The Value of (18)F-FDG PET/CT Mathematical Prediction Model in Diagnosis of Solitary Pulmonary Nodules

PURPOSE: To establish an (18)F-fluorodeoxyglucose ((18)F-FDG) positron emission tomography/computed tomography (PET/CT) mathematical prediction model to improve the diagnosis of solitary pulmonary nodules (SPNs). MATERIALS AND METHODS: We retrospectively reviewed 177 consecutive patients who underwe...

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Autores principales: Wang, Ling, Chen, Yao, Tang, Kun, Lin, Jie, Zhang, Hong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5896270/
https://www.ncbi.nlm.nih.gov/pubmed/29789808
http://dx.doi.org/10.1155/2018/9453967
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author Wang, Ling
Chen, Yao
Tang, Kun
Lin, Jie
Zhang, Hong
author_facet Wang, Ling
Chen, Yao
Tang, Kun
Lin, Jie
Zhang, Hong
author_sort Wang, Ling
collection PubMed
description PURPOSE: To establish an (18)F-fluorodeoxyglucose ((18)F-FDG) positron emission tomography/computed tomography (PET/CT) mathematical prediction model to improve the diagnosis of solitary pulmonary nodules (SPNs). MATERIALS AND METHODS: We retrospectively reviewed 177 consecutive patients who underwent (18)F-FDG PET/CT for evaluation of SPNs. The mathematical model was established by logistic regression analysis. The diagnostic capabilities of the model were calculated, and the areas under the receiver operating characteristic curve (AUC) were compared with Mayo and VA model. RESULTS: The mathematical model was y = exp⁡(x)/[1 + exp⁡(x)], x = −7.363 + 0.079 × age + 1.900 × lobulation + 1.024 × vascular convergence + 1.530 × pleural retraction + 0.359 × the maximum of standardized uptake value (SUV(max)). When the cut-off value was set at 0.56, the sensitivity, specificity, and accuracy of our model were 86.55%, 74.14%, and 81.4%, respectively. The area under the receiver operating characteristic curve (AUC) of our model was 0.903 (95% confidence interval (CI): 0.860 to 0.946). The AUC of our model was greater than that of the Mayo model, the VA model, and PET (P < 0.05) and has no difference with that of PET/CT (P > 0.05). CONCLUSION: The mathematical predictive model has high accuracy in estimating the malignant probability of patients with SPNs.
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spelling pubmed-58962702018-05-22 The Value of (18)F-FDG PET/CT Mathematical Prediction Model in Diagnosis of Solitary Pulmonary Nodules Wang, Ling Chen, Yao Tang, Kun Lin, Jie Zhang, Hong Biomed Res Int Research Article PURPOSE: To establish an (18)F-fluorodeoxyglucose ((18)F-FDG) positron emission tomography/computed tomography (PET/CT) mathematical prediction model to improve the diagnosis of solitary pulmonary nodules (SPNs). MATERIALS AND METHODS: We retrospectively reviewed 177 consecutive patients who underwent (18)F-FDG PET/CT for evaluation of SPNs. The mathematical model was established by logistic regression analysis. The diagnostic capabilities of the model were calculated, and the areas under the receiver operating characteristic curve (AUC) were compared with Mayo and VA model. RESULTS: The mathematical model was y = exp⁡(x)/[1 + exp⁡(x)], x = −7.363 + 0.079 × age + 1.900 × lobulation + 1.024 × vascular convergence + 1.530 × pleural retraction + 0.359 × the maximum of standardized uptake value (SUV(max)). When the cut-off value was set at 0.56, the sensitivity, specificity, and accuracy of our model were 86.55%, 74.14%, and 81.4%, respectively. The area under the receiver operating characteristic curve (AUC) of our model was 0.903 (95% confidence interval (CI): 0.860 to 0.946). The AUC of our model was greater than that of the Mayo model, the VA model, and PET (P < 0.05) and has no difference with that of PET/CT (P > 0.05). CONCLUSION: The mathematical predictive model has high accuracy in estimating the malignant probability of patients with SPNs. Hindawi 2018-03-28 /pmc/articles/PMC5896270/ /pubmed/29789808 http://dx.doi.org/10.1155/2018/9453967 Text en Copyright © 2018 Ling Wang et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wang, Ling
Chen, Yao
Tang, Kun
Lin, Jie
Zhang, Hong
The Value of (18)F-FDG PET/CT Mathematical Prediction Model in Diagnosis of Solitary Pulmonary Nodules
title The Value of (18)F-FDG PET/CT Mathematical Prediction Model in Diagnosis of Solitary Pulmonary Nodules
title_full The Value of (18)F-FDG PET/CT Mathematical Prediction Model in Diagnosis of Solitary Pulmonary Nodules
title_fullStr The Value of (18)F-FDG PET/CT Mathematical Prediction Model in Diagnosis of Solitary Pulmonary Nodules
title_full_unstemmed The Value of (18)F-FDG PET/CT Mathematical Prediction Model in Diagnosis of Solitary Pulmonary Nodules
title_short The Value of (18)F-FDG PET/CT Mathematical Prediction Model in Diagnosis of Solitary Pulmonary Nodules
title_sort value of (18)f-fdg pet/ct mathematical prediction model in diagnosis of solitary pulmonary nodules
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5896270/
https://www.ncbi.nlm.nih.gov/pubmed/29789808
http://dx.doi.org/10.1155/2018/9453967
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