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 coefficient.

RESULTS

Margin clarity, IQ, and density confidence were significantly higher for UHR ( < 0.001). No significant differences between UHR and NR were observed in the variability (standard deviation across readers) for measuring long and short axes ( > 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 ( = 0.566) and good for IQ ( = 0.637). Image noise was significantly higher ( < 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.