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https://hdl.handle.net/1822/59520
Título: | A DREAM challenge to build prediction models for short-term discontinuation of docetaxel in metastatic castration-resistant prostate cancer |
Autor(es): | Prostate Cancer DREAM Challenge Community Correia, Sara Freitas, Ana Alão Rocha, Miguel Vieira, Vítor |
Data: | 2017 |
Editora: | American Society of Clinical Oncology |
Revista: | JCO Clinical Cancer Informatics |
Citação: | Seyednasrollah, Fatemeh; Koestler, Devin C.; Wang, Tao; Stephen R. Piccolo; Roberto Vega; Russell Greiner; Christiane Fuchs; Eyal Gofer; Luke Kumar; Russell D. Wolfinger; Kimberly Kanigel Winner; Chris Bare; Elias Chaibub Neto; Thomas Yu; Liji Shen; Kald Abdallah; Thea Norman; Gustavo Stolovitzky; Howard R. Soule; Christopher J. Sweeney; Charles J. Ryan; Howard I. Scher; Oliver Sartor; Laura L. Elo; Fang Liz Zhou; Justin Guinney; James C. Costello; Prostate Cancer DREAM Challenge Community; Correia, Sara; Freitas, Ana Alão; Rocha, Miguel; Vieira, Vítor, A DREAM challenge to build prediction models for short-term discontinuation of docetaxel in metastatic castration-resistant prostate cancer. JCO Clinical Cancer Informatics, 2017 |
Resumo(s): | Purpose Docetaxel has a demonstrated survival benefit for patients with metastatic castration-resistant prostate cancer (mCRPC); however, 10% to 20% of patients discontinue docetaxel prematurely because of toxicity-induced adverse events, and the management of risk factors for toxicity remains a challenge. Patients and Methods The comparator arms of four phase III clinical trials in first-line mCRPC were collected, annotated, and compiled, with a total of 2,070 patients. Early discontinuation was defined as treatment stoppage within 3 months as a result of adverse treatment effects; 10% of patients discontinued treatment. We designed an open-data, crowd-sourced DREAM Challenge for developing models with which to predict early discontinuation of docetaxel treatment. Clinical features for all four trials and outcomes for three of the four trials were made publicly available, with the outcomes of the fourth trial held back for unbiased model evaluation. Challenge participants from around the world trained models and submitted their predictions. Area under the precision-recall curve was the primary metric used for performance assessment. Results In total, 34 separate teams submitted predictions. Seven models with statistically similar area under precision-recall curves (Bayes factor 3) outperformed all other models. A postchallenge analysis of risk prediction using these seven models revealed three patient subgroups: high risk, low risk, or discordant risk. Early discontinuation events were two times higher in the high-risk subgroup compared with the low-risk subgroup. Simulation studies demonstrated that use of patient discontinuation prediction models could reduce patient enrollment in clinical trials without the loss of statistical power. Conclusion This work represents a successful collaboration between 34 international teams that leveraged open clinical trial data. Our results demonstrate that routinely collected clinical features can be used to identify patients with mCRPC who are likely to discontinue treatment because of adverse events and establishes a robust benchmark with implications for clinical trial design. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/59520 |
DOI: | 10.1200/CCI.17.00018 |
ISSN: | 2473-4276 |
e-ISSN: | 2473-4276 |
Versão da editora: | http://ascopubs.org/journal/cci |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
Aparece nas coleções: | CEB - Publicações em Revistas/Séries Internacionais / Publications in International Journals/Series |
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document_46924_1.pdf Acesso restrito! | 1,06 MB | Adobe PDF | Ver/Abrir |