Systematic review and meta‑analysis of factors predicting postoperative lung function after lung cancer resection
Hongling Wang, Lihong He, Xiaoyun Hu, Gongxue Xian

TL;DR
This study reviews methods to predict lung function after lung cancer surgery and finds CT volume and density measurements to be the most accurate.
Contribution
The study systematically evaluates and compares prediction methods for postoperative lung function, identifying CT-based approaches as the most accurate.
Findings
Computed tomography (CT) volume and density measurement showed the highest accuracy for predicting postoperative FEV1.
The mean difference between predicted and actual FEV1 was 83 ml (95% CI, 41–116).
The analyzed studies had a low risk of bias, supporting the reliability of the findings.
Abstract
Lung resection continues to be the most effective treatment for early‑stage lung cancer. Prediction of postoperative lung function is particularly important when evaluating patient eligibility for surgery, as it helps assess the likelihood of experiencing difficulty breathing after the operation. We aimed to identify the most common methods used to predict postoperative lung function in clinical practice and to compare their accuracy. A systematic review and meta‑analysis were performed to synthesize research focused on the prediction of postoperative lung function. A total of 10 studies were included in the analysis. The Cochrane risk of bias tool was utilized to evaluate the risk of bias in the studies. Additionally, a meta‑analysis of the mean difference between the predicted and measured values of forced expiratory volume in 1 second (FEV1) was conducted. The I2 value was computed…
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Taxonomy
TopicsLung Cancer Diagnosis and Treatment · Radiomics and Machine Learning in Medical Imaging · Head and Neck Cancer Studies
