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Objectives <p>Computed tomography (CT) scans for lung cancer screening provide the opportunity of quantifying incidental findings. We evaluated the repeatability of AI-based measurements of incidental findings using short-term repeat CT scan pairs.</p> Materials and methods <p>AI-Rad Companion Chest CT software was applied to low-dose non-contrast CT scans from the NELSON lung cancer screening trial to measure aorta diameters, coronary artery calcium volume (CACV), vertebral height and radiodensity, and emphysema (low attenuation area percentage, LAA%). Categories (absent/present) of aortic dilatation and osteopenia, and severity (none/mild/moderate/severe) of CACV and emphysema were calculated. We included subjects who had a short-term repeat CT scan pair with a maximum interval of 120 days. We analyzed repeatability with absolute and relative differences, and agreement with the intraclass correlation coefficient (ICC) and Cohen’s kappa.</p> Results <p>1436 subjects were included, with age (mean ± SD) 59.7 ± 5.7 years, 86.3% men, 55.9% currently smoking, and scan interval 85 ± 20 days. Mean absolute differences were 0.7 to 1.5 mm for aorta diameters, 26 mm<sup>3</sup> for CACV, 0.3 mm to 0.4 mm for vertebral height, and 7.6 HU for vertebral radiodensity. Median absolute difference was 0.8% for LAA%. Aorta diameters showed good (0.75 &lt; ICC &lt; 0.9) to excellent (ICC &gt; 0.9) agreement. Agreement for the rest was excellent. Categorization Cohen’s kappa between the first and second measurements was 0.68 for aortic dilatation, 0.63 for CACV, 0.84 for osteopenia, and 0.70 for emphysema.</p> Conclusion <p>In a lung cancer screening cohort with short-term repeat CT, the repeatability and agreement of automated AI measurements of aortic diameters, coronary calcium, vertebral height and radiodensity, and emphysema was good to excellent.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>Can AI measurements of incidental findings beyond lung nodules be repeatably performed on pairs of lung cancer screening chest CT scans of the same subject?</i></p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>The repeatability and agreement of AI-based measurements of aorta diameter, coronary calcium, vertebrae, and emphysema were generally excellent.</i></p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>Measurement by radiologists of incidental findings on lung cancer screening chest CT scans imposes a high workload. AI algorithms allow for automatic measurement of incidental findings on lung cancer screening chest CT with high repeatability and agreement.</i></p> Graphical Abstract <p></p>

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Repeatability of AI-quantified incidental findings on lung cancer screening CT scans in the NELSON trial

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月17日

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  • Stijn Bunk,
  • Thijs Bruins Slot,
  • Edwin Bennink,
  • Grigory Sidorenkov,
  • Nils van der Velden,
  • Niels Schurink,
  • Félix Lades,
  • Markus Sebald,
  • Marjolein A. Heuvelmans,
  • Hester A. Gietema,
  • Joachim G. Aerts,
  • Geertruida H. de Bock,
  • Cornelia Schaefer-Prokop,
  • Pim A. de Jong,
  • Rozemarijn Vliegenthart,
  • Firdaus A. A. Mohamed Hoesein,
  • Robin Cornelissen,
  • Ralph Stadhouders,
  • Jeroen G. J. van Rooij,
  • Lianne Trap,
  • Kristiaan Nackaerts,
  • Walter de Wever,
  • Mathias Prokop,
  • Colin Jacobs,
  • Noa Antonissen,
  • Geertruida H. de Bock,
  • Danrong Zhong,
  • Harry J. M. Groen,
  • Nils van der Velden,
  • George S. Downward,
  • Roel C. H. Vermeulen

摘要

Objectives

Computed tomography (CT) scans for lung cancer screening provide the opportunity of quantifying incidental findings. We evaluated the repeatability of AI-based measurements of incidental findings using short-term repeat CT scan pairs.

Materials and methods

AI-Rad Companion Chest CT software was applied to low-dose non-contrast CT scans from the NELSON lung cancer screening trial to measure aorta diameters, coronary artery calcium volume (CACV), vertebral height and radiodensity, and emphysema (low attenuation area percentage, LAA%). Categories (absent/present) of aortic dilatation and osteopenia, and severity (none/mild/moderate/severe) of CACV and emphysema were calculated. We included subjects who had a short-term repeat CT scan pair with a maximum interval of 120 days. We analyzed repeatability with absolute and relative differences, and agreement with the intraclass correlation coefficient (ICC) and Cohen’s kappa.

Results

1436 subjects were included, with age (mean ± SD) 59.7 ± 5.7 years, 86.3% men, 55.9% currently smoking, and scan interval 85 ± 20 days. Mean absolute differences were 0.7 to 1.5 mm for aorta diameters, 26 mm3 for CACV, 0.3 mm to 0.4 mm for vertebral height, and 7.6 HU for vertebral radiodensity. Median absolute difference was 0.8% for LAA%. Aorta diameters showed good (0.75 < ICC < 0.9) to excellent (ICC > 0.9) agreement. Agreement for the rest was excellent. Categorization Cohen’s kappa between the first and second measurements was 0.68 for aortic dilatation, 0.63 for CACV, 0.84 for osteopenia, and 0.70 for emphysema.

Conclusion

In a lung cancer screening cohort with short-term repeat CT, the repeatability and agreement of automated AI measurements of aortic diameters, coronary calcium, vertebral height and radiodensity, and emphysema was good to excellent.

Key Points

Question Can AI measurements of incidental findings beyond lung nodules be repeatably performed on pairs of lung cancer screening chest CT scans of the same subject?

Findings The repeatability and agreement of AI-based measurements of aorta diameter, coronary calcium, vertebrae, and emphysema were generally excellent.

Clinical relevance Measurement by radiologists of incidental findings on lung cancer screening chest CT scans imposes a high workload. AI algorithms allow for automatic measurement of incidental findings on lung cancer screening chest CT with high repeatability and agreement.

Graphical Abstract