  Radiomics aims to extract and analyze large numbers of quantitative features
from medical images and is highly promising in staging, diagnosing, and
predicting outcomes of cancer treatments. Nevertheless, several challenges need
to be addressed to construct an optimal radiomics predictive model. First, the
predictive performance of the model may be reduced when features extracted from
an individual imaging modality are blindly combined into a single predictive
model. Second, because many different types of classifiers are available to
construct a predictive model, selecting an optimal classifier for a particular
application is still challenging. In this work, we developed multi-modality and
multi-classifier radiomics predictive models that address the aforementioned
issues in currently available models. Specifically, a new reliable classifier
fusion strategy was proposed to optimally combine output from different
modalities and classifiers. In this strategy, modality-specific classifiers
were first trained, and an analytic evidential reasoning (ER) rule was
developed to fuse the output score from each modality to construct an optimal
predictive model. One public data set and two clinical case studies were
performed to validate model performance. The experimental results indicated
that the proposed ER rule based radiomics models outperformed the traditional
models that rely on a single classifier or simply use combined features from
different modalities.
