ArticleSurgery today2024
Development of a machine learning-based risk model for postoperative complications of lung cancer surgery.
Article in Surgery today, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- AI Trustworthiness in the Perioperative Period for Patients with Serious Illness: Scoping Narrative Review.JMIR perioperative medicine · 2026Article
- Artificial intelligence in thoracic surgery: a narrative review of clinical advances and applications in 2025.Journal of thoracic disease · 2026Review
- CT-Assessed Body Composition as Predictor of Post-Operative Complications in Lung Cancer Patients.Cancers · 2026Review
- Article
- Predicting postoperative complications after pneumonectomy using machine learning: a 10-year study.Annals of medicine · 2025Article
- Prediction model for postoperative pulmonary complications after thoracoscopic surgery with machine learning algorithms and SHapley Additive exPlanations.Journal of thoracic disease · 2025Article
- Enhancing Thoracic Surgery with AI: A Review of Current Practices and Emerging Trends.Current oncology (Toronto, Ont.) · 2024Review
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Authors and funding
10 authors.
Funding
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Abstract
purposeTo develop a comorbidity risk score specifically for lung resection surgeries.
methodsWe reviewed the medical records of patients who underwent lung resections for lung cancer, and developed a risk model using data from 2014 to 2017 (training dataset), validated using data from 2018 to 2019 (validation dataset). Forty variables were analyzed, including 35 factors related to the patient's overall condition and five factors related to surgical techniques and tumor-related factors. The risk model for postoperative complications was developed using an elastic net regularized generalized linear model. The performance of the risk model was evaluated using receiver operating characteristic curves and compared with the Charlson Comorbidity Index (CCI).
resultsThe rate of postoperative complications was 34.7% in the training dataset and 21.9% in the validation dataset. The final model consisted of 20 variables, including age, surgical-related factors, respiratory function tests, and comorbidities, such as chronic obstructive pulmonary disease, a history of ischemic heart disease, and 12 blood test results. The area under the curve (AUC) for the developed risk model was 0.734, whereas the AUC for the CCI was 0.521 in the validation dataset.
conclusionsThe new machine learning model could predict postoperative complications with acceptable accuracy. CLINICAL REGISTRATION NUMBER: 2020-0375.
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