Abstract 180P
Background
Lung cancer screening is being implemented in multiple countries worldwide and consequently radiologists workload is ever increasing. Artificial intelligence (AI) could provide the solution to significantly reducing workload if used as a first read filter to accurately rule-out negative cases (none or lung nodules <100mm3). We aimed to validate an AI prototype in an external LDCT dataset.
Methods
Validation of the AI prototype was performed using 1254 LDCT baseline CT scans from the UKLS trial. Four manual readers and AI independently assessed all scans. A consensus read by two experienced radiologists, blinded from initial results, was performed in the case of discrepancies. All individual results were compared to the consensus read to determine a final classification. Individual cases were then classified as either; correct positives (CPs) or negatives (CNs)(≥100 or <100mm3, respectively), positive-misclassifications (PMs) (nodules classified by the reader/AI as ≥100mm3, but at consensus<100mm3) or negative-misclassifications(NMs) (nodules classified by the reader/AI as <100mm3, but at consensus≥100mm3).
Results
Of the 1254 cases, 816(65%) had no nodules or only nodules <100mm3 (negative cases). AI achieved a higher negative predictive value 91.7(89.8-91.4) than all manual readers [reader1; 79.0(77.5-82.0), reader 2; 80.1(81.3-85.4), reader 3; 77.5(76.0-79.0) and reader 4; 78.5(77.0-80.0)]. If AI was used as a first-read filter to rule out negative cases, workload reduction was calculated at 65% [(total scans;1254 - (CPs;370 + PMs;59)) / total scans;1254].
Conclusions
AI can achieve a better negative predictive performance than manual readers when used as a first-read filter at baseline LDCT lung cancer screening. If implemented in a lung cancer screening program, we estimate that only 35% of baseline cases with nodules >100mm3 would need to be assessed by a radiologist.
Legal entity responsible for the study
The authors.
Funding
Has not received any funding.
Disclosure
All authors have declared no conflicts of interest.