The utility of a convolutional neural network (CNN) model score for cancer risk in indeterminate small solid pulmonary nodules, compared to clinical practice according to British Thoracic Society guidelines.

PURPOSE: To determine how implementation of an artificial intelligence nodule algorithm, the Lung Cancer Prediction Convolutional Neural Network (LCP-CNN), at the point of incidental nodule detection would have influenced further investigation and management using a series of threshold scores at both the benign and malignant end of the spectrum. METHOD: An observational retrospective study was performed in the assessment of nodules between 5-15 mm (158 benign, 32 malignant) detected on CT scans, which were performed as part of routine practice. The LCP-CNN was applied to the baseline CT scan producing a percentage score, and subsequent imaging and management determined for each threshold group. We hypothesized that the 5% low risk threshold group requires only one follow-up, the 0.56% very low risk threshold group requires no follow-up and the 80% high risk threshold group warrants expedited intervention. RESULTS: The 158 benign nodules had an LCP-CNN score between 0.1 and 70.8%, median 5.5% (IQR 1.4-18.0), whilst the 32 cancer nodules had an LCP-CNN score between 10.1 and 98.7%, median 59.0% (IQR 37.1-83.9). 24/61 CT scans in the 0.56-5% group (n = 37) and 21/21 CT scans <0.56% group (n = 13) could be obviated resulting in an overall reduction of 18.6% (45/242) CT scans in the benign cohort. In the 80% group (n = 10), expedited intervention of malignant nodules could result in a 3.6-month reduction in time delay in 5 cancer patients. CONCLUSION: We show the potential of artificial intelligence to reduce the need for follow-up scans and intervention in low-scoring benign nodules, whilst potentially accelerating the investigation and treatment of high-scoring cancer nodules.