Work

Construction of a radiomics model based on CT imaging for predicting capsular invasion in thymomas

Year: 2025

Type: article

Abstract: Objective: To develop a radiomics-based predictive model for capsular invasion in thymomas by applying machine learning algorithms to non-contrast and contrast-enhanced CT imaging. This study aimed to assess the influence of intratumoural and peritumoural regions on capsular invasion prediction and to compare the performance of models derived from these regions within the same dataset, thereby identifying the optimal predictive model. Methods: = 45) in a 7:3 ratio. Radiomic feature selection was performed using univariate logistic regression analysis followed by least absolute shrinkage and selection operator (LASSO) regression. Predictive models were developed employing multiple machine learning algorithms, including logistic regression. Model performance was evaluated through receiver operating characteristic (ROC) curve analysis, with sensitivity, specificity, F1 score, and decision curve analysis (DCA) used to assess diagnostic accuracy and clinical applicability. DeLong's test was applied to compare the area under the curve (AUC) values between different models. Calibration curves were generated to evaluate model calibration, and model interpretability was examined using the Shapley Additive exPlanations (SHAP) method. Results: Comparative analysis of machine learning methods across different tumour regions revealed that the support vector machine (SVM) model, developed using radiomic features from the 4 mm peritumoural region on contrast-enhanced CT scans, demonstrated optimal predictive performance. This model achieved area under the curve (AUC) values of 0.890 [95% confidence interval (CI): 0.823-0.956] in the training cohort and 0.888 (95% CI: 0.792-0.983) in the test cohort. Conclusion: CT-based radiomics demonstrates efficacy in predicting capsular invasion in thymomas, with the peritumoural region proving particularly significant. This methodology shows potential for supporting clinicians in preoperative treatment strategy formulation.

Source: Frontiers in Radiology

DOI: 10.3389/fradi.2025.1707488

Authors: Shuo Liang, Yanhong Chen, Jianhui Li, Zhenchun Song, Li Zhou, Rui Yin

Institution: Tianjin Chest Hospital

Language: English

Topic: Radiomics and Machine Learning in Medical Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,353