Fusing Data from CT Deep Learning, CT Radiomics and Peripheral Blood Immune profiles to Diagnose Lung Cancer in a Cohort of Patients Experiencing Symptoms

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Background: Lung cancer is the leading cause of cancer-related deaths. Diagnosis at late stages is common due to the largely non-specific nature of presenting symptoms contributing to high mortality. There is a lack of specific, minimally invasive low-cost tests to screen patients ahead of the diagnostic biopsy. Methods: 344 patients experiencing symptoms from the lung clinic of Lister hospital suspected of lung cancer were recruited. Predictive covariates were successfully generated on 170 patients from Computed Tomography (CT) scans using CT Texture Analysis (CTTA) and Deep Learning Autoencoders (DLA) as well as from peripheral blood data for immunity using high depth flow-cytometry and for exosome protein components. Predictive signatures were formed by combining covariates using Bayesian regression on a randomly chosen 128-patient training set and validated on a 42-patient held-out set. Final signatures were generated by fusing the data sources at different levels. Findings: Immune, CTTA and DLA single modality signatures had overall AUCs of 0.69, 0.70 and 0.73 respectively. The final combined signature had a ROC AUC of 0.81. The overall sensitivity and specificity were 0.72 and 0.77 respectively. Interpretation: Combining immune monitoring with CT scan data is an effective approach to improving sensitivity and specificity of Lung cancer screening even in patients experiencing symptoms.

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