of patients had LNI. The NCCN LNI nomogram (AUC 82%) outperformed the Partin tables (73%) and the D'Amico risk-classification (75%) for prediction of LNI. Calibration plots revealed that all 3 tools overestimated the risk of LNI. Partin tables showed the highest net-benefit for probability threshold range between 1% and 4%. Conversely, the NCCN LNI nomogram showed the highest net-benefit for the remaining threshold probabilities. CONCLUSION: The NCCN LNI nomogram had the highest discrimination accuracy. However, using the decision curve analysis, the Partin tables demonstrated the highest net benefit when a threshold probability of LNI is <4%. In contrast, the NCCN LNI nomogram had the highest net benefit when the threshold probability used to perform PLND is greater than 4%. Crown
Authors: Hillary M Ross; Oleksandr N Kryvenko; Janet E Cowan; Jeffry P Simko; Thomas M Wheeler; Jonathan I Epstein Journal: Am J Surg Pathol Date: 2012-09 Impact factor: 6.394
Authors: G J Nason; E M O'Connor; D MacMahon; B Moss; S W Considine; A Cahill; C O'Rourke; F M O'Brien Journal: Ir J Med Sci Date: 2017-05-06 Impact factor: 1.568
Authors: John B Eifler; Zhaoyang Feng; Brian M Lin; Michael T Partin; Elizabeth B Humphreys; Misop Han; Jonathan I Epstein; Patrick C Walsh; Bruce J Trock; Alan W Partin Journal: BJU Int Date: 2012-07-26 Impact factor: 5.588