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The paper proposes a novel hybrid discovery Radiomics framework thatsimultaneously integrates temporal and spatial features extracted from non-thinchest Computed Tomography (CT) slices to predict Lung Adenocarcinoma (LUAC)malignancy with minimum expert involvement. Lung cancer is the leading cause ofmortality from cancer worldwide and has various histologic types, among whichLUAC has recently been the most prevalent. LUACs are classified aspre-invasive, minimally invasive, and invasive adenocarcinomas. Timely andaccurate knowledge of the lung nodules malignancy leads to a proper treatmentplan and reduces the risk of unnecessary or late surgeries. Currently, chest CTscan is the primary imaging modality to assess and predict the invasiveness ofLUACs. However, the radiologists' analysis based on CT images is subjective andsuffers from a low accuracy compared to the ground truth pathological reviewsprovided after surgical resections. The proposed hybrid framework, referred toas the CAET-SWin, consists of two parallel paths: (i) The ConvolutionalAuto-Encoder (CAE) Transformer path that extracts and captures informativefeatures related to inter-slice relations via a modified Transformerarchitecture, and; (ii) The Shifted Window (SWin) Transformer path, which is ahierarchical vision transformer that extracts nodules' related spatial featuresfrom a volumetric CT scan. Extracted temporal (from the CAET-path) and spatial(from the Swin path) are then fused through a fusion path to classify LUACs.Experimental results on our in-house dataset of 114 pathologically provenSub-Solid Nodules (SSNs) demonstrate that the CAET-SWin significantly improvesreliability of the invasiveness prediction task while achieving an accuracy of82.65%, sensitivity of 83.66%, and specificity of 81.66% using 10-foldcross-validation.

Spatio-Temporal Hybrid Fusion of CAE and SWIn Transformers for Lung Cancer Malignancy Prediction