CAE-Transformer: Transformer-based Model to Predict Invasiveness of Lung Adenocarcinoma Subsolid Nodules from Non-thin Section 3D CT Scans

• URL: http://arxiv.org/abs/2110.08721v1
• Date: Sun, 17 Oct 2021 04:37:24 GMT
• Title: CAE-Transformer: Transformer-based Model to Predict Invasiveness of Lung Adenocarcinoma Subsolid Nodules from Non-thin Section 3D CT Scans
• Authors: Shahin Heidarian, Parnian Afshar, Anastasia Oikonomou, Konstantinos N. Plataniotis, Arash Mohammadi
• Abstract summary: Lung Adenocarcinoma (LAUC) has recently been the most prevalent. Timely and accurate knowledge of the invasiveness of lung nodules leads to a proper treatment plan and reduces the risk of unnecessary or late surgeries. The primary imaging modality to assess and predict the invasiveness of LAUCs is the chest CT. In this paper, a predictive transformer-based framework, referred to as the "CAE-Transformer", is developed to classify LAUCs.
• Score: 36.093580055848186
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Lung cancer is the leading cause of mortality from cancer worldwide and has various histologic types, among which Lung Adenocarcinoma (LAUC) has recently been the most prevalent. Lung adenocarcinomas are classified as pre-invasive, minimally invasive, and invasive adenocarcinomas. Timely and accurate knowledge of the invasiveness of lung nodules leads to a proper treatment plan and reduces the risk of unnecessary or late surgeries. Currently, the primary imaging modality to assess and predict the invasiveness of LAUCs is the chest CT. The results based on CT images, however, are subjective and suffer from a low accuracy compared to the ground truth pathological reviews provided after surgical resections. In this paper, a predictive transformer-based framework, referred to as the "CAE-Transformer", is developed to classify LAUCs. The CAE-Transformer utilizes a Convolutional Auto-Encoder (CAE) to automatically extract informative features from CT slices, which are then fed to a modified transformer model to capture global inter-slice relations. Experimental results on our in-house dataset of 114 pathologically proven Sub-Solid Nodules (SSNs) demonstrate the superiority of the CAE-Transformer over the histogram/radiomics-based models and its deep learning-based 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.

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