A predictive model of overall survival in patients with metastatic

Metastatic castration resistant prostate cancer (mCRPC) is one of the most common cancers with a poor prognosis. To improve prognostic models of mCRPC, the Dialogue for Reverse Engineering Assessments and Methods (DREAM) Consortium organized a crowdsourced competition known as the Prostate Cancer DREAM Challenge. In the competition, data from four phase III clinical trials were utilized. A total of 1600 patients’ clinical information across three of the trials was used to generate prognostic models, whereas one of the datasets (313 patients) was held out for blinded validation. As a performance baseline, a model presented in a recent study (so called Halabi model) was used to assess improvements of the new models. This paper presents the model developed by the team TYTDreamChallenge to predict survival risk scores for mCRPC patients at 12, 18, 24 and 30-months after trial enrollment based on clinical features of each patient, as well as an improvement of the model developed after the challenge. The TYTDreamChallenge model performed similarly as the gold-standard Halabi model, whereas the post-challenge model showed markedly improved performance. Accordingly, a main observation in this challenge was that the definition of the clinical features used plays a major role and replacing our original larger set of features with a small subset for training increased the performance in terms of integrated area under the ROC curve from 0.748 to 0.779. This article is included in the DREAM Challenges gateway. 1,2* 1,2* 3 3,4 4