ArticleScientific reports2024
Pulmonologists-level lung cancer detection based on standard blood test results and smoking status using an explainable machine learning approach.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Current trends and future directions of artificial intelligence in lung cancer diagnosis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Machine Learning for Classification in Lung Cancer Using Routine Clinical and Laboratory Data.Annals of surgical oncology · 2026Article
- A comprehensive review of explainable artificial intelligence in healthcare methods, evaluation, and clinical integration.iScience · 2026Review
- Explainability in AI-enabled medical neurotechnology: a scoping review.Journal of neuroengineering and rehabilitation · 2026Article
- FPA-based weighted average ensemble of deep learning models for classification of lung cancer using CT scan images.Scientific reports · 2025Article
- Deep learning-based identification of patients at increased risk of cancer using routine laboratory markers.Scientific reports · 2025Article
- Lung Cancer Detection Using Bayesian Networks: A Retrospective Development and Validation Study on a Danish Population of High-Risk Individuals.Cancer medicine · 2025Article
- Lung cancer risk prediction using augmented machine learning pipelines with explainable AI.Frontiers in artificial intelligence · 2025Article
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10 authors.
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Abstract
Lung cancer (LC) remains the primary cause of cancer-related mortality, largely due to late-stage diagnoses. Effective strategies for early detection are therefore of paramount importance. In recent years, machine learning (ML) has demonstrated considerable potential in healthcare by facilitating the detection of various diseases. In this retrospective development and validation study, we developed an ML model based on dynamic ensemble selection (DES) for LC detection. The model leverages standard blood sample analysis and smoking history data from a large population at risk in Denmark. The study includes all patients examined on suspicion of LC in the Region of Southern Denmark from 2009 to 2018. We validated and compared the predictions by the DES model with diagnoses provided by five pulmonologists. Among the 38,944 patients, 9,940 had complete data of which 2,505 (25%) had LC. The DES model achieved an area under the roc curve of 0.77±0.01, sensitivity of 76.2%±2.04%, specificity of 63.8%±2.3%, positive predictive value of 41.6%±1.2%, and F
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