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Novel clinical radiomic nomogram method for differentiating malignant from non-malignant pleural effusions

OBJECTIVES: To establish a clinical radiomics nomogram that differentiates malignant and non-malignant pleural effusions. METHODS: A total of 146 patients with malignant pleural effusion (MPE) and 93 patients with non-MPE (NMPE) were included. The ROI image features of chest lesions were extracted u...

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Autores principales: Han, Rui, Huang, Ling, Zhou, Sijing, Shen, Jiran, Li, Pulin, Li, Min, Wu, Xingwang, Wang, Ran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10395353/
https://www.ncbi.nlm.nih.gov/pubmed/37539225
http://dx.doi.org/10.1016/j.heliyon.2023.e18056
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author Han, Rui
Huang, Ling
Zhou, Sijing
Shen, Jiran
Li, Pulin
Li, Min
Wu, Xingwang
Wang, Ran
author_facet Han, Rui
Huang, Ling
Zhou, Sijing
Shen, Jiran
Li, Pulin
Li, Min
Wu, Xingwang
Wang, Ran
author_sort Han, Rui
collection PubMed
description OBJECTIVES: To establish a clinical radiomics nomogram that differentiates malignant and non-malignant pleural effusions. METHODS: A total of 146 patients with malignant pleural effusion (MPE) and 93 patients with non-MPE (NMPE) were included. The ROI image features of chest lesions were extracted using CT. Univariate analysis was performed, and least absolute shrinkage selection operator and multivariate logistic analysis were used to screen radiomics features and calculate the radiomics score. A nomogram was constructed by combining clinical factors with radiomics scores. ROC curve and decision curve analysis (DCA) were used to evaluate the prediction effect. RESULTS: After screening, 19 radiomics features and 2 clinical factors were selected as optimal predictors to establish a combined model and construct a nomogram. The AUC of the combined model was 0.968 (95% confidence interval [CI] = 0.944–0.986) in the training cohort and 0.873 (95% CI = 0.796–0.940) in the validation cohort. The AUC value of the combined model was significantly higher than those of the clinical and radiomics models (0.968 vs. 0.874 vs. 0.878, respectively). This was similar in the validation cohort (0.873, 0.764, and 0.808, respectively). DCA confirmed the clinical utility of the radiomics nomogram. CONCLUSION: CT-based radiomics showed better diagnostic accuracy and model fit than clinical and radiological features in distinguishing MPE from NMPE. The combination of both achieved better diagnostic performance. These findings support the clinical application of the nomogram in diagnosing MPE using chest CT.
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spelling pubmed-103953532023-08-03 Novel clinical radiomic nomogram method for differentiating malignant from non-malignant pleural effusions Han, Rui Huang, Ling Zhou, Sijing Shen, Jiran Li, Pulin Li, Min Wu, Xingwang Wang, Ran Heliyon Research Article OBJECTIVES: To establish a clinical radiomics nomogram that differentiates malignant and non-malignant pleural effusions. METHODS: A total of 146 patients with malignant pleural effusion (MPE) and 93 patients with non-MPE (NMPE) were included. The ROI image features of chest lesions were extracted using CT. Univariate analysis was performed, and least absolute shrinkage selection operator and multivariate logistic analysis were used to screen radiomics features and calculate the radiomics score. A nomogram was constructed by combining clinical factors with radiomics scores. ROC curve and decision curve analysis (DCA) were used to evaluate the prediction effect. RESULTS: After screening, 19 radiomics features and 2 clinical factors were selected as optimal predictors to establish a combined model and construct a nomogram. The AUC of the combined model was 0.968 (95% confidence interval [CI] = 0.944–0.986) in the training cohort and 0.873 (95% CI = 0.796–0.940) in the validation cohort. The AUC value of the combined model was significantly higher than those of the clinical and radiomics models (0.968 vs. 0.874 vs. 0.878, respectively). This was similar in the validation cohort (0.873, 0.764, and 0.808, respectively). DCA confirmed the clinical utility of the radiomics nomogram. CONCLUSION: CT-based radiomics showed better diagnostic accuracy and model fit than clinical and radiological features in distinguishing MPE from NMPE. The combination of both achieved better diagnostic performance. These findings support the clinical application of the nomogram in diagnosing MPE using chest CT. Elsevier 2023-07-13 /pmc/articles/PMC10395353/ /pubmed/37539225 http://dx.doi.org/10.1016/j.heliyon.2023.e18056 Text en © 2023 Published by Elsevier Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Han, Rui
Huang, Ling
Zhou, Sijing
Shen, Jiran
Li, Pulin
Li, Min
Wu, Xingwang
Wang, Ran
Novel clinical radiomic nomogram method for differentiating malignant from non-malignant pleural effusions
title Novel clinical radiomic nomogram method for differentiating malignant from non-malignant pleural effusions
title_full Novel clinical radiomic nomogram method for differentiating malignant from non-malignant pleural effusions
title_fullStr Novel clinical radiomic nomogram method for differentiating malignant from non-malignant pleural effusions
title_full_unstemmed Novel clinical radiomic nomogram method for differentiating malignant from non-malignant pleural effusions
title_short Novel clinical radiomic nomogram method for differentiating malignant from non-malignant pleural effusions
title_sort novel clinical radiomic nomogram method for differentiating malignant from non-malignant pleural effusions
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10395353/
https://www.ncbi.nlm.nih.gov/pubmed/37539225
http://dx.doi.org/10.1016/j.heliyon.2023.e18056
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