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Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case−control study

OBJECTIVE: The purpose of this study is to establish model for assessing inert nodules predicting nodule volume-doubling. METHODS: A total of 201 patients with T1 lung adenocarcinoma were analysed retrospectively pulmonary nodule information was predicted by an AI pulmonary nodule auxiliary diagnosi...

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Autores principales: Wang, Bing, Zhang, Hui, Li, Wei, Fu, Siyun, Li, Ye, Gao, Xiang, Wang, Dongpo, Yang, Xinjie, Xu, Shaofa, Wang, Jinghui, Hou, Dailun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10244560/
https://www.ncbi.nlm.nih.gov/pubmed/37293594
http://dx.doi.org/10.3389/fonc.2023.1037052
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author Wang, Bing
Zhang, Hui
Li, Wei
Fu, Siyun
Li, Ye
Gao, Xiang
Wang, Dongpo
Yang, Xinjie
Xu, Shaofa
Wang, Jinghui
Hou, Dailun
author_facet Wang, Bing
Zhang, Hui
Li, Wei
Fu, Siyun
Li, Ye
Gao, Xiang
Wang, Dongpo
Yang, Xinjie
Xu, Shaofa
Wang, Jinghui
Hou, Dailun
author_sort Wang, Bing
collection PubMed
description OBJECTIVE: The purpose of this study is to establish model for assessing inert nodules predicting nodule volume-doubling. METHODS: A total of 201 patients with T1 lung adenocarcinoma were analysed retrospectively pulmonary nodule information was predicted by an AI pulmonary nodule auxiliary diagnosis system. The nodules were classified into two groups: inert nodules (volume-doubling time (VDT)>600 days n=152) noninert nodules (VDT<600 days n=49). Then taking the clinical imaging features obtained at the first examination as predictive variables the inert nodule judgement model <sn</sn>>(INM) volume-doubling time estimation model (VDTM) were constructed based on a deep learning-based neural network. The performance of the INM was evaluated by the area under the curve (AUC) obtained from receiver operating characteristic (ROC) analysis the performance of the VDTM was evaluated by R(2)(determination coefficient). RESULTS: The accuracy of the INM in the training and testing cohorts was 81.13% and 77.50%, respectively. The AUC of the INM in the training and testing cohorts was 0.7707 (95% CI 0.6779-0.8636) and 0.7700 (95% CI 0.5988-0.9412), respectively. The INM was effective in identifying inert pulmonary nodules; additionally, the R2 of the VDTM in the training cohort was 0.8008, and that in the testing cohort was 0.6268. The VDTM showed moderate performance in estimating the VDT, which can provide some reference during a patients’ first examination and consultation CONCLUSION: The INM and the VDTM based on deep learning can help radiologists and clinicians distinguish among inert nodules and predict the nodule volume-doubling time to accurately treat patients with pulmonary nodules.
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spelling pubmed-102445602023-06-08 Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case−control study Wang, Bing Zhang, Hui Li, Wei Fu, Siyun Li, Ye Gao, Xiang Wang, Dongpo Yang, Xinjie Xu, Shaofa Wang, Jinghui Hou, Dailun Front Oncol Oncology OBJECTIVE: The purpose of this study is to establish model for assessing inert nodules predicting nodule volume-doubling. METHODS: A total of 201 patients with T1 lung adenocarcinoma were analysed retrospectively pulmonary nodule information was predicted by an AI pulmonary nodule auxiliary diagnosis system. The nodules were classified into two groups: inert nodules (volume-doubling time (VDT)>600 days n=152) noninert nodules (VDT<600 days n=49). Then taking the clinical imaging features obtained at the first examination as predictive variables the inert nodule judgement model <sn</sn>>(INM) volume-doubling time estimation model (VDTM) were constructed based on a deep learning-based neural network. The performance of the INM was evaluated by the area under the curve (AUC) obtained from receiver operating characteristic (ROC) analysis the performance of the VDTM was evaluated by R(2)(determination coefficient). RESULTS: The accuracy of the INM in the training and testing cohorts was 81.13% and 77.50%, respectively. The AUC of the INM in the training and testing cohorts was 0.7707 (95% CI 0.6779-0.8636) and 0.7700 (95% CI 0.5988-0.9412), respectively. The INM was effective in identifying inert pulmonary nodules; additionally, the R2 of the VDTM in the training cohort was 0.8008, and that in the testing cohort was 0.6268. The VDTM showed moderate performance in estimating the VDT, which can provide some reference during a patients’ first examination and consultation CONCLUSION: The INM and the VDTM based on deep learning can help radiologists and clinicians distinguish among inert nodules and predict the nodule volume-doubling time to accurately treat patients with pulmonary nodules. Frontiers Media S.A. 2023-05-24 /pmc/articles/PMC10244560/ /pubmed/37293594 http://dx.doi.org/10.3389/fonc.2023.1037052 Text en Copyright © 2023 Wang, Zhang, Li, Fu, Li, Gao, Wang, Yang, Xu, Wang and Hou 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
Wang, Bing
Zhang, Hui
Li, Wei
Fu, Siyun
Li, Ye
Gao, Xiang
Wang, Dongpo
Yang, Xinjie
Xu, Shaofa
Wang, Jinghui
Hou, Dailun
Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case−control study
title Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case−control study
title_full Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case−control study
title_fullStr Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case−control study
title_full_unstemmed Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case−control study
title_short Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case−control study
title_sort neural network-based model for evaluating inert nodules and volume doubling time in t1 lung adenocarcinoma: a nested case−control study
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10244560/
https://www.ncbi.nlm.nih.gov/pubmed/37293594
http://dx.doi.org/10.3389/fonc.2023.1037052
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