Journal article
Genomic Classifier Augments the Role of Pathological Features in Identifying Optimal Candidates for Adjuvant Radiation Therapy in Patients With Prostate Cancer: Development and Internal Validation of a Multivariable Prognostic Model
Journal of clinical oncology, Vol.35(18), pp.1982-1990
06/20/2017
DOI: 10.1200/JCO.2016.69.9918
PMCID: PMC6530898
PMID: 28350520
Abstract
Purpose Despite documented oncologic benefit, use of postoperative adjuvant radiotherapy (aRT) in patients with prostate cancer is still limited in the United States. We aimed to develop and internally validate a risk-stratification tool incorporating the Decipher score, along with routinely available clinicopathologic features, to identify patients who would benefit the most from aRT. Patient and Methods Our cohort included 512 patients with prostate cancer treated with radical prostatectomy at one of four US academic centers between 1990 and 2010. All patients had ≥ pT3a disease, positive surgical margins, and/or pathologic lymph node invasion. Multivariable Cox regression analysis tested the relationship between available predictors (including Decipher score) and clinical recurrence (CR), which were then used to develop a novel risk-stratification tool. Our study adhered to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines for development of prognostic models. Results Overall, 21.9% of patients received aRT. Median follow-up in censored patients was 8.3 years. The 10-year CR rate was 4.9% vs. 17.4% in patients treated with aRT versus initial observation ( P < .001). Pathologic T3b/T4 stage, Gleason score 8-10, lymph node invasion, and Decipher score > 0.6 were independent predictors of CR (all P < .01). The cumulative number of risk factors was 0, 1, 2, and 3 to 4 in 46.5%, 28.9%, 17.2%, and 7.4% of patients, respectively. aRT was associated with decreased CR rate in patients with two or more risk factors (10-year CR rate 10.1% in aRT v 42.1% in initial observation; P = .012), but not in those with fewer than two risk factors ( P = .18). Conclusion Using the new model to indicate aRT might reduce overtreatment, decrease unnecessary adverse effects, and reduce risk of CR in the subset of patients (approximately 25% of all patients with aggressive pathologic disease in our cohort) who benefit from this therapy.
Details
- Title: Subtitle
- Genomic Classifier Augments the Role of Pathological Features in Identifying Optimal Candidates for Adjuvant Radiation Therapy in Patients With Prostate Cancer: Development and Internal Validation of a Multivariable Prognostic Model
- Creators
- Deepansh Dalela - Thomas Jefferson UniversityMaría Santiago-Jiménez - Mayo Clinic in FloridaKasra Yousefi - Henry Ford Health SystemR Jeffrey Karnes - Henry Ford Health SystemAshley E Ross - Henry Ford Health SystemRobert B Den - Mayo Clinic in FloridaStephen J Freedland - Henry Ford Health SystemEdward M Schaeffer - Henry Ford Health SystemAdam P Dicker - Thomas Jefferson UniversityMani Menon - Henry Ford Health SystemAlberto Briganti - Thomas Jefferson UniversityElai Davicioni - Mayo Clinic in FloridaFiras Abdollah - Mayo Clinic in Florida
- Resource Type
- Journal article
- Publication Details
- Journal of clinical oncology, Vol.35(18), pp.1982-1990
- DOI
- 10.1200/JCO.2016.69.9918
- PMID
- 28350520
- PMCID
- PMC6530898
- NLM abbreviation
- J Clin Oncol
- ISSN
- 0732-183X
- eISSN
- 1527-7755
- Grant note
- U01 CA196390 / NCI NIH HHS
- Language
- English
- Date published
- 06/20/2017
- Academic Unit
- Urology
- Record Identifier
- 9984949456002771
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