早期准确有效的检测卵巢癌对于确保患者接受适当治疗至关重要。在早期诊断研究中被研究的一线疗法之一是从蛋白质质谱中提取的特征。然而，这种方法仅考虑了特定的谱响应子集，忽略了蛋白质表达水平之间的相互作用，这些相互作用也可能包含诊断信息。我们提出了一种新的方法，自动搜索蛋白质质谱中的可区分特征，并考虑谱的自相似性质。自相似性的评估是通过对蛋白质质谱进行小波分解，并估计由结果小波系数的能量的逐级衰减率来完成的。采用距离方差的鲁棒方式估计逐级能量，并通过滚动窗口方法在局部估计速率。这样形成一组速率，可用于表征蛋白质之间的相互作用，这可能表明癌症的存在。然后从这些演化速率中选择可区分的描述符，并用作分类特征。所提出的基于小波的特征与现有文献提出的特征一起，在美国国家癌症研究所发表的两个数据集中用于卵巢癌早期诊断。加入新模式的小波特征，可以提高早期卵巢癌检测的诊断性能。这证明了所提出模式表征新的卵巢癌诊断信息的能力。©2023作者。Statistics in Medicine由John Wiley＆Sons Ltd出版。

Accurate and efficient detection of ovarian cancer at early stages is critical to ensure proper treatments for patients. Among the first-line modalities investigated in studies of early diagnosis are features distilled from protein mass spectra. This method, however, considers only a specific subset of spectral responses and ignores the interplay among protein expression levels, which can also contain diagnostic information. We propose a new modality that automatically searches protein mass spectra for discriminatory features by considering the self-similar nature of the spectra. Self-similarity is assessed by taking a wavelet decomposition of protein mass spectra and estimating the rate of level-wise decay in the energies of the resulting wavelet coefficients. Level-wise energies are estimated in a robust manner using distance variance, and rates are estimated locally via a rolling window approach. This results in a collection of rates that can be used to characterize the interplay among proteins, which can be indicative of cancer presence. Discriminatory descriptors are then selected from these evolutionary rates and used as classifying features. The proposed wavelet-based features are used in conjunction with features proposed in the existing literature for early stage diagnosis of ovarian cancer using two datasets published by the American National Cancer Institute. Including the wavelet-based features from the new modality results in improvements in diagnostic performance for early-stage ovarian cancer detection. This demonstrates the ability of the proposed modality to characterize new ovarian cancer diagnostic information.© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.