Discussion
lung cancer, cancer, tumor, cancer from non-cancer
TUMOR, LUNG, CANCER, THORACIC, LUNG CANCER
To our knowledge, this study is the first that combines automated flow cytometric analysis with machine learning to predict the presence of lung cancer from sputum samples. Sputum as diagnostic material provides a snapshot of the tumor itself, of its microenvironment (ME), and of its field of cancerization (FoC). Expert cytological analysis of sputum can detect cancerous and pre-malignant cells [The case for moving to a high-throughput, automated flow-based approach combined with machine learning is thus compelling: (a) the assay can be put into routine lab use without requiring expert evaluation of samples or being subject to operator bias; (b) the entire sputum sample can be rapidly analyzed; and (c) numerical analysis can capture complex interactions between lung cancer, ME, and FoC cells which would be difficult for individuals to detect reliably. Our discovery during assay development of the predictive value of viability staining density, for example, suggests a link with apoptosis that merits further study. Our model also indicates that specific immune cell populations may be involved. Neither of these predictors was on our radar before we began model building, but in retrospect the importance of both processes early in cancer development is consistent with previous reports [Earlier versions of CyPath Lung relied solely on the porphyrin to distinguish cancer from non-cancer samples [Nearly 95% of participants in this study fulfilled the criteria for lung cancer screening most recently issued by the US Preventive Services Task Force [In its 2017 Official Policy Statement, the American Thoracic Society (ATS) stated that clinical usefulness of a novel biomarker should be evaluated by estimating the minimal accuracy required for that biomarker [The ATS statement also presents a use case where screening is expanded to include participants currently ineligible for LDCT screening [