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Early diagnosis of pathological invasiveness of pulmonary adenocarcinomasusing computed tomography (CT) imaging would alter the course of treatment ofadenocarcinomas and subsequently improve the prognosis. Most of the existingsystems use either conventional radiomics features or deep-learning featuresalone to predict the invasiveness. In this study, we explore the fusion of thetwo kinds of features and claim that radiomics features can be complementary todeep-learning features. An effective deep feature fusion network is proposed toexploit the complementarity between the two kinds of features, which improvesthe invasiveness prediction results. We collected a private dataset thatcontains lung CT scans of 676 patients categorized into four invasiveness typesfrom a collaborating hospital. Evaluations on this dataset demonstrate theeffectiveness of our proposal.

Invasiveness Prediction of Pulmonary Adenocarcinomas Using Deep Feature Fusion Networks