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A multi-kernel and multi-scale learning based deep ensemble model for predicting recurrence of non-small cell lung cancer
- Title
- A multi-kernel and multi-scale learning based deep ensemble model for predicting recurrence of non-small cell lung cancer
- Authors
- Kim, Gihyeon; Park, Young Mi; Yoon, Hyun Jung; Choi, Jang-Hwan
- Ewha Authors
- 박영미; 최장환
- SCOPUS Author ID
- 박영미; 최장환
- Issue Date
- 2023
- Journal Title
- PEERJ COMPUTER SCIENCE
- ISSN
- 2376-5992
- Citation
- PEERJ COMPUTER SCIENCE vol. 9
- Keywords
- Multi-scale network; Multi-kernel network; Ensemble model; Non-small cell lung cancer; Lung cancer recurrence; Artificial neural network; Deep learning
- Publisher
- PEERJ INC
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
- Abstract
- Predicting recurrence in patients with non-small cell lung cancer (NSCLC) before treatment is vital for guiding personalized medicine. Deep learning techniques have revolutionized the application of cancer informatics, including lung cancer timeto-event prediction. Most existing convolutional neural network (CNN) models are based on a single two-dimensional (2D) computational tomography (CT) image or three-dimensional (3D) CT volume. However, studies have shown that using multiscale input and fusing multiple networks provide promising performance. This study proposes a deep learning-based ensemble network for recurrence prediction using a dataset of 530 patients with NSCLC. This network assembles 2D CNN models of various input slices, scales, and convolutional kernels, using a deep learning-based feature fusion model as an ensemble strategy. The proposed framework is uniquely designed to benefit from (i) multiple 2D in-plane slices to provide more information than a single central slice, (ii) multi-scale networks and multi-kernel networks to capture the local and peritumoral features, (iii) ensemble design to integrate features from various inputs and model architectures for final prediction. The ensemble of five 2D-CNN models, three slices, and two multi-kernel networks, using 5 x 5 and 6 x 6 convolutional kernels, achieved the best performance with an accuracy of 69.62%, area under the curve (AUC) of 72.5%, F1 score of 70.12%, and recall of 70.81%. Furthermore, the proposed method achieved competitive results compared with the 2D and 3D-CNN models for cancer outcome prediction in the benchmark studies. Our model is also a potential adjuvant treatment tool for identifying NSCLC patients with a high risk of recurrence.
- DOI
- 10.7717/peerj-cs.1311|http://dx.doi.org/10.7717/peerj-cs.1311
- Appears in Collections:
- 의과대학 > 의학과 > Journal papers
- Files in This Item:
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