Contrast-enhanced CT-based radiomics nomogram for differentiation of low-risk and high-risk thymomas
- VernacularTitle:基于增强CT影像组学列线图鉴别胸腺瘤风险程度的单中心回顾性研究
- Author:
Qinglin REN
1
,
2
;
Wenbo HE
2
,
3
;
Jiarui YUE
2
,
3
;
Hongbi XIAO
2
,
3
;
Yusheng SHU
2
,
3
Author Information
1. 1. Dalian Medical University, Dalian, 116000, Liaoning, P. R. China 2. Department of Thoracic Surgery, Northern Jiangsu People'
2. s Hospital, Yangzhou, 225000, Jiangsu, P. R. China
3. Department of Thoracic Surgery, Northern Jiangsu People'
- Publication Type:Journal Article
- Keywords:
Thymoma;
radiomics;
contrast-enhanced CT;
nomogram
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2024;31(08):1109-1118
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop a radiomics nomogram based on contrast-enhanced CT (CECT) for preoperative prediction of high-risk and low-risk thymomas. Methods Clinical data of patients with thymoma who underwent surgical resection and pathological confirmation at Northern Jiangsu People's Hospital from January 2018 to February 2023 were retrospectively analyzed. Feature selection was performed using the Pearson correlation coefficient and least absolute shrinkage and selection operator (LASSO) method. An ExtraTrees classifier was used to construct the radiomics signature model and the radiomics signature. Univariate and multivariable logistic regression was applied to analyze clinical-radiological characteristics and identify variables for developing a clinical model. The radiomics nomogram model was developed by combining the radiomics signature and clinical features. Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, accuracy, negative predictive value, and positive predictive value. Calibration curves and decision curves were plotted to assess model accuracy and clinical values. Results A total of 120 patients including 59 females and 61 males with an average age of 56.30±12.10 years. There were 84 patients in the training group and 36 in the validation group, 62 in the low-risk thymoma group and 58 in the high-risk thymoma group. Radiomics features (1 038 in total) were extracted from the arterial phase of CECT scans, among which 6 radiomics features were used to construct the radiomics signature. The radiomics nomogram model, combining clinical-radiological characteristics and the radiomics signature, achieved an AUC of 0.872 in the training group and 0.833 in the validation group. Decision curve analysis demonstrated better clinical efficacy of the radiomics nomogram than the radiomics signature and clinical model. Conclusion The radiomics nomogram based on CECT showed good diagnostic value in distinguishing high-risk and low-risk thymoma, which may provide a noninvasive and efficient method for clinical decision-making.
