Development of a predictive model for pneumothorax after microwave ablation based on radiomics and clinical baseline data

Sep 24, 2026

Aim: Develop a predictive model for pneumothorax following microwave ablation (MWA) by integrating radiomic data with clinical baseline information. Methods: Retrospective data from 111 lung cancer patients undergoing MWA were analyzed; clinical features used binary logistic regression, radiomics used LASSO with fivefold nested cross-validation, and a combined model used logistic regression. Performance was assessed with ROC/AUC, DeLong’s test, and calibration curves. Results: The comprehensive model achieved the highest AUC (0.9262). Conclusion: Combined radiomics and clinical data improve prediction accuracy and may support more reliable clinical risk assessment.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Abstract

Introduction

Background

Methods

Results

Conclusion

Xie etal. BMC Pulmonary Medicine (2025) 25:396 [https://doi.org/10.1186/s12890-025-03850-3](https://doi.org/10.1186/s12890-025-03850-3) BMC Pulmonary Medicine RESEARCH Open Access Development of a predictive model for pneumothorax after microwave ablation based on radiomics and clinical baseline data Xiangyu Xie 1†, Kun Li 1†, Lei Chen 1, Hong Li 1, Chaofan Meng 1 and Liang Zheng 1* Abstract Aim Lung cancer is a leading cause of cancer-related mortality globally, with a five-year survival rate lower than many other cancers. Surgery remains the most effective treatment; however, fewer than 50% of patients are eligible due to compromised pulmonary function or the presence of multiple lesions. Microwave ablation is an emerging, minimally invasive treatment that has shown promise in prolonging survival and preserving organ integrity with fewer side effects. Despite its safety profile, pneumothorax remains a common complication. Radiomics has gained traction for early diagnosis, prognosis prediction, and treatment assessment. This study aims to develop a predictive model for pneumothorax following MWA by integrating radiomic data. Methods Data from 111 lung cancer patients undergoing MWA were retrospectively analyzed. A clinical model was developed using binary logistic regression, while a radiomics model was constructed via LASSO regression with fivefold nested cross-validation. A comprehensive model was built by combining both feature sets using logistic regression. Model performance was evaluated using ROC curves, AUC values, DeLong’s test, and calibration curves to assess the agreement between predicted and observed outcomes. Results The clinical model achieved an AUC of 0.8846 (95% CI: 0 .8160–0. 9533), the radiomics model had an AUC of 0.8353 (95% CI: 0 .7453–0. 9253), and the comprehensive model showed the highest AUC of 0.9262 (95% CI: 0.8712– 0. 9812) . DeLong’s test revealed that the comprehensive model outperformed both the clinical model (Z = -2 . 24, P = 0 . 025) and the radiomics model (Z = -2 . 57, P = 0 . 010) . Conclusion Compared with the individual models, the predictive model developed by combining radiomic and clinical baseline data demonstrated superior diagnostic performance in predicting pneumothorax after microwave ablation. By incorporating additional multimodal data and clinical factors in the future, this model has the potential to serve as a more accurate predictive tool in clinical practice. Keywords Non-small cell lung cancer, Radiomics, Predictive model †Xiangyu Xie and Kun Li contributed equally to this work. *Correspondence: Liang Zheng [suda12366@163.com](suda12366@163.com) 1Department of Thoracic Surgery, The First People’s Hospital of Changzhou and The Third Affiliated Hospital of Soochow University, Changzhou, China © The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creati](http://creati)[vecommons.org/l](vecommons.org/l)icenses/by-nc-nd/4.0/. Xie et al. BMC Pulmonary Medicine (2025) 25:396 Introduction Background Lung cance

FAQs

What is the main goal of the study?

To develop a predictive model for pneumothorax after microwave ablation by integrating radiomics with clinical baseline data.

How were the clinical and radiomics models built?

The clinical model used binary logistic regression. The radiomics model used LASSO regression with fivefold nested cross-validation.

Which model performed best and how was it evaluated?

The comprehensive combined model performed best, assessed using ROC curves, AUC values, DeLong’s test, and calibration curves.

Sep 24, 2026

Uploaded by 4398046744996