Detection of early-stage lung cancer in sputum using automated flow cytometry and machine learning

• Published in: Respiratory research 2023-01, Vol.24 (1), p.23-23, Article 23
• Main Authors: Lemieux, Madeleine E, Reveles, Xavier T, Rebeles, Jennifer, Bederka, Lydia H, Araujo, Patricia R, Sanchez, Jamila R, Grayson, Marcia, Lai, Shao-Chiang, DePalo, Louis R, Habib, Sheila A, Hill, David G, Lopez, Kathleen, Patriquin, Lara, Sussman, Robert, Joyce, Roby P, Rebel, Vivienne I
• Format: Article
• Language: English
• Subjects: Analysis; Antibodies; Automated flow cytometry; Automation; Bias; Biopsy; Cancer screening; Care and treatment; Cell suspensions; Computed tomography; Data processing; Decision making; Diagnosis; Early Detection of Cancer - methods; Early-stage lung cancer; Evaluation; Flow Cytometry; Hispanic Americans; Humans; Learning algorithms; Lung; Lung cancer; Lung diseases; Lung Neoplasms - diagnostic imaging; Lung nodules; Machine Learning; Medical research; Methods; Microscopy; Model testing; Nodules; Porphyrin; Porphyrins; Prediction models; Quality control; Robustness; Sensitivity; Sensitivity analysis; Sputum
• ISSN: 1465-993X; 1465-9921; 1465-993X; 1465-9921
• DOI: 10.1186/s12931-023-02327-3

Low-dose spiral computed tomography (LDCT) may not lead to a clear treatment path when small to intermediate-sized lung nodules are identified. We have combined flow cytometry and machine learning to develop a sputum-based test (CyPath Lung) that can assist physicians in decision-making in such cases. Single cell suspensions prepared from induced sputum samples collected over three consecutive days were labeled with a viability dye to exclude dead cells, antibodies to distinguish cell types, and a porphyrin to label cancer-associated cells. The labeled cell suspension was run on a flow cytometer and the data collected. An analysis pipeline combining automated flow cytometry data processing with machine learning was developed to distinguish cancer from non-cancer samples from 150 patients at high risk of whom 28 had lung cancer. Flow data and patient features were evaluated to identify predictors of lung cancer. Random training and test sets were chosen to evaluate predictive variables iteratively until a robust model was identified. The final model was tested on a second, independent group of 32 samples, including six samples from patients diagnosed with lung cancer. Automated analysis combined with machine learning resulted in a predictive model that achieved an area under the ROC curve (AUC) of 0.89 (95% CI 0.83-0.89). The sensitivity and specificity were 82% and 88%, respectively, and the negative and positive predictive values 96% and 61%, respectively. Importantly, the test was 92% sensitive and 87% specific in cases when nodules were