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A sputum-based approach (CyPath Lung) combines flow cytometry with machine learning to help physicians distinguish cancer from non-cancer. Induced sputum from high-risk participants was processed with viability exclusion, cell-type antibodies, and a cancer-associated porphyrin label, then modeled from 150 patients and tested on an independent cohort.",{"@graph":69,"@context":115},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/detection-of-early-stage-lung-cancer-in-sputum-using-automated-flow-cytometry-and-machine-learning/377518/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/detection-of-early-stage-lung-cancer-in-sputum-using-automated-flow-cytometry-and-machine-learning/377518.png","ImageObject",300,407,{"name":92,"@type":93},"RuangKosong","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109],{"name":110,"@type":111,"acceptedAnswer":112},"What diagnostic performance did the automated model achieve?","Question",{"text":113,"@type":114},"The model reached an AUC of 0.89 with sensitivity of 82% and specificity of 88%. 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Respiratory Research (2023) 24:23 [https://doi.org/10.1186/s12931-023-02327-3](https://doi.org/10.1186/s12931-023-02327-3)  \nRespiratory Research  \n RESEARCH Open Access  \nDetection of early-stage lung cancer in sputum using automated flow cytometry and machine learning  \nMadeleine E. Lemieux 1, Xavier T. Reveles2, Jennifer Rebeles2, Lydia H. Bederka2, Patricia R. Araujo2, Jamila R. Sanchez2, Marcia Grayson2, Shao‑Chiang Lai2, Louis R. DePalo3, Sheila A. Habib4, David G. Hill5, Kathleen Lopez6, Lara Patriquin6,7, Robert Sussman8, Roby P. Joyce9 and Vivienne I. Rebel2*  \nAbstract  \nBackground 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.  \nMethods 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, independ‑ ent group of 32 samples, including six samples from patients diagnosed with lung cancer.  \nResults 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 \u003C 20 mm (AUC of 0 . 94; 95% CI 0 .89–0. 99) . Testing of the model on an independent second set of samples showed an AUC of 0.85 (95% CI 0.71–0. 98) with an 83% sensitivity, 77% specific‑ ity, 95% negative predictive value and 45% positive predictive value. The model is robust to differences in sample processing and disease state.  \nConclusion CyPath Lung correctly classifies samples as cancer or non‑cancer with high accuracy, including from participants at different disease stages and with nodules \u003C 20 mm in diameter. This test is intended for use after lung cancer screening to improve early‑stage lung cancer diagnosis.  \nTrial [registration ClinicalTrials.gov](registration ClinicalTrials.gov) ID: NCT03457415; March 7, 2018  \nKeywords Sputum, Automated flow cytometry, Machine learning, Porphyrin, Early‑stage lung cancer  \n*Correspondence: Vivienne I. Rebel [vr@bioaffinitytech.com](vr@bioaffinitytech.com)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a c","cbCail2O3CtvZUNg","https://ap.wps.com/l/cbCail2O3CtvZUNg","pdf",4154804,16,"English","# Abstract\n## Background\n## Methods\n## Results\n## Conclusion\n# Background\n## Screening rationale and limitations\n## Need for non-invasive follow-up tests","[{\"question\":\"What diagnostic performance did the automated model achieve?\",\"answer\":\"The model reached an AUC of 0.89 with sensitivity of 82% and specificity of 88%. For nodules under 20 mm, performance increased to 92% sensitivity and 87% specificity, and results were supported in an independent second test set.\"}]","Detection of early-stage lung cancer in sputum using automated flow cytometry and machine learning | PDF",1790225846]