ArticleJournal of clinical medicine2019
Deep Convolutional Neural Network-Based Positron Emission Tomography Analysis Predicts Esophageal Cancer Outcome.
Article in Journal of clinical medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- AI in esophageal cancer: advances, barriers to clinical translation, and perspectives for digital health.Journal of translational medicine · 2026Review
- Deep Learning for Image Analysis in the Diagnosis and Management of Esophageal Cancer.Cancers · 2024Review
- Utilizing convolutional neural networks for discriminating cancer and stromal cells in three-dimensional cell culture images with nuclei counterstain.Journal of biomedical optics · 2024Article
- Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET).Nuklearmedizin. Nuclear medicine · 2023Review
- Assessment of hyperspectral imaging and CycleGAN-simulated narrowband techniques to detect early esophageal cancer.Scientific reports · 2023Article
- Systematic meta-analysis of computer-aided detection to detect early esophageal cancer using hyperspectral imaging.Biomedical optics express · 2023Article
- Classification of head and neck cancer from PET images using convolutional neural networks.Scientific reports · 2023Article
- The Oesophageal Cancer Multidisciplinary Team: Can Machine Learning Assist Decision-Making?Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract · 2023Review
- Article
- Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview.World journal of gastroenterology · 2022Review
- Evaluation of the Effect of Comprehensive Nursing Interventions on Plaque Control in Patients with Periodontal Disease in the Context of Artificial Intelligence.Journal of healthcare engineering · 2022Article
- Review
- Predicting aggressive histopathological features in esophageal cancer with positron emission tomography using a deep convolutional neural network.Annals of translational medicine · 2021Article
- Lung Nodule Sizes Are Encoded When Scaling CT Image for CNN's.Tomography (Ann Arbor, Mich.) · 2020Article
- Automatic classification of esophageal lesions in endoscopic images using a convolutional neural network.Annals of translational medicine · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In esophageal cancer, few prediction tools can be confidently used in current clinical practice. We developed a deep convolutional neural network (CNN) with 798 positron emission tomography (PET) scans of esophageal squamous cell carcinoma and 309 PET scans of stage I lung cancer. In the first stage, we pretrained a 3D-CNN with all PET scans for a task to classify the scans into esophageal cancer or lung cancer. Overall, 548 of 798 PET scans of esophageal cancer patients were included in the second stage with an aim to classify patients who expired within or survived more than one year after diagnosis. The area under the receiver operating characteristic curve (AUC) was used to evaluate model performance. In the pretrain model, the deep CNN attained an AUC of 0.738 in identifying patients who expired within one year after diagnosis. In the survival analysis, patients who were predicted to be expired but were alive at one year after diagnosis had a 5-year survival rate of 32.6%, which was significantly worse than the 5-year survival rate of the patients who were predicted to survive and were alive at one year after diagnosis (50.5%,
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