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Radiomics aims to extract and analyze large numbers of quantitative featuresfrom medical images and is highly promising in staging, diagnosing, andpredicting outcomes of cancer treatments. Nevertheless, several challenges needto be addressed to construct an optimal radiomics predictive model. First, thepredictive performance of the model may be reduced when features extracted froman individual imaging modality are blindly combined into a single predictivemodel. Second, because many different types of classifiers are available toconstruct a predictive model, selecting an optimal classifier for a particularapplication is still challenging. In this work, we developed multi-modality andmulti-classifier radiomics predictive models that address the aforementionedissues in currently available models. Specifically, a new reliable classifierfusion strategy was proposed to optimally combine output from differentmodalities and classifiers. In this strategy, modality-specific classifierswere first trained, and an analytic evidential reasoning (ER) rule wasdeveloped to fuse the output score from each modality to construct an optimalpredictive model. One public data set and two clinical case studies wereperformed to validate model performance. The experimental results indicatedthat the proposed ER rule based radiomics models outperformed the traditionalmodels that rely on a single classifier or simply use combined features fromdifferent modalities.

Constructing multi-modality and multi-classifier radiomics predictive models through reliable classifier fusion