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

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. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Rajiv Yadav [verfasserIn] Sohrab Arora [verfasserIn] Manish Sachdeva [verfasserIn] Narmada Prasad Gupta [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	India nomogram Partin's nomogram prostate cancer radical prostatectomy robotic prostatectomy

Übergeordnetes Werk:	In: Indian Journal of Urology - Wolters Kluwer Medknow Publications, 2017, 32(2016), 3, Seite 199-203
Übergeordnetes Werk:	volume:32 ; year:2016 ; number:3 ; pages:199-203

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.4103/0970-1591.185096

Katalog-ID:	DOAJ059103280

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520			\|a 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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spelling	10.4103/0970-1591.185096 doi (DE-627)DOAJ059103280 (DE-599)DOAJ9f6aa601caf24ba1a3c917a821379196 DE-627 ger DE-627 rakwb eng RC870-923 Rajiv Yadav verfasserin aut Assessment of the performance of Partin's nomogram (2007) in contemporary Indian cohort 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. India nomogram Partin's nomogram prostate cancer radical prostatectomy robotic prostatectomy Diseases of the genitourinary system. Urology Sohrab Arora verfasserin aut Manish Sachdeva verfasserin aut Narmada Prasad Gupta verfasserin aut In Indian Journal of Urology Wolters Kluwer Medknow Publications, 2017 32(2016), 3, Seite 199-203 (DE-627)508330939 (DE-600)2223236-9 19983824 nnns volume:32 year:2016 number:3 pages:199-203 https://doi.org/10.4103/0970-1591.185096 kostenfrei https://doaj.org/article/9f6aa601caf24ba1a3c917a821379196 kostenfrei http://www.indianjurol.com/article.asp?issn=0970-1591;year=2016;volume=32;issue=3;spage=199;epage=203;aulast=Yadav kostenfrei https://doaj.org/toc/0970-1591 Journal toc kostenfrei https://doaj.org/toc/1998-3824 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2106 GBV_ILN_2232 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 32 2016 3 199-203
allfields_unstemmed	10.4103/0970-1591.185096 doi (DE-627)DOAJ059103280 (DE-599)DOAJ9f6aa601caf24ba1a3c917a821379196 DE-627 ger DE-627 rakwb eng RC870-923 Rajiv Yadav verfasserin aut Assessment of the performance of Partin's nomogram (2007) in contemporary Indian cohort 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. India nomogram Partin's nomogram prostate cancer radical prostatectomy robotic prostatectomy Diseases of the genitourinary system. Urology Sohrab Arora verfasserin aut Manish Sachdeva verfasserin aut Narmada Prasad Gupta verfasserin aut In Indian Journal of Urology Wolters Kluwer Medknow Publications, 2017 32(2016), 3, Seite 199-203 (DE-627)508330939 (DE-600)2223236-9 19983824 nnns volume:32 year:2016 number:3 pages:199-203 https://doi.org/10.4103/0970-1591.185096 kostenfrei https://doaj.org/article/9f6aa601caf24ba1a3c917a821379196 kostenfrei http://www.indianjurol.com/article.asp?issn=0970-1591;year=2016;volume=32;issue=3;spage=199;epage=203;aulast=Yadav kostenfrei https://doaj.org/toc/0970-1591 Journal toc kostenfrei https://doaj.org/toc/1998-3824 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2106 GBV_ILN_2232 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 32 2016 3 199-203
allfieldsGer	10.4103/0970-1591.185096 doi (DE-627)DOAJ059103280 (DE-599)DOAJ9f6aa601caf24ba1a3c917a821379196 DE-627 ger DE-627 rakwb eng RC870-923 Rajiv Yadav verfasserin aut Assessment of the performance of Partin's nomogram (2007) in contemporary Indian cohort 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. India nomogram Partin's nomogram prostate cancer radical prostatectomy robotic prostatectomy Diseases of the genitourinary system. Urology Sohrab Arora verfasserin aut Manish Sachdeva verfasserin aut Narmada Prasad Gupta verfasserin aut In Indian Journal of Urology Wolters Kluwer Medknow Publications, 2017 32(2016), 3, Seite 199-203 (DE-627)508330939 (DE-600)2223236-9 19983824 nnns volume:32 year:2016 number:3 pages:199-203 https://doi.org/10.4103/0970-1591.185096 kostenfrei https://doaj.org/article/9f6aa601caf24ba1a3c917a821379196 kostenfrei http://www.indianjurol.com/article.asp?issn=0970-1591;year=2016;volume=32;issue=3;spage=199;epage=203;aulast=Yadav kostenfrei https://doaj.org/toc/0970-1591 Journal toc kostenfrei https://doaj.org/toc/1998-3824 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2106 GBV_ILN_2232 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 32 2016 3 199-203
