ArXiv Preprint
The paper proposes a novel hybrid discovery Radiomics framework that
simultaneously integrates temporal and spatial features extracted from non-thin
chest Computed Tomography (CT) slices to predict Lung Adenocarcinoma (LUAC)
malignancy with minimum expert involvement. Lung cancer is the leading cause of
mortality from cancer worldwide and has various histologic types, among which
LUAC has recently been the most prevalent. LUACs are classified as
pre-invasive, minimally invasive, and invasive adenocarcinomas. Timely and
accurate knowledge of the lung nodules malignancy leads to a proper treatment
plan and reduces the risk of unnecessary or late surgeries. Currently, chest CT
scan is the primary imaging modality to assess and predict the invasiveness of
LUACs. However, the radiologists' analysis based on CT images is subjective and
suffers from a low accuracy compared to the ground truth pathological reviews
provided after surgical resections. The proposed hybrid framework, referred to
as the CAET-SWin, consists of two parallel paths: (i) The Convolutional
Auto-Encoder (CAE) Transformer path that extracts and captures informative
features related to inter-slice relations via a modified Transformer
architecture, and; (ii) The Shifted Window (SWin) Transformer path, which is a
hierarchical vision transformer that extracts nodules' related spatial features
from 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 proven
Sub-Solid Nodules (SSNs) demonstrate that the CAET-SWin significantly improves
reliability of the invasiveness prediction task while achieving an accuracy of
82.65%, sensitivity of 83.66%, and specificity of 81.66% using 10-fold
cross-validation.
Sadaf Khademi, Shahin Heidarian, Parnian Afshar, Farnoosh Naderkhani, Anastasia Oikonomou, Konstantinos Plataniotis, Arash Mohammadi
2022-10-27
