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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-07-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023052647 |
| _version_ | 1797771445452931072 |
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| author | Rui Han Ling Huang Sijing Zhou Jiran Shen Pulin Li Min Li Xingwang Wu Ran Wang |
| author_facet | Rui Han Ling Huang Sijing Zhou Jiran Shen Pulin Li Min Li Xingwang Wu Ran Wang |
| author_sort | Rui Han |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-12T21:37:41Z |
| format | Article |
| id | doaj.art-a40620c6749f47c08789dc9fe80374b9 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-12T21:37:41Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-a40620c6749f47c08789dc9fe80374b92023-07-27T05:58:24ZengElsevierHeliyon2405-84402023-07-0197e18056Novel clinical radiomic nomogram method for differentiating malignant from non-malignant pleural effusionsRui Han0Ling Huang1Sijing Zhou2Jiran Shen3Pulin Li4Min Li5Xingwang Wu6Ran Wang7Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, ChinaDepartment of Infectious Disease, Hefei Second People's Hospital, Hefei, 230001, ChinaDepartment of Occupational Disease, Hefei Third Clinical College of Anhui Medical University, Hefei, 230022, ChinaDepartment of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, ChinaDepartment of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, ChinaDepartment of Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, ChinaDepartment of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China; Corresponding author.Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2405844023052647Pleural effusionComputed tomographyRadiomicsNomogram |
| spellingShingle | Rui Han Ling Huang Sijing Zhou Jiran Shen Pulin Li Min Li Xingwang Wu Ran Wang Novel clinical radiomic nomogram method for differentiating malignant from non-malignant pleural effusions Heliyon Pleural effusion Computed tomography Radiomics Nomogram |
| 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 | Pleural effusion Computed tomography Radiomics Nomogram |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023052647 |
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