External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules
Language English Country England, Great Britain Media print-electronic
Document type Journal Article, Multicenter Study, Research Support, Non-U.S. Gov't, Validation Study
Grant support
II-LB-0716-20006
Department of Health - United Kingdom
PubMed
32139611
PubMed Central
PMC7231457
DOI
10.1136/thoraxjnl-2019-214104
PII: thoraxjnl-2019-214104
Knihovny.cz E-resources
- Keywords
- CT imaging, lung cancer, non-small cell lung cancer,
- MeSH
- Algorithms MeSH
- Early Detection of Cancer methods MeSH
- Databases, Factual MeSH
- Adult MeSH
- Risk Assessment MeSH
- Incidence MeSH
- Neoplasm Invasiveness pathology MeSH
- Cohort Studies MeSH
- Middle Aged MeSH
- Humans MeSH
- Multiple Pulmonary Nodules epidemiology pathology physiopathology MeSH
- Cell Transformation, Neoplastic pathology MeSH
- Lung Neoplasms epidemiology pathology physiopathology MeSH
- Neural Networks, Computer * MeSH
- Area Under Curve MeSH
- Predictive Value of Tests MeSH
- Prognosis MeSH
- Retrospective Studies MeSH
- ROC Curve MeSH
- Aged MeSH
- Neoplasm Staging MeSH
- Artificial Intelligence * MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Multicenter Study MeSH
- Research Support, Non-U.S. Gov't MeSH
- Validation Study MeSH
BACKGROUND: Estimation of the risk of malignancy in pulmonary nodules detected by CT is central in clinical management. The use of artificial intelligence (AI) offers an opportunity to improve risk prediction. Here we compare the performance of an AI algorithm, the lung cancer prediction convolutional neural network (LCP-CNN), with that of the Brock University model, recommended in UK guidelines. METHODS: A dataset of incidentally detected pulmonary nodules measuring 5-15 mm was collected retrospectively from three UK hospitals for use in a validation study. Ground truth diagnosis for each nodule was based on histology (required for any cancer), resolution, stability or (for pulmonary lymph nodes only) expert opinion. There were 1397 nodules in 1187 patients, of which 234 nodules in 229 (19.3%) patients were cancer. Model discrimination and performance statistics at predefined score thresholds were compared between the Brock model and the LCP-CNN. RESULTS: The area under the curve for LCP-CNN was 89.6% (95% CI 87.6 to 91.5), compared with 86.8% (95% CI 84.3 to 89.1) for the Brock model (p≤0.005). Using the LCP-CNN, we found that 24.5% of nodules scored below the lowest cancer nodule score, compared with 10.9% using the Brock score. Using the predefined thresholds, we found that the LCP-CNN gave one false negative (0.4% of cancers), whereas the Brock model gave six (2.5%), while specificity statistics were similar between the two models. CONCLUSION: The LCP-CNN score has better discrimination and allows a larger proportion of benign nodules to be identified without missing cancers than the Brock model. This has the potential to substantially reduce the proportion of surveillance CT scans required and thus save significant resources.
Motol Teaching Hospital 5 Prague Czech Republic
Oxford University Hospitals NHS Foundation Trust Oxford Oxfordshire UK
Radiology Churchill Hospital Oxford UK
Radiology Leeds Teaching Hospitals NHS Trust Leeds UK
Radiology Nottingham University Hospitals NHS Trust Nottingham UK
Respiratory Medicine Glenfield General Hospital Leicester UK
Respiratory Medicine Leeds Teaching Hospitals NHS Trust Leeds UK
Respiratory Medicine Nottingham University Hospitals City Campus Nottingham UK
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