  Genetic studies have identified associations between gene mutations and clear
cell renal cell carcinoma (ccRCC). Because the complete gene mutational
landscape cannot be characterized through biopsy and sequencing assays for each
patient, non-invasive tools are needed to determine the mutation status for
tumors. Radiogenomics may be an attractive alternative tool to identify disease
genomics by analyzing amounts of features extracted from medical images. Most
current radiogenomics predictive models are built based on a single classifier
and trained through a single objective. However, since many classifiers are
available, selecting an optimal model is difficult. On the other hand, a single
objective may not be a good measure to guide model training. We proposed a new
multi-classifier multi-objective (MCMO) radiogenomics predictive model. To
obtain more reliable prediction results, similarity-based sensitivity and
specificity were defined and considered as the two objective functions
simultaneously during training. To take advantage of different classifiers, the
evidential reasoning (ER) approach was used for fusing the output of each
classifier. Additionally, a new similarity-based multi-objective optimization
algorithm (SMO) was developed for training the MCMO to predict ccRCC related
gene mutations (VHL, PBRM1 and BAP1). Preliminary results revealed an
association between some quantitative computed tomography (CT) features and
underlying mutations. Using the proposed MCMO model, we achieved a predictive
area under the receiver operating characteristic curve (AUC) over 0.86 for VHL,
PBRM1, and BAP1 genes with balanced sensitivity and specificity. Furthermore,
MCMO outperformed all the individual classifiers and yielded more reliable
results than other optimization algorithms and commonly used fusion strategies.
