  Lung cancer is the leading cause of mortality from cancer worldwide and has
various histologic types, among which Lung Adenocarcinoma (LUAC) has recently
been the most prevalent one. The current approach to determine the invasiveness
of LUACs is surgical resection, which is not a viable solution to fight lung
cancer in a timely fashion. An alternative approach is to analyze chest
Computed Tomography (CT) scans. The radiologists' analysis based on CT images,
however, is subjective and might result in a low accuracy. In this paper, a
transformer-based framework, referred to as the "CAE-Transformer", is developed
to efficiently classify LUACs using whole CT images instead of finely annotated
nodules. The proposed CAE-Transformer can achieve high accuracy over a small
dataset and requires minor supervision from radiologists. The CAE Transformer
utilizes an encoder to automatically extract informative features from CT
slices, which are then fed to a modified transformer to capture global
inter-slice relations and provide classification labels. Experimental results
on our in-house dataset of 114 pathologically proven Sub-Solid Nodules (SSNs)
demonstrate the superiority of the CAE-Transformer over its counterparts,
achieving an accuracy of 87.73%, sensitivity of 88.67%, specificity of 86.33%,
and AUC of 0.913, using a 10-fold cross-validation.
