A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer.

Jiang Wu,Ling Zhao,Suyi Li,Yanju Ji,Mengying Ji,Zhuang Ye

doi:10.1155/2016/6169249

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.

Highlights

The mortality of ovarian cancer ranks first in female genital malignancies; owing to the fact of being uneasy to find, the 5-year survival rate is only about 30% [1]
Probability principal component analysis (PCA) (PPCA) restricts the factor loading matrix with a noise variance estimation using the principle components ignored by the traditional PCA and obtains the optimal probability model through the estimated parameters by the expectation-maximization algorithm
According to the above analysis, we focused on the design of an automatic model using principal components analysis (PPCA) and support vector machine (SVM) technique for the ovarian cancer identification from MS data

Summary

Introduction

The mortality of ovarian cancer ranks first in female genital malignancies; owing to the fact of being uneasy to find, the 5-year survival rate is only about 30% [1]. 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. 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. 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. Those of the PPCA-SVM model were 90.80%, 92.98%, and 88.97%, respectively. The model combined with the SELDI-TOF-MS technology had a prospect in early clinical detection and diagnosis of ovarian cancer

Methods

Results

Conclusion