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Deep Convolutional Neural Network-Based Positron Emission Tomography Analysis Predicts Esophageal Cancer Outcome

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...

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Autores principales: Yang, Cheng-Kun, Yeh, Joe Chao-Yuan, Yu, Wei-Hsiang, Chien, Ling-I., Lin, Ko-Han, Huang, Wen-Sheng, Hsu, Po-Kuei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6616908/
https://www.ncbi.nlm.nih.gov/pubmed/31200519
http://dx.doi.org/10.3390/jcm8060844
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author Yang, Cheng-Kun
Yeh, Joe Chao-Yuan
Yu, Wei-Hsiang
Chien, Ling-I.
Lin, Ko-Han
Huang, Wen-Sheng
Hsu, Po-Kuei
author_facet Yang, Cheng-Kun
Yeh, Joe Chao-Yuan
Yu, Wei-Hsiang
Chien, Ling-I.
Lin, Ko-Han
Huang, Wen-Sheng
Hsu, Po-Kuei
author_sort Yang, Cheng-Kun
collection PubMed
description 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%, p < 0.001). These results suggest that the prediction model could identify tumors with more aggressive behavior. In the multivariable analysis, the prediction result remained an independent prognostic factor (hazard ratio: 2.830; 95% confidence interval: 2.252–3.555, p < 0.001). We conclude that a 3D-CNN can be trained with PET image datasets to predict esophageal cancer outcome with acceptable accuracy.
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spelling pubmed-66169082019-07-18 Deep Convolutional Neural Network-Based Positron Emission Tomography Analysis Predicts Esophageal Cancer Outcome Yang, Cheng-Kun Yeh, Joe Chao-Yuan Yu, Wei-Hsiang Chien, Ling-I. Lin, Ko-Han Huang, Wen-Sheng Hsu, Po-Kuei J Clin Med Article 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%, p < 0.001). These results suggest that the prediction model could identify tumors with more aggressive behavior. In the multivariable analysis, the prediction result remained an independent prognostic factor (hazard ratio: 2.830; 95% confidence interval: 2.252–3.555, p < 0.001). We conclude that a 3D-CNN can be trained with PET image datasets to predict esophageal cancer outcome with acceptable accuracy. MDPI 2019-06-13 /pmc/articles/PMC6616908/ /pubmed/31200519 http://dx.doi.org/10.3390/jcm8060844 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yang, Cheng-Kun
Yeh, Joe Chao-Yuan
Yu, Wei-Hsiang
Chien, Ling-I.
Lin, Ko-Han
Huang, Wen-Sheng
Hsu, Po-Kuei
Deep Convolutional Neural Network-Based Positron Emission Tomography Analysis Predicts Esophageal Cancer Outcome
title Deep Convolutional Neural Network-Based Positron Emission Tomography Analysis Predicts Esophageal Cancer Outcome
title_full Deep Convolutional Neural Network-Based Positron Emission Tomography Analysis Predicts Esophageal Cancer Outcome
title_fullStr Deep Convolutional Neural Network-Based Positron Emission Tomography Analysis Predicts Esophageal Cancer Outcome
title_full_unstemmed Deep Convolutional Neural Network-Based Positron Emission Tomography Analysis Predicts Esophageal Cancer Outcome
title_short Deep Convolutional Neural Network-Based Positron Emission Tomography Analysis Predicts Esophageal Cancer Outcome
title_sort deep convolutional neural network-based positron emission tomography analysis predicts esophageal cancer outcome
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6616908/
https://www.ncbi.nlm.nih.gov/pubmed/31200519
http://dx.doi.org/10.3390/jcm8060844
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