| Autor: |
John F. McDonald, Facundo M. Fernández, Alexander Gray, Benedict B. Benigno, Roman Mezencev, L. DeEtte Walker, Wei Guan, Manshui Zhou |
| Rok vydání: |
2023 |
| DOI: |
10.1158/1055-9965.c.6515089 |
| Popis: |
Background: Ovarian cancer diagnosis is problematic because the disease is typically asymptomatic, especially at the early stages of progression and/or recurrence. We report here the integration of a new mass spectrometric technology with a novel support vector machine computational method for use in cancer diagnostics, and describe the application of the method to ovarian cancer.Methods: We coupled a high-throughput ambient ionization technique for mass spectrometry (direct analysis in real-time mass spectrometry) to profile relative metabolite levels in sera from 44 women diagnosed with serous papillary ovarian cancer (stages I-IV) and 50 healthy women or women with benign conditions. The profiles were input to a customized functional support vector machine–based machine-learning algorithm for diagnostic classification. Performance was evaluated through a 64-30 split validation test and with a stringent series of leave-one-out cross-validations.Results: The assay distinguished between the cancer and control groups with an unprecedented 99% to 100% accuracy (100% sensitivity and 100% specificity by the 64-30 split validation test; 100% sensitivity and 98% specificity by leave-one-out cross-validations).Conclusion: The method has significant clinical potential as a cancer diagnostic tool. Because of the extremely low prevalence of ovarian cancer in the general population (∼0.04%), extensive prospective testing will be required to evaluate the test's potential utility in general screening applications. However, more immediate applications might be as a diagnostic tool in higher-risk groups or to monitor cancer recurrence after therapeutic treatment.Impact: The ability to accurately and inexpensively diagnose ovarian cancer will have a significant positive effect on ovarian cancer treatment and outcome. Cancer Epidemiol Biomarkers Prev; 19(9); 2262–71. ©2010 AACR. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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