WCLC 2025 - Posters & ePosters-PT1.04.01 Incidental Pulmonary Nodule Programs (IPN) Working Together With LDCT Scans and Artificial Intelligence (AI) Increase Lung Cancer Detection

PT1.04.01 Incidental Pulmonary Nodule Programs (IPN) Working Together With LDCT Scans and Artificial Intelligence (AI) Increase Lung Cancer Detection

Back to course

Pdf Summary

This study from the Memorial Cancer Institute and Florida International University explores how Incidental Pulmonary Nodule (IPN) programs, combined with Low-Dose CT (LDCT) scans and Artificial Intelligence (AI), can improve lung cancer detection. Although LDCT is the standard for lung cancer screening in high-risk groups, barriers such as low screening rates and social determinants of health limit its reach, especially among underserved populations, younger individuals, and never-smokers.<br /><br />To address these gaps, the researchers implemented an IPN program using an AI tool called ILUMINATE, integrated with their electronic medical records system (EPIC). This AI screened over 205,000 radiology reports from various CT scans—including chest, abdomen, spine, and calcium scoring—across five hospitals. A nurse navigator reviewed AI-flagged cases weekly with pulmonologists and thoracic surgeons to recommend follow-up interventions or continued surveillance.<br /><br />Comparing results from July 2021 to January 2025, the LDCT program screened 2,373 individuals with 10 lung cancers (0.4%) detected. In contrast, the IPN program identified 5,972 individuals with nodules needing follow-up, leading to 127 lung cancer diagnoses (2.1%). The IPN group included many patients who did not qualify for LDCT screening. Both programs diagnosed mostly early-stage (I/II) non-small cell lung cancers in outpatient or emergency settings, with no significant demographic differences between groups.<br /><br />The findings demonstrate that an AI-driven IPN program significantly increases lung cancer detection compared to LDCT screening alone by efficiently processing a large volume of imaging data and overcoming human resource limitations. This approach potentially reduces disparities by capturing patients missed by traditional screening criteria. Further follow-up is planned to evaluate survival outcomes and stage migration.

Asset Subtitle

Luis Raez

Meta Tag

Speaker Luis Raez

Topic Screening and Early Detection

Keywords

Incidental Pulmonary Nodule

IPN program

Low-Dose CT

LDCT screening

Artificial Intelligence

AI in lung cancer detection

ILUMINATE AI tool

lung cancer disparities

lung cancer screening

early-stage non-small cell lung cancer