OBJECTIVES: • To develop a '2010 Partin Nomogram' with total prostate-specific antigen (tPSA) as a continuous biomarker, in light of the fact that the current 2007 Partin Tables restrict the application of tPSA as a non-continuous biomarker by creating 'groups' for risk stratification with tPSA levels (ng/mL) of 0-2.5, 2.6-4.0, 4.1-6.0, 6.1-10.0 and >10.0. • To use a 'predictiveness curve' to calculate the percentile risk of a patient among the cohort. PATIENTS AND METHODS: • In all, 5730 and 1646 patients were treated with radical prostatectomy (without neoadjuvant therapy) between 2000 and 2005 at the Johns Hopkins Hospital (JHH) and University Clinic Hamburg-Eppendorf (UCHE), respectively. • Multinomial logistic regression analysis was performed to create a model for predicting the risk of the four non-ordered pathological stages, i.e. organ-confined disease (OC), extraprostatic extension (EPE), and seminal vesicle (SV+) and lymph node (LN+) involvement. • Patient-specific risk was modelled as a function of the B-spline basis of tPSA (with knots at the first, second and third quartiles), clinical stage (T1c, T2a, and T2b/T2c) and biopsy Gleason score (5-6, 3 + 4 = 7, 4 + 3 = 7, 8-10). RESULTS: • The '2010 Partin Nomogram' calculates patient-specific absolute risk for all four pathological outcomes (OC, EPE, SV+, LN+) given a patient's preoperative clinical stage, tPSA and biopsy Gleason score. • While having similar performance in terms of calibration and discriminatory power, this new model provides a more accurate prediction of patients' pathological stage than the 2007 Partin Tables model. • The use of 'predictiveness curves' has also made it possible to obtain the percentile risk of a patient among the cohort and to gauge the impact of risk thresholds for making decisions regarding radical prostatectomy. CONCLUSION: • The '2010 Partin Nomogram' using tPSA as a continuous biomarker together with the corresponding 'predictiveness curve' will help clinicians and patients to make improved treatment decisions.
OBJECTIVES: • To develop a '2010 Partin Nomogram' with total prostate-specific antigen (tPSA) as a continuous biomarker, in light of the fact that the current 2007 Partin Tables restrict the application of tPSA as a non-continuous biomarker by creating 'groups' for risk stratification with tPSA levels (ng/mL) of 0-2.5, 2.6-4.0, 4.1-6.0, 6.1-10.0 and >10.0. • To use a 'predictiveness curve' to calculate the percentile risk of a patient among the cohort. PATIENTS AND METHODS: • In all, 5730 and 1646 patients were treated with radical prostatectomy (without neoadjuvant therapy) between 2000 and 2005 at the Johns Hopkins Hospital (JHH) and University Clinic Hamburg-Eppendorf (UCHE), respectively. • Multinomial logistic regression analysis was performed to create a model for predicting the risk of the four non-ordered pathological stages, i.e. organ-confined disease (OC), extraprostatic extension (EPE), and seminal vesicle (SV+) and lymph node (LN+) involvement. • Patient-specific risk was modelled as a function of the B-spline basis of tPSA (with knots at the first, second and third quartiles), clinical stage (T1c, T2a, and T2b/T2c) and biopsy Gleason score (5-6, 3 + 4 = 7, 4 + 3 = 7, 8-10). RESULTS: • The '2010 Partin Nomogram' calculates patient-specific absolute risk for all four pathological outcomes (OC, EPE, SV+, LN+) given a patient's preoperative clinical stage, tPSA and biopsy Gleason score. • While having similar performance in terms of calibration and discriminatory power, this new model provides a more accurate prediction of patients' pathological stage than the 2007 Partin Tables model. • The use of 'predictiveness curves' has also made it possible to obtain the percentile risk of a patient among the cohort and to gauge the impact of risk thresholds for making decisions regarding radical prostatectomy. CONCLUSION: • The '2010 Partin Nomogram' using tPSA as a continuous biomarker together with the corresponding 'predictiveness curve' will help clinicians and patients to make improved treatment decisions.
Authors: G S Gerber; R A Thisted; P T Scardino; H G Frohmuller; F H Schroeder; D F Paulson; A W Middleton; D B Rukstalis; J A Smith; P F Schellhammer; M Ohori; G W Chodak Journal: JAMA Date: 1996-08-28 Impact factor: 56.272
Authors: Makoto Ohori; Michael W Kattan; Hideshige Koh; Norio Maru; Kevin M Slawin; Shahrokh Shariat; Masatoshi Muramoto; Victor E Reuter; Thomas M Wheeler; Peter T Scardino Journal: J Urol Date: 2004-05 Impact factor: 7.450
Authors: Hideshige Koh; Michael W Kattan; Peter T Scardino; Kazuho Suyama; Norio Maru; Kevin Slawin; Thomas M Wheeler; Makoto Ohori Journal: J Urol Date: 2003-10 Impact factor: 7.450
Authors: Alexander Haese; Manisha Chaudhari; M Craig Miller; Jonathan I Epstein; Hartwig Huland; Juri Palisaar; Markus Graefen; Peter Hammerer; Edward C Poole; Gerard J O'Dowd; Alan W Partin; Robert W Veltri Journal: Cancer Date: 2003-02-15 Impact factor: 6.860
Authors: Ilias Cagiannos; Pierre Karakiewicz; James A Eastham; Makato Ohori; Farhang Rabbani; Claudia Gerigk; Victor Reuter; Markus Graefen; Peter G Hammerer; Andreas Erbersdobler; Hartwig Huland; Patrick Kupelian; Eric Klein; David I Quinn; Susan M Henshall; John J Grygiel; Robert L Sutherland; Phillip D Stricker; Christopher G Morash; Peter T Scardino; Michael W Kattan Journal: J Urol Date: 2003-11 Impact factor: 7.450
Authors: Ingrid Oakley-Girvan; David Feldman; T Ross Eccleshall; Richard P Gallagher; Anna H Wu; Laurence N Kolonel; Jerry Halpern; Raymond R Balise; Dee W West; Ralph S Paffenbarger; Alice S Whittemore Journal: Cancer Epidemiol Biomarkers Prev Date: 2004-08 Impact factor: 4.254
Authors: Kareem N Rayn; Jonathan B Bloom; Samuel A Gold; Graham R Hale; Joseph A Baiocco; Sherif Mehralivand; Marcin Czarniecki; Vikram K Sabarwal; Vladimir Valera; Bradford J Wood; Maria J Merino; Peter Choyke; Baris Turkbey; Peter A Pinto Journal: J Urol Date: 2018-05-29 Impact factor: 7.450
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
Authors: Dennie Meijer; Pim J van Leeuwen; Maarten L Donswijk; Thierry N Boellaard; Ivo G Schoots; Henk G van der Poel; Harry N Hendrikse; Daniela E Oprea-Lager; André N Vis Journal: BJU Int Date: 2021-06-16 Impact factor: 5.969