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Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
Published on: May 19, 2023
Comparison of nodule volumetric classification by using two different nodule segmentation algorithms in an LDCT lung
Yifei Mao1, Harriet L Lancaster1, Marjolein A Heuvelmans2
1Department of Epidemiology, and Radiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands; Institute for Diagnostic Accuracy, Groningen, the Netherlands.
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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