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A Graph Neural Network Approach for Predicting the Lung Carcinogenicity of Single Molecular Compounds
http://doi.org/10.5626/JOK.2025.52.6.482
Cancer is one of the major diseases causing millions of deaths worldwide every year, and lung cancer has been recorded as the leading cause of cancer-related deaths in Korea in 2022. Therefore, research on lung cancer-causing compounds is essential, and this study proposes and evaluates a novel approach to predict lung cancer-causing potential using graph neural networks to overcome the limitations of existing machine learning and deep learning methods. Based on SMILES(Simplified Molecular Input Line Entry System) information from the compound carcinogenicity databases CPDB, CCRIS, IRIS and T3DB, the structure and chemical properties of molecules were converted into graph data for training, and the proposed model showed superior prediction performance compared to other models. This demonstrates the potential of graph neural networks as an effective tool for lung cancer prediction and suggests that they can make important contributions to future cancer research and treatment development.
Automatic Segmentation of Lung Cancer in Chest CT Images through Capsule Network-based Dual-Window Ensemble Learning
Jumin Lee, Julip Jung, Helen Hong, Bong-Seog Kim
http://doi.org/10.5626/JOK.2021.48.8.905
It is difficult to accurately segment lung cancer in chest CT images when it has an irregular shape or nearby structures have a similar intensity as lung cancer. In this study, we proposed a dual window ensemble network that uses a capsule network to learn the relationship between lung cancer and nearby structures and additionally considers the mediastinal window image with the lung window image to distinguish lung cancer from the nearby structures. First, intensity and spacing normalization was performed on the input images of the lung window setting and mediastinal window setting. Second, two types of 2D capsule network were performed with the lung and mediastinal setting images. Third, the final segmentation mask was generated by ensemble the probability maps of the lung and mediastinal window images through average voting by reflecting the weight based on the characteristics of each image. The proposed method showed a Dice similarity coefficient(DSC) of 75.98% which was 0.53% higher than the method not considering the weight of each window setting. Furthermore, segmentation accuracy was improved even when lung cancer was surrounded by nearby structures.
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