Effect of emphysema on AI software and human reader performance in lung nodule detection from low-dose chest CT.

Sourlos, Nikos; Pelgrim, GertJan; Wisselink, Hendrik Joost; Yang, Xiaofei; de Jonge, Gonda; Rook, Mieneke; Prokop, Mathias; Sidorenkov, Grigory; van Tuinen, Marcel; Vliegenthart, Rozemarijn; van Ooijen, Peter M. A.

Background: Emphysema influences the appearance of lung tissue in computed tomography (CT). We evaluated whether this affects lung nodule detection by artificial intelligence (AI) and human readers (HR). Methods: Individuals were selected from the "Lifelines" cohort who had undergone low-dose chest CT. Nodules in individuals without emphysema were matched to similar-sized nodules in individuals with at least moderate emphysema. AI results for nodular findings of 30–100 mm3 and 101–300 mm3 were compared to those of HR; two expert radiologists blindly reviewed discrepancies. Sensitivity and false positives (FPs)/scan were compared for emphysema and non-emphysema groups. Results: Thirty-nine participants with and 82 without emphysema were included (n = 121, aged 61 ± 8 years (mean ± standard deviation), 58/121 males (47.9%)). AI and HR detected 196 and 206 nodular findings, respectively, yielding 109 concordant nodules and 184 discrepancies, including 118 true nodules. For AI, sensitivity was 0.68 (95% confidence interval 0.57–0.77) in emphysema versus 0.71 (0.62–0.78) in non-emphysema, with FPs/scan 0.51 and 0.22, respectively (p = 0.028). For HR, sensitivity was 0.76 (0.65–0.84) and 0.80 (0.72–0.86), with FPs/scan of 0.15 and 0.27 (p = 0.230). Overall sensitivity was slightly higher for HR than for AI, but this difference disappeared after the exclusion of benign lymph nodes. FPs/scan were higher for AI in emphysema than in non-emphysema (p = 0.028), while FPs/scan for HR were higher than AI for 30–100 mm3 nodules in non-emphysema (p = 0.009). Conclusions: AI resulted in more FPs/scan in emphysema compared to non-emphysema, a difference not observed for HR. Relevance statement: In the creation of a benchmark dataset to validate AI software for lung nodule detection, the inclusion of emphysema cases is important due to the additional number of FPs. Key points: • The sensitivity of nodule detection by AI was similar in emphysema and non-emphysema. • AI had more FPs/scan in emphysema compared to non-emphysema. • Sensitivity and FPs/scan by the human reader were comparable for emphysema and non-emphysema. • Emphysema and non-emphysema representation in benchmark dataset is important for validating AI.

COMPUTED tomography; PULMONARY nodules; ARTIFICIAL intelligence

European Radiology Experimental, 2024, Vol 8, Issue 1, p1

2509-9280

Academic Journal

10.1186/s41747-024-00459-9