Background: Accurate noninvasive 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 regulatoryapproved 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 to 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 to 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.

Dorian Pamart, Aristotelis Kotronoulas, and Marielle Herzog are employees of Belgian Volition, Jacob Micallef is an employee of Volition Diagnostics UK. Aristotelis Kotronoulas, Marielle Herzog and Jacob Micallef are shareholders of VolitionRX Limited.

researchregistry-10711

This study was supported by grants from the Ministry of Science and Technology, Taiwan (MOST 113-2314-B-002-038-MY3) and VolitionRX Limited. Both Volition and the authors collaboratively contributed to the study's development and interpretation of the results.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This prospective blood specimen collection and retrospective evaluation study was approved by the institutional Review Boards (201905009RIFC) of National Taiwan University Hospital

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All data produced in the present study are available upon reasonable request to the author