PURPOSE: To assess the relationship between prognostic factors, postradiation prostate-specific antigen (PSA) dynamics, and clinical failure after prostate cancer radiation therapy using contemporary statistical models. METHODS AND MATERIALS: Data from 4,247 patients with 40,324 PSA measurements treated with external beam radiation monotherapy in five cohorts were analyzed. Temporal change of PSA after treatment completion was described by a specially developed linear mixed model that included standard prognostic factors. These factors, along with predicted PSA evolution, were incorporated into a Cox model to establish their predictive value for the risk of clinical recurrence over time. RESULTS: Consistent relationships were found across cohorts. The initial PSA decline after radiation therapy was associated with baseline PSA and T-stage (p < 0.001). The long-term PSA rise was associated with baseline PSA, T-stage, and Gleason score (p < 0.001). The risk of clinical recurrence increased with current level (p < 0.001) and current slope of PSA (p < 0.001). In a pooled analysis, higher doses of radiation were associated with a lower long-term PSA rise (p < 0.001) but not with the risk of recurrence after adjusting for PSA trajectory (p = 0.63). Conversely, after adjusting for other factors, increased age at diagnosis was not associated with long-term PSA rise (p = 0.85) but was directly associated with decreased risk of recurrence (p < 0.001). CONCLUSIONS: We conclude that a linear mixed model can be reliably used to construct typical patient PSA profiles after prostate cancer radiation therapy. Pretreatment factors along with PSA evolution and the associated risk of recurrence provide an efficient and quantitative way to assess the impact of risk factors on disease progression.
PURPOSE: To assess the relationship between prognostic factors, postradiation prostate-specific antigen (PSA) dynamics, and clinical failure after prostate cancer radiation therapy using contemporary statistical models. METHODS AND MATERIALS: Data from 4,247 patients with 40,324 PSA measurements treated with external beam radiation monotherapy in five cohorts were analyzed. Temporal change of PSA after treatment completion was described by a specially developed linear mixed model that included standard prognostic factors. These factors, along with predicted PSA evolution, were incorporated into a Cox model to establish their predictive value for the risk of clinical recurrence over time. RESULTS: Consistent relationships were found across cohorts. The initial PSA decline after radiation therapy was associated with baseline PSA and T-stage (p < 0.001). The long-term PSA rise was associated with baseline PSA, T-stage, and Gleason score (p < 0.001). The risk of clinical recurrence increased with current level (p < 0.001) and current slope of PSA (p < 0.001). In a pooled analysis, higher doses of radiation were associated with a lower long-term PSA rise (p < 0.001) but not with the risk of recurrence after adjusting for PSA trajectory (p = 0.63). Conversely, after adjusting for other factors, increased age at diagnosis was not associated with long-term PSA rise (p = 0.85) but was directly associated with decreased risk of recurrence (p < 0.001). CONCLUSIONS: We conclude that a linear mixed model can be reliably used to construct typical patientPSA profiles after prostate cancer radiation therapy. Pretreatment factors along with PSA evolution and the associated risk of recurrence provide an efficient and quantitative way to assess the impact of risk factors on disease progression.
Authors: C I Sartor; M H Strawderman; X H Lin; K E Kish; P W McLaughlin; H M Sandler Journal: Int J Radiat Oncol Biol Phys Date: 1997-07-15 Impact factor: 7.038
Authors: Mack Roach; Kathryn Winter; Jeffrey M Michalski; James D Cox; James A Purdy; Walter Bosch; Xiao Lin; William S Shipley Journal: Int J Radiat Oncol Biol Phys Date: 2004-12-01 Impact factor: 7.038
Authors: W U Shipley; H D Thames; H M Sandler; G E Hanks; A L Zietman; C A Perez; D A Kuban; S L Hancock; C D Smith Journal: JAMA Date: 1999-05-05 Impact factor: 56.272
Authors: Winkle Kwan; Tom Pickles; Graeme Duncan; Mitchell Liu; Alexander Agranovich; Eric Berthelet; Mira Keyes; Charmaine Kim-Sing; W James Morris; Chuck Paltiel Journal: Int J Radiat Oncol Biol Phys Date: 2004-11-15 Impact factor: 7.038
Authors: Scott G Williams; Gillian M Duchesne; Jeremy L Millar; Gary R Pratt Journal: Int J Radiat Oncol Biol Phys Date: 2004-11-15 Impact factor: 7.038
Authors: Mbéry Sène; Jeremy Mg Taylor; James J Dignam; Hélène Jacqmin-Gadda; Cécile Proust-Lima Journal: Stat Methods Med Res Date: 2014-05-20 Impact factor: 3.021
Authors: Cécile Proust-Lima; Jeremy M G Taylor; Solène Sécher; Howard Sandler; Larry Kestin; Tom Pickles; Kyoungwha Bae; Roger Allison; Scott Williams Journal: Int J Radiat Oncol Biol Phys Date: 2010-04-08 Impact factor: 7.038
Authors: Joseph R Evans; Shuang G Zhao; S Laura Chang; Scott A Tomlins; Nicholas Erho; Andrea Sboner; Matthew J Schiewer; Daniel E Spratt; Vishal Kothari; Eric A Klein; Robert B Den; Adam P Dicker; R Jeffrey Karnes; Xiaochun Yu; Paul L Nguyen; Mark A Rubin; Johann de Bono; Karen E Knudsen; Elai Davicioni; Felix Y Feng Journal: JAMA Oncol Date: 2016-04 Impact factor: 31.777
Authors: Jeremy M G Taylor; Yongseok Park; Donna P Ankerst; Cecile Proust-Lima; Scott Williams; Larry Kestin; Kyoungwha Bae; Tom Pickles; Howard Sandler Journal: Biometrics Date: 2013-02-04 Impact factor: 2.571