Persistent pulmonary subsolid nodules: model-based iterative reconstruction for nodule classification and measurement variability on low-dose CT.

OBJECTIVES: To compare the pulmonary subsolid nodule (SSN) classification agreement and measurement variability between filtered back projection (FBP) and model-based iterative reconstruction (MBIR).
METHODS: Low-dose CTs were reconstructed using FBP and MBIR for 47 patients with 47 SSNs. Two readers independently classified SSNs into pure or part-solid ground-glass nodules, and measured the size of the whole nodule and solid portion twice on both reconstruction algorithms. Nodule classification agreement was analyzed using Cohen's kappa and compared between reconstruction algorithms using McNemar's test. Measurement variability was investigated using Bland-Altman analysis and compared with the paired t-test.
RESULTS: Cohen's kappa for inter-reader SSN classification agreement was 0.541-0.662 on FBP and 0.778-0.866 on MBIR. Between the two readers, nodule classification was consistent in 79.8 % (75/94) with FBP and 91.5 % (86/94) with MBIR (p = 0.027). Inter-reader measurement variability range was -5.0-2.1 mm on FBP and -3.3-1.8 mm on MBIR for whole nodule size, and was -6.5-0.9 mm on FBP and -5.5-1.5 mm on MBIR for solid portion size. Inter-reader measurement differences were significantly smaller on MBIR (p = 0.027, whole nodule; p = 0.011, solid portion).
CONCLUSIONS: MBIR significantly improved SSN classification agreement and reduced measurement variability of both whole nodules and solid portions between readers. KEY POINTS: • Low-dose CT using MBIR algorithm improves reproducibility in the classification of SSNs. • MBIR would enable more confident clinical planning according to the SSN type. • Reduced measurement variability on MBIR allows earlier detection of potentially malignant nodules.