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Multiparameter diagnostic model based on (18)F-FDG PET and clinical characteristics can differentiate thymic epithelial tumors from thymic lymphomas

OBJECTIVE: To evaluate the diagnostic performance of combined multiparametric (18)F-fluorodeoxyglucose positron emission tomography ((18)FDG PET) with clinical characteristics in differentiating thymic epithelial tumors (TETs) from thymic lymphomas. PATIENTS AND METHODS: A total of 173 patients with...

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Autores principales: Wang, Guanyun, Du, Lei, Lu, Xia, Liu, Jiajin, Zhang, Mingyu, Pan, Yue, Meng, Xiaolin, Xu, Xiaodan, Guan, Zhiwei, Yang, Jigang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9382789/
https://www.ncbi.nlm.nih.gov/pubmed/35974323
http://dx.doi.org/10.1186/s12885-022-09988-1
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author Wang, Guanyun
Du, Lei
Lu, Xia
Liu, Jiajin
Zhang, Mingyu
Pan, Yue
Meng, Xiaolin
Xu, Xiaodan
Guan, Zhiwei
Yang, Jigang
author_facet Wang, Guanyun
Du, Lei
Lu, Xia
Liu, Jiajin
Zhang, Mingyu
Pan, Yue
Meng, Xiaolin
Xu, Xiaodan
Guan, Zhiwei
Yang, Jigang
author_sort Wang, Guanyun
collection PubMed
description OBJECTIVE: To evaluate the diagnostic performance of combined multiparametric (18)F-fluorodeoxyglucose positron emission tomography ((18)FDG PET) with clinical characteristics in differentiating thymic epithelial tumors (TETs) from thymic lymphomas. PATIENTS AND METHODS: A total of 173 patients with 80 TETs and 93 thymic lymphomas who underwent (18)F-FDG PET/CT before treatment were enrolled in this retrospective study. All patients were confirmed by pathology, and baseline characteristics and clinical data were also collected. The semi-parameters of (18)F-FDG PET/CT, including lesion size, SUVmax (maximum standard uptake value), SUVmean (mean standard uptake value), TLG (total lesion glycolysis), MTV (metabolic tumor volume) and SUVR (tumor-to-normal liver standard uptake value ratio) were evaluated. The differential diagnostic efficacy was evaluated using the receiver operating characteristic (ROC) curve. Integrated discriminatory improvement (IDI) and net reclassification improvement (NRI), and Delong test were used to evaluate the improvement in diagnostic efficacy. The clinical efficacy was evaluated by decision curve analysis (DCA). RESULTS: Age, clinical symptoms, and metabolic parameters differed significantly between patients with TETs and thymic lymphomas. The ROC curve analysis of SUVR showed the highest differentiating diagnostic value (sensitivity = 0.763; specificity = 0.888; area under the curve [AUC] = 0.881). The combined diagnostics model of age, clinical symptoms and SUVR resulted in the highest AUC of 0.964 (sensitivity = 0.882, specificity = 0.963). Compared with SUVR, the diagnostic efficiency of the model was improved significantly. The DCA also confirmed the clinical efficacy of the model. CONCLUSIONS: The multiparameter diagnosis model based on (18)F-FDG PET and clinical characteristics had excellent value in the differential diagnosis of TETs and thymic lymphomas. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-022-09988-1.
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spelling pubmed-93827892022-08-18 Multiparameter diagnostic model based on (18)F-FDG PET and clinical characteristics can differentiate thymic epithelial tumors from thymic lymphomas Wang, Guanyun Du, Lei Lu, Xia Liu, Jiajin Zhang, Mingyu Pan, Yue Meng, Xiaolin Xu, Xiaodan Guan, Zhiwei Yang, Jigang BMC Cancer Research OBJECTIVE: To evaluate the diagnostic performance of combined multiparametric (18)F-fluorodeoxyglucose positron emission tomography ((18)FDG PET) with clinical characteristics in differentiating thymic epithelial tumors (TETs) from thymic lymphomas. PATIENTS AND METHODS: A total of 173 patients with 80 TETs and 93 thymic lymphomas who underwent (18)F-FDG PET/CT before treatment were enrolled in this retrospective study. All patients were confirmed by pathology, and baseline characteristics and clinical data were also collected. The semi-parameters of (18)F-FDG PET/CT, including lesion size, SUVmax (maximum standard uptake value), SUVmean (mean standard uptake value), TLG (total lesion glycolysis), MTV (metabolic tumor volume) and SUVR (tumor-to-normal liver standard uptake value ratio) were evaluated. The differential diagnostic efficacy was evaluated using the receiver operating characteristic (ROC) curve. Integrated discriminatory improvement (IDI) and net reclassification improvement (NRI), and Delong test were used to evaluate the improvement in diagnostic efficacy. The clinical efficacy was evaluated by decision curve analysis (DCA). RESULTS: Age, clinical symptoms, and metabolic parameters differed significantly between patients with TETs and thymic lymphomas. The ROC curve analysis of SUVR showed the highest differentiating diagnostic value (sensitivity = 0.763; specificity = 0.888; area under the curve [AUC] = 0.881). The combined diagnostics model of age, clinical symptoms and SUVR resulted in the highest AUC of 0.964 (sensitivity = 0.882, specificity = 0.963). Compared with SUVR, the diagnostic efficiency of the model was improved significantly. The DCA also confirmed the clinical efficacy of the model. CONCLUSIONS: The multiparameter diagnosis model based on (18)F-FDG PET and clinical characteristics had excellent value in the differential diagnosis of TETs and thymic lymphomas. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-022-09988-1. BioMed Central 2022-08-16 /pmc/articles/PMC9382789/ /pubmed/35974323 http://dx.doi.org/10.1186/s12885-022-09988-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Wang, Guanyun
Du, Lei
Lu, Xia
Liu, Jiajin
Zhang, Mingyu
Pan, Yue
Meng, Xiaolin
Xu, Xiaodan
Guan, Zhiwei
Yang, Jigang
Multiparameter diagnostic model based on (18)F-FDG PET and clinical characteristics can differentiate thymic epithelial tumors from thymic lymphomas
title Multiparameter diagnostic model based on (18)F-FDG PET and clinical characteristics can differentiate thymic epithelial tumors from thymic lymphomas
title_full Multiparameter diagnostic model based on (18)F-FDG PET and clinical characteristics can differentiate thymic epithelial tumors from thymic lymphomas
title_fullStr Multiparameter diagnostic model based on (18)F-FDG PET and clinical characteristics can differentiate thymic epithelial tumors from thymic lymphomas
title_full_unstemmed Multiparameter diagnostic model based on (18)F-FDG PET and clinical characteristics can differentiate thymic epithelial tumors from thymic lymphomas
title_short Multiparameter diagnostic model based on (18)F-FDG PET and clinical characteristics can differentiate thymic epithelial tumors from thymic lymphomas
title_sort multiparameter diagnostic model based on (18)f-fdg pet and clinical characteristics can differentiate thymic epithelial tumors from thymic lymphomas
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9382789/
https://www.ncbi.nlm.nih.gov/pubmed/35974323
http://dx.doi.org/10.1186/s12885-022-09988-1
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