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.