AI performance for nodule volume doubling time in the follow-up of the UKLS lung cancer screening study compared to expert consensus and histological validation

Aim To validate an artificial intelligence (AI) software for automated assessment of nodule growth by volume doubling time measurement (VDT) on protocol-mandated follow-up low-dose CT (LDCT) scans from the UK lung cancer screening (UKLS) trial. Methods This validation study included 710 UKLS participants with 939 LDCT follow-up scans (361 3-month and 578 12-month). Follow-up scans were assessed independently by both AI and human readers. A positive finding warranting referral was defined as the largest nodule with a solid component ≥ 100 mm3 showing VDT ≤ 400 days at follow-up. Performance was benchmarked against the European expert panel (reference standard) and then the histological outcomes (gold standard). Results Against the expert panel, AI achieved the lowest 3-month negative misclassification (NM) rate (1/11, 9.1 %), versus human readers (range: 18.2–63.6 %). AI’s positive misclassification (PM) rate was initially 7.8 % (28/361) at 3 months but decreased to 0.9 % (5/578) at 12 months. Against histological outcomes of 9 screen-detected lung cancers, AI identified VDT ≤ 400 days in all 4 cancers also deemed positive by the expert panel at the earliest 3-month follow-up, while human readers missed or delayed referrals in 1–3 of these. AI also identified VDT ≤ 400 days in 3 of 5 cancers that the panel classified as negative, primarily due to their sub-threshold volume (<100mm³). Conclusions The automated AI system showed strong VDT assessment performance in follow-up screening, outperforming human readers in the early identification of rapid growth in histologically-confirmed cancers, thus supporting its potential to enhance risk stratification and facilitate earlier lung cancer detection.