ArticlePloS one2015
Evaluating the clinical impact of a genomic classifier in prostate cancer using individualized decision analysis.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02783950 (Genomics in Michigan Impacting Observation or Radiation), which is not on this map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Genomics in Michigan Impacting Observation or Radiation (G-MINOR)
Who cites it
7 citing papers in PubMed.
- Article
- Sociodemographic Features, Health Care Costs, and Treatment Implications of Genomic Classifier Testing for Localized Prostate Cancer in the United States.JCO precision oncology · 2025Article
- Tissue-Based Biomarkers for the Risk Stratification of Men With Clinically Localized Prostate Cancer.Frontiers in oncology · 2021Review
- Tissue-based biomarkers in prostate cancer.Expert review of precision medicine and drug development · 2017Article
- Efficacy of post-operative radiation in a prostatectomy cohort adjusted for clinical and genomic risk.Prostate cancer and prostatic diseases · 2016Article
- Prostate cancer radiomics and the promise of radiogenomics.Translational cancer research · 2016Article
- Incorporation of tissue-based genomic biomarkers into localized prostate cancer clinics.BMC medicine · 2016Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCurrently there is controversy surrounding the optimal way to treat patients with prostate cancer in the post-prostatectomy setting. Adjuvant therapies carry possible benefits of improved curative results, but there is uncertainty in which patients should receive adjuvant therapy. There are concerns about giving toxicity to a whole population for the benefit of only a subset. We hypothesized that making post-prostatectomy treatment decisions using genomics-based risk prediction estimates would improve cancer and quality of life outcomes.
methodsWe developed a state-transition model to simulate outcomes over a 10 year horizon for a cohort of post-prostatectomy patients. Outcomes included cancer progression rates at 5 and 10 years, overall survival, and quality-adjusted survival with reductions for treatment, side effects, and cancer stage. We compared outcomes using population-level versus individual-level risk of cancer progression, and for genomics-based care versus usual care treatment recommendations.
resultsCancer progression outcomes, expected life-years (LYs), and expected quality-adjusted life-years (QALYs) were significantly different when individual genomics-based cancer progression risk estimates were used in place of population-level risk estimates. Use of the genomic classifier to guide treatment decisions provided small, but statistically significant, improvements in model outcomes. We observed an additional 0.03 LYs and 0.07 QALYs, a 12% relative increase in the 5-year recurrence-free survival probability, and a 4% relative reduction in the 5-year probability of metastatic disease or death.
conclusionsThe use of genomics-based risk prediction to guide treatment decisions may improve outcomes for prostate cancer patients. This study offers a framework for individualized decision analysis, and can be extended to incorporate a wide range of personal attributes to enable delivery of patient-centered tools for informed decision-making.
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Registered trials
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