Pulmonary nodules are small growths in the lung and a common incidental finding on computed tomography (CT) scans. They are mostly benign but in some cases are early lung cancers. The earlier that a lung cancer is identified and treated, the more likely the outcome will be successful. However, currently, lung nodules may need to be observed with repeat CT scans for up to two years to be able distinguish between benign and malignant nodules. A key clinical challenge is to improve the ability to predict malignancy using the initial CT scan, which would remove the diagnostic delay introduced by the need for repeat scans.
There are two types of lung cancer risk prediction models, which use either statistical approaches, such as the Brock model, or machine learning approaches such as the Lung Cancer Prediction Convolutional Neural Network, LCP-CNN developed by Oxford spin-out Optellum Ltd. The Brock model is currently recommended in clinical practice by the British Thoracic Society and incorporates patient clinical information and nodule characteristics to predict which nodules are likely to be malignant.
LCP-CNN is an externally validated artificial intelligence model that performs better than the Brock model at predicting malignancy. However, because of its AI-based nature, LCP-CNN is not fully interpretable and therefore the importance of individual parameters on the model performance are not known.
In a report published in the journal European Radiology, Dr Madhurima Chetan (Oxford University Hospitals NHS Trust) and colleagues investigated the importance of CT imaging features that contributed to the enhanced performance of LCP-CNN.