Background: The impact of cancer in society created the necessity of new and faster theoretical models for the early diagnosis of cancer.

Methods: In this work, a mass spectrometry (MS) data analysis method based on the star-like graph of protein and support vector machine (SVM) was proposed and applied to the ovarian cancer early classification in the MS data set. Firstly, the MS data is reduced and transformed into the corresponding protein sequence. Then, the topological indexes of the star-like graph are calculated to describe each MS data of the cancer sample. Finally, the SVM model is suggested to classify the MS data.

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 model were 96.45%, 96.88%, and 95.67%, respectively, for [0,1] normalization data, and 94.43%, 96.25%, and 91.11% for [-1,1] normalization data.

Conclusion: The model combined with the SELDI-TOF-MS technology has a prospect in early clinical detection and diagnosis of ovarian cancer.

背景：癌症对社会的影响为癌症的早期诊断创造了新的，更快的理论模型的必要性。

方法：在这项工作中，提出了一种基于蛋白质星形图和支持向量机（SVM）的质谱（MS）数据分析方法，并将其应用于MS数据集中的卵巢癌早期分类。首先，将MS数据还原并转化为相应的蛋白质序列。然后，计算星形图的拓扑指数以描述癌症样本的每个MS数据。最后，建议使用SVM模型对MS数据进行分类。

结果：使用独立训练和测试实验10次评估卵巢癌检测模型，对于[0,1]标准化，该模型的平均预测准确性，敏感性和特异性分别为96.45％，96.88％和95.67％。数据，对于[-1,1]归一化数据，分别为94.43％，96.25％和91.11％。

结论：该模型与SELDI-TOF-MS技术相结合，对卵巢癌的早期临床检测和诊断具有广阔的前景。