A retrospective study in the United Kingdom evaluated the AI tool AVIEW LCS+ (Coreline Soft) for lung nodule detection and classification in CT lung cancer screening using 1,252 low-dose CT (LDCT) scans from the UK Lung Cancer Screening (UKLS) trial. The AI tool was independently validated against individual radiologists’ reads and a European expert panel reference standard, as well as gold-standard histological lung cancer outcomes.
AI analyzed the scans using a 100 mm³ solid component threshold to classify cases as negative or indeterminate/positive, achieving a negative predictive value (NPV) of 99.8% by correctly identifying 30 of 31 baseline lung cancers. Compared to radiologists, AI demonstrated fewer misclassifications (10% vs. 17–19%) and a lower false-negative rate (5% vs. 12–19%), while also aligning more closely with the expert panel reference standard than any individual reader.
By accurately ruling out negative cases, AI substantially reduced the number of CT scans requiring radiologist review, leading to a projected workload reduction of 67–79%. These findings highlight AI’s potential as a first-reader in lung cancer screening, improving efficiency, diagnostic consistency, and the scalability of screening programs worldwide.
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Histological proven AI performance in the UKLS CT lung cancer screening study: Potential for workload reduction
European Journal of Cancer, 2025
Abstract
Purpose
Artificial intelligence (AI) could reduce lung cancer screening computer tomography (CT)-reading workload if used as a first-reader, ruling-out negative CT-scans at baseline. Evidence is lacking to support AI performance when compared to gold-standard lung cancer outcomes. This study validated the performance of a commercially available AI software in the UK lung cancer screening (UKLS) trial dataset, with comparison to human reads and histological lung cancer outcomes, and estimated CT-reading workload reduction.
Methods
1252 UKLS-baseline-CT-scans were evaluated independently by AI and human readers. AI performance was evaluated on two-levels. Firstly, AI classification and individual reads were compared to a EU reference standard (based on NELSON2.0-European Position Statement) determined by a European expert panel blinded from individual results. A positive misclassification was defined as a nodule positive read ≥ 100mm3 and no/<100mm3 nodules in the expert read; A negative misclassification was defined as a nodule negative read, whereas an indeterminate or positive finding in the expert read. Secondly, AI nodule classification was compared to gold-standard histological lung cancer outcomes. CT-reading workload reduction was calculated from AI negative CT-scans when AI was used as first-reader.
Results
Expert panel reference standard reported 815 (65 %) negative and 437 (35 %) indeterminate/positive CT-scans in the dataset of 1252 UKLS-participants. Compared to the reference standard, AI resulted in less misclassification than human reads, NPV 92·0 %(90·2 %-95·3 %). On comparison to gold-standard, AI detected all 31 baseline-round lung cancers, but classified one as negative due to the 100mm3 threshold, NPV 99·8 %(99·0 %-99·9 %). Estimated maximum CT-reading workload reduction was 79 %.
Conclusion
Implementing AI as first-reader to rule-out negative CT-scans, shows considerable potential to reduce CT-reading workload and does not lead to missed lung cancers.