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Deep Learning Analysis Using (18)F-FDG PET/CT to Predict Occult Lymph Node Metastasis in Patients With Clinical N0 Lung Adenocarcinoma
INTRODUCTION: The aim of this work was to determine the feasibility of using a deep learning approach to predict occult lymph node metastasis (OLM) based on preoperative FDG-PET/CT images in patients with clinical node-negative (cN0) lung adenocarcinoma. MATERIALS AND METHODS: Dataset 1 (for trainin...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9301998/ https://www.ncbi.nlm.nih.gov/pubmed/35875089 http://dx.doi.org/10.3389/fonc.2022.915871 |
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author | Ouyang, Ming-li Zheng, Rui-xuan Wang, Yi-ran Zuo, Zi-yi Gu, Liu-dan Tian, Yu-qian Wei, Yu-guo Huang, Xiao-ying Tang, Kun Wang, Liang-xing |
author_facet | Ouyang, Ming-li Zheng, Rui-xuan Wang, Yi-ran Zuo, Zi-yi Gu, Liu-dan Tian, Yu-qian Wei, Yu-guo Huang, Xiao-ying Tang, Kun Wang, Liang-xing |
author_sort | Ouyang, Ming-li |
collection | PubMed |
description | INTRODUCTION: The aim of this work was to determine the feasibility of using a deep learning approach to predict occult lymph node metastasis (OLM) based on preoperative FDG-PET/CT images in patients with clinical node-negative (cN0) lung adenocarcinoma. MATERIALS AND METHODS: Dataset 1 (for training and internal validation) included 376 consecutive patients with cN0 lung adenocarcinoma from our hospital between May 2012 and May 2021. Dataset 2 (for prospective test) used 58 consecutive patients with cN0 lung adenocarcinoma from June 2021 to February 2022 at the same center. Three deep learning models: PET alone, CT alone, and combined model, were developed for the prediction of OLM. The performance of the models was evaluated on internal validation and prospective test in terms of accuracy, sensitivity, specificity, and areas under the receiver operating characteristic curve (AUCs). RESULTS: The combined model incorporating PET and CT showed the best performance, achieved an AUC of 0.81 [95% confidence interval (CI): 0.61, 1.00] in the prediction of OLM in internal validation set (n = 60) and an AUC of 0.87 (95% CI: 0.75, 0.99) in the prospective test set (n = 58). The model achieved 87.50% sensitivity, 80.00% specificity, and 81.00% accuracy in the internal validation set and achieved 75.00% sensitivity, 88.46% specificity, and 86.60% accuracy in the prospective test set. CONCLUSION: This study presented a deep learning approach to enable the prediction of occult nodal involvement based on the PET/CT images before surgery in cN0 lung adenocarcinoma, which would help clinicians select patients who would be suitable for sublobar resection. |
format | Online Article Text |
id | pubmed-9301998 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93019982022-07-22 Deep Learning Analysis Using (18)F-FDG PET/CT to Predict Occult Lymph Node Metastasis in Patients With Clinical N0 Lung Adenocarcinoma Ouyang, Ming-li Zheng, Rui-xuan Wang, Yi-ran Zuo, Zi-yi Gu, Liu-dan Tian, Yu-qian Wei, Yu-guo Huang, Xiao-ying Tang, Kun Wang, Liang-xing Front Oncol Oncology INTRODUCTION: The aim of this work was to determine the feasibility of using a deep learning approach to predict occult lymph node metastasis (OLM) based on preoperative FDG-PET/CT images in patients with clinical node-negative (cN0) lung adenocarcinoma. MATERIALS AND METHODS: Dataset 1 (for training and internal validation) included 376 consecutive patients with cN0 lung adenocarcinoma from our hospital between May 2012 and May 2021. Dataset 2 (for prospective test) used 58 consecutive patients with cN0 lung adenocarcinoma from June 2021 to February 2022 at the same center. Three deep learning models: PET alone, CT alone, and combined model, were developed for the prediction of OLM. The performance of the models was evaluated on internal validation and prospective test in terms of accuracy, sensitivity, specificity, and areas under the receiver operating characteristic curve (AUCs). RESULTS: The combined model incorporating PET and CT showed the best performance, achieved an AUC of 0.81 [95% confidence interval (CI): 0.61, 1.00] in the prediction of OLM in internal validation set (n = 60) and an AUC of 0.87 (95% CI: 0.75, 0.99) in the prospective test set (n = 58). The model achieved 87.50% sensitivity, 80.00% specificity, and 81.00% accuracy in the internal validation set and achieved 75.00% sensitivity, 88.46% specificity, and 86.60% accuracy in the prospective test set. CONCLUSION: This study presented a deep learning approach to enable the prediction of occult nodal involvement based on the PET/CT images before surgery in cN0 lung adenocarcinoma, which would help clinicians select patients who would be suitable for sublobar resection. Frontiers Media S.A. 2022-07-07 /pmc/articles/PMC9301998/ /pubmed/35875089 http://dx.doi.org/10.3389/fonc.2022.915871 Text en Copyright © 2022 Ouyang, Zheng, Wang, Zuo, Gu, Tian, Wei, Huang, Tang and Wang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Ouyang, Ming-li Zheng, Rui-xuan Wang, Yi-ran Zuo, Zi-yi Gu, Liu-dan Tian, Yu-qian Wei, Yu-guo Huang, Xiao-ying Tang, Kun Wang, Liang-xing Deep Learning Analysis Using (18)F-FDG PET/CT to Predict Occult Lymph Node Metastasis in Patients With Clinical N0 Lung Adenocarcinoma |
title | Deep Learning Analysis Using (18)F-FDG PET/CT to Predict Occult Lymph Node Metastasis in Patients With Clinical N0 Lung Adenocarcinoma |
title_full | Deep Learning Analysis Using (18)F-FDG PET/CT to Predict Occult Lymph Node Metastasis in Patients With Clinical N0 Lung Adenocarcinoma |
title_fullStr | Deep Learning Analysis Using (18)F-FDG PET/CT to Predict Occult Lymph Node Metastasis in Patients With Clinical N0 Lung Adenocarcinoma |
title_full_unstemmed | Deep Learning Analysis Using (18)F-FDG PET/CT to Predict Occult Lymph Node Metastasis in Patients With Clinical N0 Lung Adenocarcinoma |
title_short | Deep Learning Analysis Using (18)F-FDG PET/CT to Predict Occult Lymph Node Metastasis in Patients With Clinical N0 Lung Adenocarcinoma |
title_sort | deep learning analysis using (18)f-fdg pet/ct to predict occult lymph node metastasis in patients with clinical n0 lung adenocarcinoma |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9301998/ https://www.ncbi.nlm.nih.gov/pubmed/35875089 http://dx.doi.org/10.3389/fonc.2022.915871 |
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