  Early diagnosis of pathological invasiveness of pulmonary adenocarcinomas
using computed tomography (CT) imaging would alter the course of treatment of
adenocarcinomas and subsequently improve the prognosis. Most of the existing
systems use either conventional radiomics features or deep-learning features
alone to predict the invasiveness. In this study, we explore the fusion of the
two kinds of features and claim that radiomics features can be complementary to
deep-learning features. An effective deep feature fusion network is proposed to
exploit the complementarity between the two kinds of features, which improves
the invasiveness prediction results. We collected a private dataset that
contains lung CT scans of 676 patients categorized into four invasiveness types
from a collaborating hospital. Evaluations on this dataset demonstrate the
effectiveness of our proposal.
