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Genomic classifier to augment 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

Authors :
Firas Abdollah
Deepansh Dalela
Maria Santiago-Jimenez
Kasra Yousefi
Jeffrey Karnes
Ashley Ross
Robert B. Den
Stephen J. Freedland
Edward M. Schaeffer
Adam Dicker
Alberto Briganti
Elai Davicioni
Mani Menon
Source :
Journal of Clinical Oncology. 35:142-142
Publication Year :
2017
Publisher :
American Society of Clinical Oncology (ASCO), 2017.

Abstract

142 Background: Despite documented oncological benefit, postoperative adjuvant radiotherapy (aRT) utilization in prostate cancer (PCa) patients is still limited in the US. 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. Methods: Our cohort included a total of 512 PCa patients treated with RP at one of four US academic centers between 1990-2010. All patients had ≥ pT3a disease, positive margins, and/or pathologic lymph node invasion (LNI). Multivariable Cox regression analysis (MVA) 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 TRIPOD guidelines for development of prognostic models. Results: Overall, 21.9% 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 vs. initial observation (p < 0.001). Pathological T3b/T4 stage, Gleason score 8-10, LNI and Decipher score > 0.6 were independent predictors of CR (all p < 0.01) Cumulative number of risk factors was 0, 1, 2, and 3-4 in respectively 46.5, 28.9, 17.2, and 7.4% of patients. Adjuvant RT was associated with decreased CR rate in patients with ≥ 2 risk factors (10-year CR rate 10.1% in aRT vs. 42.1% in initial observation, p = 0.008), but not in those with < 2 risk factors (p = 0.23). Conclusions: Utilizing the novel model to indicate aRT might reduce overtreatment, decrease unnecessary side effects, and reduce risk of CR in the subset of patients (~25% of all patients with aggressive pathological disease) who really benefit from this therapy.

Subjects

Subjects :
Cancer Research
Oncology

Details

ISSN :
15277755 and 0732183X
Volume :
35
Database :
OpenAIRE
Journal :
Journal of Clinical Oncology
Accession number :
edsair.doi...........93da03b49f34f364cc5aff9a6078e01a
Full Text :
https://doi.org/10.1200/jco.2017.35.6_suppl.142