A multireader, multicase study comparing ultra-high-resolution and conventional-resolution computed tomography for lung nodule characterization.

Objectives: The objective of the study was to evaluate the efficacy of ultra-high-resolution computed tomography (UHRCT) in comparison to conventional resolution computed tomography (CT) for the characterization of lung nodules.

Material and methods: 104 non-contrast chest UHRCT scans (mean age of 66 years, 57 females) with pulmonary nodules were retrospectively collected (February-November 2022), and corresponding normal-resolution (NR) reconstructions were synthesized using a validated algorithm. Five blinded radiologists scored the following for each localized nodule in the ultra-high-resolution (UHR) and NR datasets: Margin clarity (5-point Likert scale), image quality "IQ" (3-point), density confidence (0-100%), and size (long/short axes). Image noise (voxel standard deviation) was calculated within the trachea. Differences between UHR and NR were tested using the Wilcoxon signed-rank test. Intrareader agreement was quantified with intraclass correlation coefficient (ICC), and ordinal association between margin clarity and IQ was quantified with Kendall's τ coefficient.

Results: Margin clarity, IQ, and density confidence were significantly higher for UHR (P < 0.001). No significant differences between UHR and NR were observed in the variability (standard deviation across readers) for measuring long and short axes (P > 0.100). Intrareader agreement for UHR and NR was poor for margin clarity, IQ, and density confidences (ICC < 0.250) but moderate for short axes (ICC = 0.731) and good for long axes (ICC = 0.807). Ordinal association between margin clarity and IQ was moderate for UHR (τ = 0.566) and good for IQ (τ = 0.637). Image noise was significantly higher (P < 0.001) for UHR compared to NR.

Conclusion: UHRCT offers significant improvements in the visualization of lung nodules compared to conventional resolution CT, albeit with an increase in image noise.