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. Patients and Methods: 344 symptomatic patients 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. Results: Immune and DLA were the best single modality signatures with test set AUCs of 0.76 (95% CI: 0.61 - 0.91) and 0.75 (95% CI: 0.60 - 0.90) respectively. The final combined signature had a ROC AUC of 0.86 (95% CI: 0.73 - 0.99) on the withheld test set. The overall sensitivity and specificity were 0.722 and 0.901 respectively. Conclusions: Combining immune monitoring with CT scan data is an effective approach to improving sensitivity and specificity of Lung cancer screening even in symptomatic patients.

BG is the co-founder/co-inventor of TexRAD texture analysis software used in this study for CT Texture analysis and a shareholder (not an employee) of Feedback Plc., a UK-based company which owns the TexRAD texture analysis software. PB and TN are both currently employees of GlaxoSmithKline (GSK) but this study has not received any funding support from GSK. All remaining authors have declared no conflicts of interest.

https://clinicaltrials.gov/ct2/show/NCT04629079

This work was funded by the CRUK grant [C1519/A27375]. SE, EC and JAS were further supported by core funding from the Wellcome Trust/EPSRC Centre for Medical Engineering [WT203148/Z/16/Z] and by the National Institute for Health and Care Research (NIHR) Clinical Research Facility at Guy's and St Thomas' NHS Foundation Trust. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. This work was undertaken at UCLH/UCL, which received a proportion of the funding from the UK's Department of Health's NIHR Biomedical Research Center's funding scheme.

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Health Research Authority (HRA) of the UK NHS gave ethical approval for this work (REC reference: 19/EE/0357 20th Feb 2020).

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All data produced in the present study are available upon reasonable request to the authors from the DIO: 10.18742/28070390