title: A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer.
abstract: Background. Surfaced-enhanced laser desorption-ionization-time of flight mass spectrometry (SELDI-TOF-MS) technology plays an important role in the early diagnosis of ovarian cancer. However, the raw MS data is highly dimensional and redundant. Therefore, it is necessary to study rapid and accurate detection methods from the massive MS data. Methods. The clinical data set used in the experiments for early cancer detection consisted of 216 SELDI-TOF-MS samples. An MS analysis method based on probabilistic principal components analysis (PPCA) and support vector machine (SVM) was proposed and applied to the ovarian cancer early classification in the data set. Additionally, by the same data set, we also established a traditional PCA-SVM model. Finally we compared the two models in detection accuracy, specificity, and sensitivity. Results. Using independent training and testing experiments 10 times to evaluate the ovarian cancer detection models, the average prediction accuracy, sensitivity, and specificity of the PCA-SVM model were 83.34%, 82.70%, and 83.88%, respectively. In contrast, those of the PPCA-SVM model were 90.80%, 92.98%, and 88.97%, respectively. Conclusions. The PPCA-SVM model had better detection performance. And the model combined with the SELDI-TOF-MS technology had a prospect in early clinical detection and diagnosis of ovarian cancer.
authors.affiliation: College of Electrical Engineering and Instrumentation, Jilin University, Changchun 130061, China.
authors.forename: Jiang
authors.initials: J
authors.lastname: Wu
authors.affiliation: College of Electrical Engineering and Instrumentation, Jilin University, Changchun 130061, China.
authors.forename: Yanju
authors.identifier: 0000-0001-9134-2927
authors.initials: Y
authors.lastname: Ji
authors.affiliation: First Hospital, Jilin University, Changchun 130021, China.
authors.forename: Ling
authors.identifier: 0000-0003-0239-0631
authors.initials: L
authors.lastname: Zhao
authors.affiliation: College of Electrical Engineering and Instrumentation, Jilin University, Changchun 130061, China.
authors.forename: Mengying
authors.initials: M
authors.lastname: Ji
authors.affiliation: First Hospital, Jilin University, Changchun 130021, China.
authors.forename: Zhuang
authors.initials: Z
authors.lastname: Ye
authors.affiliation: College of Electrical Engineering and Instrumentation, Jilin University, Changchun 130061, China.
authors.forename: Suyi
authors.identifier: 0000-0002-5703-4311
authors.initials: S
authors.lastname: Li
mesh_terms: D055088:Early Detection of Cancer / Q000379:methods*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D013058:Mass Spectrometry; D010051:Ovarian Neoplasms / Q000000981:diagnostic imaging*; D025341:Principal Component Analysis; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine