ArticleBMC pulmonary medicine2025
Development of a predictive model for pneumothorax after microwave ablation based on radiomics and clinical baseline data.
Article in BMC pulmonary medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
aimLung 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.
methodsData 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.
resultsThe 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).
conclusionCompared 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.