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Meng H, Wang TD, Zhuo LY, Hao JW, Sui LY, Yang W, Zang LL, Cui JJ, Wang JN, Yin XP. Quantitative radiomics analysis of imaging features in adults and children Mycoplasma pneumonia. Front Med (Lausanne) 2024; 11:1409477. [PMID: 38831994 PMCID: PMC11146305 DOI: 10.3389/fmed.2024.1409477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Accepted: 04/30/2024] [Indexed: 06/05/2024] Open
Abstract
Purpose This study aims to explore the value of clinical features, CT imaging signs, and radiomics features in differentiating between adults and children with Mycoplasma pneumonia and seeking quantitative radiomic representations of CT imaging signs. Materials and methods In a retrospective analysis of 981 cases of mycoplasmal pneumonia patients from November 2021 to December 2023, 590 internal data (adults:450, children: 140) randomly divided into a training set and a validation set with an 8:2 ratio and 391 external test data (adults:121; children:270) were included. Using univariate analysis, CT imaging signs and clinical features with significant differences (p < 0.05) were selected. After segmenting the lesion area on the CT image as the region of interest, 1,904 radiomic features were extracted. Then, Pearson correlation analysis (PCC) and the least absolute shrinkage and selection operator (LASSO) were used to select the radiomic features. Based on the selected features, multivariable logistic regression analysis was used to establish the clinical model, CT image model, radiomic model, and combined model. The predictive performance of each model was evaluated using ROC curves, AUC, sensitivity, specificity, accuracy, and precision. The AUC between each model was compared using the Delong test. Importantly, the radiomics features and quantitative and qualitative CT image features were analyzed using Pearson correlation analysis and analysis of variance, respectively. Results For the individual model, the radiomics model, which was built using 45 selected features, achieved the highest AUCs in the training set, validation set, and external test set, which were 0.995 (0.992, 0.998), 0.952 (0.921, 0.978), and 0.969 (0.953, 0.982), respectively. In all models, the combined model achieved the highest AUCs, which were 0.996 (0.993, 0.998), 0.972 (0.942, 0.995), and 0.986 (0.976, 0.993) in the training set, validation set, and test set, respectively. In addition, we selected 11 radiomics features and CT image features with a correlation coefficient r greater than 0.35. Conclusion The combined model has good diagnostic performance for differentiating between adults and children with mycoplasmal pneumonia, and different CT imaging signs are quantitatively represented by radiomics.
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Affiliation(s)
- Huan Meng
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Tian-Da Wang
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Li-Yong Zhuo
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Jia-Wei Hao
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Lian-yu Sui
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Wei Yang
- Department of Radiology, Baoding First Central Hospital, Baoding, China
| | - Li-Li Zang
- Department of Radiology, Baoding Children's Hospital, Baoding, China
| | - Jing-Jing Cui
- Department of Research and Development, United Imaging Intelligence (Beijing) Co., Beijing, China
| | - Jia-Ning Wang
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Xiao-Ping Yin
- Clinical Medicine School of Hebei University, Baoding, China
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
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Yang W, Hao Y, Mu K, Li J, Tao Z, Ma D, Xu A. Application of a Radiomics Machine Learning Model for Differentiating Aldosterone-Producing Adenoma from Non-Functioning Adrenal Adenoma. Bioengineering (Basel) 2023; 10:1423. [PMID: 38136014 PMCID: PMC10740639 DOI: 10.3390/bioengineering10121423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 11/23/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023] Open
Abstract
To evaluate the secretory function of adrenal incidentaloma, this study explored the usefulness of a contrast-enhanced computed tomography (CECT)-based radiomics model for distinguishing aldosterone-producing adenoma (APA) from non-functioning adrenal adenoma (NAA). Overall, 68 APA and 60 NAA patients were randomly assigned (8:2 ratio) to either a training or a test cohort. In the training cohort, univariate and least absolute shrinkage and selection operator regression analyses were conducted to select the significant features. A logistic regression machine learning (ML) model was then constructed based on the radiomics score and clinical features. Model effectiveness was evaluated according to the receiver operating characteristic, accuracy, sensitivity, specificity, F1 score, calibration plots, and decision curve analysis. In the test cohort, the area under the curve (AUC) of the Radscore model was 0.869 [95% confidence interval (CI), 0.734-1.000], and the accuracy, sensitivity, specificity, and F1 score were 0.731, 1.000, 0.583, and 0.900, respectively. The Clinic-Radscore model had an AUC of 0.994 [95% CI, 0.978-1.000], and the accuracy, sensitivity, specificity, and F1 score values were 0.962, 0.929, 1.000, and 0.931, respectively. In conclusion, the CECT-based radiomics and clinical radiomics ML model exhibited good diagnostic efficacy in differentiating APAs from NAAs; this non-invasive, cost-effective, and efficient method is important for the management of adrenal incidentaloma.
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Affiliation(s)
- Wenhua Yang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (W.Y.); (Y.H.); (K.M.); (J.L.); (Z.T.)
| | - Yonghong Hao
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (W.Y.); (Y.H.); (K.M.); (J.L.); (Z.T.)
| | - Ketao Mu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (W.Y.); (Y.H.); (K.M.); (J.L.); (Z.T.)
| | - Jianjun Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (W.Y.); (Y.H.); (K.M.); (J.L.); (Z.T.)
| | - Zihui Tao
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (W.Y.); (Y.H.); (K.M.); (J.L.); (Z.T.)
| | - Delin Ma
- Department of Endocrinology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Anhui Xu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; (W.Y.); (Y.H.); (K.M.); (J.L.); (Z.T.)
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