PT - JOURNAL ARTICLE AU - CHERYLLE GOEBEL AU - CHRISTOPHER L. LOUDEN AU - ROBERT MCKENNA, Jr. AU - OSITA ONUGHA AU - ANDREW WACHTEL AU - THOMAS LONG TI - Diagnosis of Non-small Cell Lung Cancer for Early Stage Asymptomatic Patients AID - 10.21873/cgp.20128 DP - 2019 Jul 01 TA - Cancer Genomics - Proteomics PG - 229--244 VI - 16 IP - 4 4099 - http://cgp.iiarjournals.org/content/16/4/229.short 4100 - http://cgp.iiarjournals.org/content/16/4/229.full SO - Cancer Genomics Proteomics2019 Jul 01; 16 AB - Background/Aim: In 2016 in the United States, 7 of 10 patients were estimated to die following lung cancer diagnosis. This is due to a lack of a reliable screening method that detects early-stage lung cancer. Our aim is to accurately detect early stage lung cancer using algorithms and protein biomarkers. Patients and Methods: A total of 1,479 human plasma samples were processed using a multiplex immunoassay platform. 82 biomarkers and 6 algorithms were explored. There were 351 NSCLC samples (90.3% Stage I, 2.3% Stage II, and 7.4% Stage III/IV). Results: We identified 33 protein biomarkers and developed a classifier using Random Forest. Our test detected early-stage Non-Small Cell Lung Cancer (NSCLC) with a 90% accuracy, 80% sensitivity, and 95% specificity in the validation set using the 33 markers. Conclusion: A specific, non-invasive, early-detection test, in combination with low-dose computed tomography, could increase survival rates and reduce false positives from screenings.