Robust Cancer Treatment Outcome Prediction Dealing with Small-Sized and Imbalanced Data from FDG-PET Images

Accurately predicting the outcome of cancer therapy is valuable for tailoring and adapting treatment planning. To this end, features extracted from multi-sources of information (e.g., radiomics and clinical characteristics) are potentially profitable. While it is of great interest to select the most informative features from all available ones, small-sized and imbalanced dataset, as often encountered in the medical domain, is a crucial challenge hindering reliable and stable subset selection. We propose a prediction system primarily using radiomic features extracted from FDG-PET images. It incorporates a feature selection method based on Dempster-Shafer theory, a powerful tool for modeling and reasoning with uncertain and/or imprecise information. Utilizing a data rebalancing procedure and specified prior knowledge to enhance the reliability and robustness of selected feature subsets, the proposed method aims to reduce the imprecision and overlaps between different classes in the selected feature subspace, thus finally improving the prediction accuracy. It has been evaluated by two clinical datasets, showing good performance.