OBJECTIVES: Current risk-stratification approaches for incidental PNs involve significant uncertainty and may result in unnecessary invasive procedures. This study aimed to estimate potential patient outcomes by utilizing an integrated blood-based auto-antibody (AAb) test and a blood-based integrated classifier (IC) test to reclassify risk of malignancy in incidental PNs.

METHODS: An Excel-based clinical outcomes model was developed to evaluate use and impact of two blood-based tests for incidental PN diagnosis and follow-up within a health system of 1,000,000 patients over a 1-year time horizon. Use of the tests within a health system was compared to standard of care (without the blood-based tests). Model inputs included those related to pre-test risk of lung cancer, risk classification with and without the blood-based tests, and utilization of diagnostic procedures for PNs and lung cancer, including imaging and invasive procedures informed by current diagnostic practices based on patient risk classification.

RESULTS: In a hypothetical health system, the model estimated that the use of the two blood-based tests reduced missed malignancies by 6.9%, reduced unnecessary biopsy on benign nodules by 19.9%, and reduced unnecessary surgeries in benign nodules by 13.3% while also reducing time to cancer diagnosis by 24%.

CONCLUSIONS: The addition of a minimally-invasive blood-based AAb test and IC test to reclassify PN risk of malignancy in a health system’s lung nodule program may help to reduce unnecessary procedures and lead to earlier diagnosis of lung cancer. The proposed model can help health systems understand and evaluate benefits of blood-based tests for PNs and improve patient outcomes. Real-world studies of the tests align with these findings and demonstrate improved patient outcomes.