allfieldsSound	10.4103/0970-1591.185096 doi (DE-627)DOAJ059103280 (DE-599)DOAJ9f6aa601caf24ba1a3c917a821379196 DE-627 ger DE-627 rakwb eng RC870-923 Rajiv Yadav verfasserin aut Assessment of the performance of Partin's nomogram (2007) in contemporary Indian cohort 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. India nomogram Partin's nomogram prostate cancer radical prostatectomy robotic prostatectomy Diseases of the genitourinary system. Urology Sohrab Arora verfasserin aut Manish Sachdeva verfasserin aut Narmada Prasad Gupta verfasserin aut In Indian Journal of Urology Wolters Kluwer Medknow Publications, 2017 32(2016), 3, Seite 199-203 (DE-627)508330939 (DE-600)2223236-9 19983824 nnns volume:32 year:2016 number:3 pages:199-203 https://doi.org/10.4103/0970-1591.185096 kostenfrei https://doaj.org/article/9f6aa601caf24ba1a3c917a821379196 kostenfrei http://www.indianjurol.com/article.asp?issn=0970-1591;year=2016;volume=32;issue=3;spage=199;epage=203;aulast=Yadav kostenfrei https://doaj.org/toc/0970-1591 Journal toc kostenfrei https://doaj.org/toc/1998-3824 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2014 GBV_ILN_2106 GBV_ILN_2232 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 32 2016 3 199-203
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source	In Indian Journal of Urology 32(2016), 3, Seite 199-203 volume:32 year:2016 number:3 pages:199-203
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issn	19983824
topic_title	RC870-923 Assessment of the performance of Partin's nomogram (2007) in contemporary Indian cohort India nomogram Partin's nomogram prostate cancer radical prostatectomy robotic prostatectomy
topic	misc RC870-923 misc India misc nomogram misc Partin's nomogram misc prostate cancer misc radical prostatectomy misc robotic prostatectomy misc Diseases of the genitourinary system. Urology
topic_unstemmed	misc RC870-923 misc India misc nomogram misc Partin's nomogram misc prostate cancer misc radical prostatectomy misc robotic prostatectomy misc Diseases of the genitourinary system. Urology
topic_browse	misc RC870-923 misc India misc nomogram misc Partin's nomogram misc prostate cancer misc radical prostatectomy misc robotic prostatectomy misc Diseases of the genitourinary system. Urology
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title	Assessment of the performance of Partin's nomogram (2007) in contemporary Indian cohort
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title_full	Assessment of the performance of Partin's nomogram (2007) in contemporary Indian cohort
author_sort	Rajiv Yadav
journal	Indian Journal of Urology
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author_browse	Rajiv Yadav Sohrab Arora Manish Sachdeva Narmada Prasad Gupta
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doi_str_mv	10.4103/0970-1591.185096
author2-role	verfasserin
title_sort	assessment of the performance of partin's nomogram (2007) in contemporary indian cohort
callnumber	RC870-923
title_auth	Assessment of the performance of Partin's nomogram (2007) in contemporary Indian cohort
abstract	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.
abstractGer	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.
abstract_unstemmed	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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title_short	Assessment of the performance of Partin's nomogram (2007) in contemporary Indian cohort
url	https://doi.org/10.4103/0970-1591.185096 https://doaj.org/article/9f6aa601caf24ba1a3c917a821379196 http://www.indianjurol.com/article.asp?issn=0970-1591;year=2016;volume=32;issue=3;spage=199;epage=203;aulast=Yadav https://doaj.org/toc/0970-1591 https://doaj.org/toc/1998-3824
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up_date	2024-07-03T21:47:08.901Z
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