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 (approximately 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. (c)2010 AACR.
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 (approximately 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. (c)2010 AACR.
Authors: Victoria O Shender; Marat S Pavlyukov; Rustam H Ziganshin; Georgij P Arapidi; Sergey I Kovalchuk; Nikolay A Anikanov; Ilya A Altukhov; Dmitry G Alexeev; Ivan O Butenko; Alexey L Shavarda; Elena B Khomyakova; Evgeniy Evtushenko; Lev A Ashrafyan; Irina B Antonova; Igor N Kuznetcov; Alexey Yu Gorbachev; Mikhail I Shakhparonov; Vadim M Govorun Journal: Mol Cell Proteomics Date: 2014-09-30 Impact factor: 5.911
Authors: A W L Bayci; D A Baker; A E Somerset; O Turkoglu; Z Hothem; R E Callahan; R Mandal; B Han; T Bjorndahl; D Wishart; R Bahado-Singh; S F Graham; R Keidan Journal: Metabolomics Date: 2018-08-03 Impact factor: 4.290
Authors: Matthew F Buas; Haiwei Gu; Danijel Djukovic; Jiangjiang Zhu; Charles W Drescher; Nicole Urban; Daniel Raftery; Christopher I Li Journal: Gynecol Oncol Date: 2015-10-30 Impact factor: 5.482