title: Determinants of change in prostate-specific antigen over time and its association with recurrence after external beam radiation therapy for prostate cancer in five large cohorts.
abstract: OBJECTIVE
  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
  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.
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
authors.forename: Cécile
authors.initials: C
authors.lastname: Proust-Lima
authors.forename: Jeremy M G
authors.initials: JM
authors.lastname: Taylor
authors.forename: Scott G
authors.initials: SG
authors.lastname: Williams
authors.forename: Donna P
authors.initials: DP
authors.lastname: Ankerst
authors.forename: Ning
authors.initials: N
authors.lastname: Liu
authors.forename: Larry L
authors.initials: LL
authors.lastname: Kestin
authors.forename: Kyounghwa
authors.initials: K
authors.lastname: Bae
authors.forename: Howard M
authors.initials: HM
authors.lastname: Sandler
mesh_terms: D000230:Adenocarcinoma / Q000473:pathology / Q000532:radiotherapy*; D000368:Aged; D014408:Biomarkers, Tumor / Q000097:blood; D015331:Cohort Studies; D018450:Disease Progression; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008875:Middle Aged; D009367:Neoplasm Staging; D011379:Prognosis; D017430:Prostate-Specific Antigen / Q000097:blood*; D011471:Prostatic Neoplasms / Q000473:pathology / Q000532:radiotherapy*; D020266:Radiotherapy, Conformal / Q000379:methods*; D012008:Recurrence; D012307:Risk Factors; D013997:Time Factors
chemical_list: D014408:Biomarkers, Tumor; D017430:Prostate-Specific Antigen