Figure 2. Combining clinical and molecular features enhance model predictive accuracy.
To test whether incorporating multi-source information (i.e. clinical, genomic, imaging data) to statistical learning would improve prediction accuracy of treatment response, the Concr model was trained using data from 743 breast cancer patients (across sub-types and treatments).
Results depict differential accuracy of the Concr risk model, trained using distinct data parcels (x-axis), as measured by a time-integrated Area Under the Curve (AUC) and c-index. The predictive accuracy improved stepwise by adding additional relevant data types, with the final AUC of 0.8 and c-index of 0.82.