ORIGINAL RESEARCH article
Front. Med.
Sec. Pulmonary Medicine
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1652653
This article is part of the Research TopicBridging Tradition and Future: Cutting-edge Exploration and Application of Artificial Intelligence in Comprehensive Diagnosis and Treatment of Lung DiseasesView all 11 articles
Multicenter Study on CT-Based Radiomics for Predicting Severity and Delayed Recovery in Mycoplasma Pneumoniae Pneumonia
Provisionally accepted
- 1Baoding First Central Hospital, Baoding, China
- 2United Imaging Intelligence Co Ltd, Beijing, China
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Objective To develop and validate model based on clinical, imaging, and Radiomics features for predicting disease severity and delayed recovery in Mycoplasma pneumoniae pneumonia (MPP). Methods This multicenter retrospective study enrolled 238 patients (training cohort), 60 (testing cohort), and 278 (validation cohort). Patients were classified into non-severe MPP (NSMPP) and severe MPP (SMPP) groups based on guideline, and further stratified post-treatment into recovery or delayed recovery groups. Radiomics features were extracted from chest CT using PyRadiomics, with Least Absolute Shrinkage and Selection Operator (LASSO) regression for feature selection. Three random forest-based predictive models were developed, including Clinical-Image, Radiomics, and Integrated. Predictive performance was evaluated via by the area under the receiver operating characteristic curve (AUC), calibration, and clinical utility. Results The Integrated model demonstrated superior discrimination for severity prediction (validation AUC: 0.784, 95% CI: 0.722–0.845) and delayed recovery (validation AUC: 0.865, 95% CI: 0.770–0.960), outperforming Clinical-Image (severity AUC: 0.771, 95% CI: 0.695–0.847; delayed recovery AUC: 0.807, 95% CI: 0.724–0.950) and Radiomics model (severity AUC: 0.710, 95% CI: 0.643–0.776; delayed recovery AUC: 0.837, 95% CI: 0.724–0.950). Integrated Discrimination Improvement (IDI) analysis demonstrated significant enhancements in the Integrated model compared to both the Clinical-Image and Radiomics models for predicting both disease severity and delayed recovery (all P < 0.05). Key predictors comprised D-dimer (severity OR=1.371; delayed recovery OR=4.061), systemic immune-inflammation index (delayed recovery OR=6.607), and consolidation patterns (delayed recovery OR=2.820). Conclusion The Integrated model combining clinical, imaging, and Radiomics features enhances risk stratification for MPP severity and delayed recovery.
Keywords: Patient Outcome Assessment, Mycoplasma pneumonia, Radiomics, X-ray computed tomography, machine learning
Received: 24 Jun 2025; Accepted: 20 Oct 2025.
Copyright: © 2025 Li, Song, Chen and Yan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Qian Li, liqian202505@163.com
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