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Year : 2016  |  Volume : 32  |  Issue : 3  |  Page : 199-203

Assessment of the performance of Partin's nomogram (2007) in contemporary Indian cohort

Department of Urology, Institute of Kidney and Urology, Medanta - The Medicity, Gurgaon, Haryana, India

Correspondence Address:
Rajiv Yadav
Senior Consultant, Urologic Oncology and Robotic Surgery, Institute of Kidney and Urology, Medanta - The Medicity, Sector 38, Gurgaon, Haryana
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0970-1591.185096

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Introduction: Partin's nomogram is an important prognostic tool to predict adverse pathological features for clinically localized prostate carcinoma. This tool is widely used by both radiation and surgical oncologists for pre-intervention counseling, treatment planning, and predicting the possible need for adjuvant treatment. However, the model is derived from a Western population with typical characteristics of prostate cancer in a prostate-specific antigen (PSA) screened population. Therefore, this study was conducted to assess the performance of the Partin's nomogram as applied to an Indian cohort by assessing the discrimination and calibration properties. Methods: A retrospective review of 282 patients treated with robotic radical prostatectomy from 2010 to 2015 was conducted. Partin tables (year 2007) were used to calculate the predicted probabilities for lymph node invasion (LNI), seminal vesicle invasion (SVI), and extraprostatic extension (EPE). The discrimination properties were assessed using the receiver operating characteristic (ROC) curves. Calibration of the model was done to show the relationship between predicted and observed values. Results: The mean age of the patients was 64.3 years. Most (59.4%) were clinical T2 disease. Patients with PSA >10 ng/ml comprised 60% of the population. ECE, SVI, and LNI were present in 39.2%, 22%, and 11% of cases, respectively. ROC analysis revealed area under curve values for EPE, SVI, and LNI of 68%, 67.5%, and 71.2%, respectively. Calibration plot suggested that the Partin tables under-predicted the risk whenever the values of predicted risk were more than 26%, 3%, and 1% for EPE, SVI, and LNI, respectively, and over predicted when the risk was lower. Conclusion: Our data show that Partin's tables, despite having fair discrimination properties, do not accurately predict LNI, SVI, and ECE across the entire range of predicted values in a contemporary Indian cohort.

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