Predictive models in external beam radiotherapy for clinically localized prostate cancer

Mack Roach, Fred Waldman, Alan Pollack

Research output: Contribution to journalArticlepeer-review

49 Scopus citations


Predictive models are being used increasingly in effort to allow physician and patient expectations to be aligned with outcomes that are based on available data. Most predictive models for men who receive external beam radiotherapy for clinically localized prostate cancer are based on Gleason score, clinical tumor classification, and prostate-specific antigen (PSA) levels. More sophisticated models also have been developed that incorporate treatment-related variables, such as the dose of radiation and the use of androgen-deprivation therapy. Most of the predictive models applied to prostate cancer were derived using PSA recurrence rates as the major endpoint, but clinical endpoints have been incorporated increasingly into predictive models. Biomarkers also are increasingly being added to predictive models in an effort to strengthen them. The Radiation Therapy Oncology Group (RTOG) has completed studies on a wide range of markers using tissue from 2 phase 3 trials (RTOG 8610 and 9202). To date, preliminary assessments of p53; DNA ploidy; p16/retinoblastoma 1 protein; Ki-67; mouse double-minute p53 binding protein homolog; Bcl-2/Bcl-2-associated X protein; cytosine, adenine, and guanine repeats; cyclooxygenase-2; signal transducer and activator of transcription 3; cytochrome P450 3A4; and protein kinase A have been completed. Although they are not ready for widespread, routine use, there are reasons to believe that future models will combine these markers with traditional pretreatment and treatment-related variables and will improve our ability to predict outcome and select the optimal treatment.

Original languageEnglish (US)
Pages (from-to)3112-3120
Number of pages9
Issue numberSUPPL 13
StatePublished - Jul 1 2009


  • Predictive models
  • Prostate cancer
  • Radiotherapy
  • Randomized trials

ASJC Scopus subject areas

  • Cancer Research
  • Oncology


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