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The most common type of lung cancer, lung adenocarcinoma (LUAD), has beenincreasingly detected since the advent of low-dose computed tomographyscreening technology. In clinical practice, pre-invasive LUAD (Pre-IAs) shouldonly require regular follow-up care, while invasive LUAD (IAs) should receiveimmediate treatment with appropriate lung cancer resection, based on the cancersubtype. However, prior research on diagnosing LUAD has mainly focused onclassifying Pre-IAs/IAs, as techniques for distinguishing different subtypes ofIAs have been lacking. In this study, we proposed a multi-head attentionalfeature fusion (MHA-FF) model for not only distinguishing IAs from Pre-IAs, butalso for distinguishing the different subtypes of IAs. To predict the subtypeof each nodule accurately, we leveraged both radiomics and deep featuresextracted from computed tomography images. Furthermore, those features wereaggregated through an adaptive fusion module that can learn attention-baseddiscriminative features. The utility of our proposed method is demonstratedhere by means of real-world data collected from a multi-center cohort.

Adaptive Fusion of Radiomics and Deep Features for Lung Adenocarcinoma Subtype Recognition