Quantitative disparities in pulmonary nodule classification Between two segmentation methods in LDCT screening cohorts

Product: AVIEW LCS+ Company: Coreline Soft

Comparison of nodule volumetric classification by using two different nodule segmentation algorithms in an LDCT lung cancer baseline screening dataset

European Journal of Radiology, 2025

Abstract

Objectives

To investigate the performance of two segmentation algorithms for nodule volumetric classification at participant/scan level in the NELCIN-B3 cohort (Netherlands and China Big-3), a lung cancer screening program (LCS) using low-dose CT (LDCT).

Methods

Baseline scans with qualified LDCT images from consecutive NELCIN-B3 participants were included from June 2017 to July 2018. Performance of two software algorithms were independently evaluated by two radiologists: software A (Syngo.via VB30A) by reader 1 and software B (AVIEW v1.1.39.14) by reader 2. According to the NELSON2.0 protocol, nodules with a solid component ≥ 100 mm3 were classified as indeterminate-positive, while all other nodules were classified as negative. Disagreements in classification were resolved by consensus with three senior radiologists. These results served as a reference standard for identifying positive misclassifications (PM) and negative misclassifications (NM).

Results

In total, 300 participants were evaluated comprising 159 women (53.0 %) and 193 (64.3 %) never smokers, with a mean ± standard deviation age of 61.2 ± 7.1 years. There were disagreements in 17 cases: in 11 (11/300, 3.7 %), this was due to differences in nodule selection and nodule type classification between readers; and in 6 (6/300, 2.0 %), this was due to variations in nodule volume metrics between algorithms. Inter-software agreement was almost perfect (κ = 0.88 [95 %CI: 0.83–0.93]). In the consensus read, reader 1/software A generated 12 misclassifications (11 PM, 1 NM), giving a negative predictive value of 99.6 % (95 % CI: 98.9 %–100.0 %). Reader 2/software B generated 5 misclassifications (2 PM, 3 NM), giving a negative predictive value of 98.9 % (95 % CI: 97.7 %–100.0 %).

Conclusion

Two software algorithms (Syngo.via VB30A and AVIEW v1.1.39.14) showed comparable performance for lung nodule volumetric classification at participant/scan level. Further research is needed to confirm the results in other LDCT LCS programs.

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