Accurate diagnosis of high-risk pulmonary nodules using a non-invasive epigenetic biomarker test
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Background
Accurate non-invasive tests to improve the early detection and diagnosis of lung cancer are urgently needed, given that large tumors may metastasize or be resistant to treatment. However, no regulatory-approved blood tests are available for the early detection of lung cancer. We aimed to improve the classification of pulmonary nodules to identify malignant ones in a high-prevalence patient group.
Methods
A study involving 806 participants with undiagnosed nodules larger than 5 mm, identified via CT imaging, focused on assessing nucleosome levels and histone modifications in circulating blood. Nodules were classified as malignant or benign. A logistic regression analysis was performed. For model development, the data were randomly divided into training (n = 483) and validation (n = 121) datasets. The model’s performance was then evaluated using a separate testing dataset (n = 202).
Results
Among patients, 755 (93.7%) had a tissue diagnosis. The overall malignancy rate in the cohort was 80.4%. For all datasets, the AUCs were as follows: training, 0.74; validation, 0.86; and test, 0.79 (accuracy range: 0.80–0.88). Sensitivity showed consistent results across all datasets (0.91, 0.95, and 0.93, respectively), whereas specificity ranged from 0.37 to 0.64. For smaller nodules (5–10 mm), the model recorded accuracy values of 0.76, 0.88, and 0.85. Sensitivity values of 0.91, 1.00, and 0.94 further highlight the robust diagnostic capability of the model. The performance of the model across the RADS categories was evaluated, and it demonstrated consistent accuracy.
Conclusion
Our EB panel detected non-small cell lung cancer early in a high-risk patient group with high sensitivity and accuracy. The EB model was particularly effective in identifying high-risk lung nodules, including small, part-solid, and non-solid nodules, and provided further evidence for external validation.
Synopsis
An epigenetic biomarker (EB) model demonstrated high sensitivity and accuracy for early lung cancer detection in 806 patients with pulmonary nodules. It performed well across imaging types and stages, including small nodules, reducing false negatives in minimally invasive surgery.
