Diagnosis of Non-small Cell Lung Cancer for Early Stage Asymptomatic Patients
| Autor: | Thomas C. Long, Osita Onugha, Cherylle Goebel, Robert Mckenna, Andrew Wachtel, Christopher Louden |
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| Rok vydání: | 2019 |
| Předmět: |
Adult
Male Oncology Cancer Research medicine.medical_specialty Lung Neoplasms Stage ii Biochemistry Asymptomatic 03 medical and health sciences 0302 clinical medicine Carcinoma Non-Small-Cell Lung Internal medicine Genetics medicine False positive paradox Carcinoma Humans Multiplex Lung cancer Molecular Biology Early Detection of Cancer Neoplasm Staging medicine.diagnostic_test business.industry medicine.disease 030220 oncology & carcinogenesis Immunoassay Female Non small cell medicine.symptom business Research Article |
| Zdroj: | Cancer Genomics - Proteomics. 16:229-244 |
| ISSN: | 1790-6245 1109-6535 |
| DOI: | 10.21873/cgp.20128 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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