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Objective <p>To evaluate the effect of perifissural nodules (PFNs) on radiologist workload within an AI-first reader workflow for lung cancer screening, given that AI cannot morphologically classify benign PFNs measuring ≥ 100 mm<sup>3</sup>.</p> Materials and methods <p>One thousand two hundred fifty baseline low-dose CT scans from the UK Lung Screening (UKLS) Trial were analyzed. A commercially available AI software automatically identified all nodules with solid components ≥ 100 mm³ per the NELSON 2.0-European Position Statement (EUPS) guideline. Three readers independently performed PFN classification, with a senior radiologist with over 20 years of experience performing an arbitration read for the final reference classification (typical PFN, atypical PFN, or non-PFN). Histological outcomes for all fissure-attached nodules were reviewed to confirm benignity. The proportion of participants where a benign typical PFN was the sole finding of nodule presence ≥ 100 mm³ was calculated, representing the extra workload for radiologists to review.</p> Results <p>A total of 1252 participants (mean age, 68.5 ± 4.0 years; 928 men [74%]) were analyzed. AI detected 838 nodules with solid components ≥ 100 mm³ in 431 (34%) participants. 57 nodules in 49 (3.9%) participants were classified as typical PFNs by the reference standard. Only 24 of 1252 participants (1.9%) had a typical PFN ≥ 100 mm³ as the sole finding that added extra workload. No typical PFNs (0/57) were malignant.</p> Conclusion <p>The impact of typical PFNs on the maximum achievable radiologist workload reduction in an AI-first reader workflow is negligible, with only 1.9% of participants requiring additional radiologist review triggered solely by these benign nodules.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>In an AI-first lung cancer screening workflow, do typical PFNs ≥ 100 mm</i><sup>3</sup><i> create a significant bottleneck for radiologist workload</i>?</p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>In the UKLS trial, typical PFNs ≥ 100 mm³ were rare, creating negligible extra workload (1.9% of participants), and none were malignant (0/57)</i>.</p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>The concern that PFN morphology creates a bottleneck in AI-first screening workflows is unfounded. Our findings support the feasibility of volume-based AI triage, allowing radiologists to focus on other false positives without being overwhelmed by PFNs</i>.</p> Graphical Abstract <p></p>

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Negligible impact of perifissural nodules in an AI-first reader workflow from UK lung screening trial

  • 期刊论文

  • 发表日期：

    2026年3月23日

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  • Beibei Jiang,
  • Daiwei Han,
  • Jiali Cai,
  • Harriet L. Lancaster,
  • Michael P. A. Davies,
  • Anna N. H. Walstra,
  • Jan-Willem C. Gratama,
  • Mario Silva,
  • Jaeyoun Yi,
  • Carlijn M. van der Aalst,
  • Marjolein A. Heuvelmans,
  • John K. Field,
  • Matthijs Oudkerk

摘要

Objective

To evaluate the effect of perifissural nodules (PFNs) on radiologist workload within an AI-first reader workflow for lung cancer screening, given that AI cannot morphologically classify benign PFNs measuring ≥ 100 mm3.

Materials and methods

One thousand two hundred fifty baseline low-dose CT scans from the UK Lung Screening (UKLS) Trial were analyzed. A commercially available AI software automatically identified all nodules with solid components ≥ 100 mm³ per the NELSON 2.0-European Position Statement (EUPS) guideline. Three readers independently performed PFN classification, with a senior radiologist with over 20 years of experience performing an arbitration read for the final reference classification (typical PFN, atypical PFN, or non-PFN). Histological outcomes for all fissure-attached nodules were reviewed to confirm benignity. The proportion of participants where a benign typical PFN was the sole finding of nodule presence ≥ 100 mm³ was calculated, representing the extra workload for radiologists to review.

Results

A total of 1252 participants (mean age, 68.5 ± 4.0 years; 928 men [74%]) were analyzed. AI detected 838 nodules with solid components ≥ 100 mm³ in 431 (34%) participants. 57 nodules in 49 (3.9%) participants were classified as typical PFNs by the reference standard. Only 24 of 1252 participants (1.9%) had a typical PFN ≥ 100 mm³ as the sole finding that added extra workload. No typical PFNs (0/57) were malignant.

Conclusion

The impact of typical PFNs on the maximum achievable radiologist workload reduction in an AI-first reader workflow is negligible, with only 1.9% of participants requiring additional radiologist review triggered solely by these benign nodules.

Key Points

Question In an AI-first lung cancer screening workflow, do typical PFNs ≥ 100 mm3 create a significant bottleneck for radiologist workload?

Findings In the UKLS trial, typical PFNs ≥ 100 mm³ were rare, creating negligible extra workload (1.9% of participants), and none were malignant (0/57).

Clinical relevance The concern that PFN morphology creates a bottleneck in AI-first screening workflows is unfounded. Our findings support the feasibility of volume-based AI triage, allowing radiologists to focus on other false positives without being overwhelmed by PFNs.

Graphical Abstract