Objectives To assess the impact of reconstruction parameters on AI’s performance in detecting and classifying risk-dominant nodules in a baseline low-dose CT (LDCT) screening among a Chinese general population. Materials and methods Baseline LDCT scans from 300 consecutive participants in the Netherlands and China Big-3 (NELCIN-B3) trial were included. AI analyzed each scan reconstructed with four settings: 1 mm/0.7 mm thickness/interval with medium-soft and hard kernels (D45f/1 mm, B80f/1 mm) and 2 mm/1 mm with soft and medium-soft kernels (B30f/2 mm, D45f/2 mm). Reading results from consensus read by two radiologists served as reference standard. At scan level, inter-reader agreement between AI and reference standard, sensitivity, and specificity in determining the presence of a risk-dominant nodule were evaluated. For reference-standard risk-dominant nodules, nodule detection rate, and agreement in nodule type classification between AI and reference standard were assessed. Results AI-D45f/1 mm demonstrated a significantly higher sensitivity than AI-B80f/1 mm in determining the presence of a risk-dominant nodule per scan (77.5% vs. 31.5%, p < 0.0001). For reference-standard risk-dominant nodules (111/300, 37.0%), kernel variations (AI-D45f/1 mm vs. AI-B80f/1 mm) did not significantly affect AI’s nodule detection rate (87.4% vs. 82.0%, p = 0.26) but substantially influenced the agreement in nodule type classification between AI and reference standard (87.7% [50/57] vs. 17.7% [11/62], p < 0.0001). Change in thickness/interval (AI-D45f/1 mm vs. AI-D45f/2 mm) had no substantial influence on any of AI’s performance (p > 0.05). Conclusion Variations in reconstruction kernels significantly affected AI’s performance in risk-dominant nodule type classification, but not nodule detection. Ensuring consistency with radiologist-preferred kernels significantly improved agreement in nodule type classification and may help integrate AI more smoothly into clinical workflows. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> Patient management in lung cancer screening depends on the risk-dominant nodule, yet no prior studies have assessed the impact of reconstruction parameters on AI performance for these nodules. <Emphasis Type="BoldItalic">Findings</Emphasis> The difference between reconstruction kernels (AI-D45f/1 mm vs. AI-B80f/1 mm, or AI-B30f/2 mm vs. AI-D45f/2 mm) significantly affected AI’s performance in risk-dominant nodule type classification, but not nodule detection. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> The use of kernel for AI consistent with radiologist’s choice is likely to improve the overall performance of AI-based CAD systems as an independent reader and support greater clinical acceptance and integration of AI tools into routine practice. Graphical Abstract

Artificial intelligence as an independent reader of risk-dominant lung nodules: influence of CT reconstruction parameters

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2025年8月29日

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Yifei Mao,
Marjolein A. Heuvelmans,
Marcel van Tuinen,
Donghoon Yu,
Jaeyoun Yi,
Matthijs Oudkerk,
Zhaoxiang Ye,
Geertruida H. de Bock,
Monique D. Dorrius

摘要

Objectives

To assess the impact of reconstruction parameters on AI’s performance in detecting and classifying risk-dominant nodules in a baseline low-dose CT (LDCT) screening among a Chinese general population.

Materials and methods

Baseline LDCT scans from 300 consecutive participants in the Netherlands and China Big-3 (NELCIN-B3) trial were included. AI analyzed each scan reconstructed with four settings: 1 mm/0.7 mm thickness/interval with medium-soft and hard kernels (D45f/1 mm, B80f/1 mm) and 2 mm/1 mm with soft and medium-soft kernels (B30f/2 mm, D45f/2 mm). Reading results from consensus read by two radiologists served as reference standard. At scan level, inter-reader agreement between AI and reference standard, sensitivity, and specificity in determining the presence of a risk-dominant nodule were evaluated. For reference-standard risk-dominant nodules, nodule detection rate, and agreement in nodule type classification between AI and reference standard were assessed.

Results

AI-D45f/1 mm demonstrated a significantly higher sensitivity than AI-B80f/1 mm in determining the presence of a risk-dominant nodule per scan (77.5% vs. 31.5%, p < 0.0001). For reference-standard risk-dominant nodules (111/300, 37.0%), kernel variations (AI-D45f/1 mm vs. AI-B80f/1 mm) did not significantly affect AI’s nodule detection rate (87.4% vs. 82.0%, p = 0.26) but substantially influenced the agreement in nodule type classification between AI and reference standard (87.7% [50/57] vs. 17.7% [11/62], p < 0.0001). Change in thickness/interval (AI-D45f/1 mm vs. AI-D45f/2 mm) had no substantial influence on any of AI’s performance (p > 0.05).

Conclusion

Variations in reconstruction kernels significantly affected AI’s performance in risk-dominant nodule type classification, but not nodule detection. Ensuring consistency with radiologist-preferred kernels significantly improved agreement in nodule type classification and may help integrate AI more smoothly into clinical workflows.

Key Points

Question Patient management in lung cancer screening depends on the risk-dominant nodule, yet no prior studies have assessed the impact of reconstruction parameters on AI performance for these nodules.

Findings The difference between reconstruction kernels (AI-D45f/1 mm vs. AI-B80f/1 mm, or AI-B30f/2 mm vs. AI-D45f/2 mm) significantly affected AI’s performance in risk-dominant nodule type classification, but not nodule detection.

Clinical relevance The use of kernel for AI consistent with radiologist’s choice is likely to improve the overall performance of AI-based CAD systems as an independent reader and support greater clinical acceptance and integration of AI tools into routine practice.

Graphical Abstract