Adaptive Fusion of Radiomics and Deep Features for Lung Adenocarcinoma Subtype Recognition
This work addresses a critical need in clinical practice for precise subtype recognition in lung cancer, enabling better treatment decisions, though it is incremental as it builds on existing feature fusion methods.
The study tackled the problem of distinguishing invasive lung adenocarcinoma subtypes from pre-invasive ones using CT images, achieving accurate classification by adaptively fusing radiomics and deep features with a multi-head attentional model.
The most common type of lung cancer, lung adenocarcinoma (LUAD), has been increasingly detected since the advent of low-dose computed tomography screening technology. In clinical practice, pre-invasive LUAD (Pre-IAs) should only require regular follow-up care, while invasive LUAD (IAs) should receive immediate treatment with appropriate lung cancer resection, based on the cancer subtype. However, prior research on diagnosing LUAD has mainly focused on classifying Pre-IAs/IAs, as techniques for distinguishing different subtypes of IAs have been lacking. In this study, we proposed a multi-head attentional feature fusion (MHA-FF) model for not only distinguishing IAs from Pre-IAs, but also for distinguishing the different subtypes of IAs. To predict the subtype of each nodule accurately, we leveraged both radiomics and deep features extracted from computed tomography images. Furthermore, those features were aggregated through an adaptive fusion module that can learn attention-based discriminative features. The utility of our proposed method is demonstrated here by means of real-world data collected from a multi-center cohort.