Comparison of single- and dual-energy CT combined with artificial intelligence for the diagnosis of pulmonary nodules

Aim: To explore the efficiency of single- and dual-energy computed tomography (CT) with artificial intelligence (AI) for the diagnosis of pulmonary nodules.

Materials and methods: In a prospective study, 682 patients undergoing a chest CT examination using a dual-energy system were divided randomly into two groups: single-energy mode (group S, n=341) and dual-energy mode (group D, n=341). CT images were first analysed automatically with the AI pulmonary nodule-detection software. CT features including nodule number, lesion size, and nodule type were then analysed by experienced radiologists to establish a reference diagnosis. Subsequently, the accuracy, sensitivity, false-positive rate, and miss rate of AI were calculated. Additionally, image quality and radiation dose were also compared between the two groups.

Results: The contrast-to-noise ratio data suggested that the image quality of group D was superior to that of group S (0.16 ± 0.10 versus 0.00 ± 0.17), and the radiation dose of group D was lower than that of group S (0.32 ± 0.10 versus 0.62 ± 0.11 mSv.cm). Compared to group S, group D exhibited a significantly higher sensitivity and lower accuracy for nodule identification, size classification, and nodule type (all p<0.05, except for 5-10 mm and calcified nodules).

Conclusions: Compared with single-energy CT, dual-energy CT may significantly improve the sensitivity of AI for the diagnosis of pulmonary nodules and is practical for the screening of pulmonary nodules in a large population. In addition, dual-energy CT examination demonstrates improved image quality and is associated with reduced exposure to ionising radiation, but its accuracy is poorer.