Georgia Institute of Technology, Atlanta, Georgia 30332-0363, USA.
Cancer Epidemiol Biomarkers Prev. 2010 Sep;19(9):2262-71. doi: 10.1158/1055-9965.EPI-10-0126. Epub 2010 Aug 10.
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.
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.
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).
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.
The ability to accurately and inexpensively diagnose ovarian cancer will have a significant positive effect on ovarian cancer treatment and outcome.
卵巢癌的诊断存在问题，因为该病通常没有症状，尤其是在疾病进展和/或复发的早期阶段。我们在此报告一种新的质谱技术与新型支持向量机计算方法的整合，用于癌症诊断，并描述该方法在卵巢癌中的应用。
我们将高通量环境电离技术与质谱（实时直接分析质谱）相结合，分析 44 名经诊断患有浆液性乳头状卵巢癌（I-IV 期）和 50 名健康女性或良性疾病女性的血清中相对代谢物水平。将这些图谱输入定制的基于功能支持向量机的机器学习算法进行诊断分类。通过 64-30 分割验证测试和严格的留一法交叉验证来评估性能。
该检测方法在区分癌症和对照组方面具有前所未有的 99%至 100%的准确率（64-30 分割验证测试的 100%敏感性和 100%特异性；留一法交叉验证的 100%敏感性和 98%特异性）。
该方法具有显著的临床应用潜力，可作为癌症诊断工具。由于卵巢癌在普通人群中的患病率极低（约为 0.04%），因此需要进行广泛的前瞻性测试，以评估该测试在一般筛查应用中的潜在效用。然而，更直接的应用可能是作为高危人群的诊断工具，或用于监测治疗后的癌症复发。
能够准确、廉价地诊断卵巢癌，将对卵巢癌的治疗和结果产生重大积极影响。
