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

10/17/2021
by   Shahin Heidarian, et al.
0

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 and AUC of 0.913, using a 10-fold cross-validation.

READ FULL TEXT
research
10/27/2022

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

The paper proposes a novel hybrid discovery Radiomics framework that sim...
research
09/01/2015

Discovery Radiomics for Pathologically-Proven Computed Tomography Lung Cancer Prediction

Lung cancer is the leading cause for cancer related deaths. As such, the...
research
03/05/2020

A deep learning-facilitated radiomics solution for the prediction of lung lesion shrinkage in non-small cell lung cancer trials

Herein we propose a deep learning-based approach for the prediction of l...
research
02/20/2019

Knowledge-based Analysis for Mortality Prediction from CT Images

Recent studies have highlighted the high correlation between cardiovascu...
research
05/30/2022

Zero-Shot and Few-Shot Learning for Lung Cancer Multi-Label Classification using Vision Transformer

Lung cancer is the leading cause of cancer-related death worldwide. Lung...
research
08/27/2023

High-risk Factor Prediction in Lung Cancer Using Thin CT Scans: An Attention-Enhanced Graph Convolutional Network Approach

Lung cancer, particularly in its advanced stages, remains a leading caus...
research
10/15/2021

Prediction of Lung CT Scores of Systemic Sclerosis by Cascaded Regression Neural Networks

Visually scoring lung involvement in systemic sclerosis from CT scans pl...

Please sign up or login with your details

Forgot password? Click here to reset