Detecting early-stage ovarian cancer accurately and efficiently is crucial for timely treatment. Various methods for early diagnosis have been explored, including a focus on features derived from protein mass spectra, but these tend to overlook the complex interplay across protein expression levels. We propose an innovative method to automate the search for diagnostic features in these spectra by analyzing their inherent scaling characteristics. We compare two techniques for estimating the self-similarity in a signal using the scaling behavior of its wavelet packet decomposition. The methods are applied to the mass spectra using a rolling window approach, yielding a collection of self-similarity indexes that capture protein interactions, potentially indicative of ovarian cancer. Then, the most discriminatory scaling descriptors from this collection are selected for use in classification algorithms. To assess their effectiveness for early diagnosis of ovarian cancer, the techniques are applied to two datasets from the American National Cancer Institute. Comparative evaluation against an existing wavelet-based method shows that one wavelet packet-based technique led to improved diagnostic performance for one of the analyzed datasets (95.67% vs. 96.78% test accuracy, respectively). This highlights the potential of wavelet packet-based methods to capture novel diagnostic information related to ovarian cancer. This innovative approach offers promise for better early detection and improved patient outcomes in ovarian cancer.

翻译：准确高效地检测早期卵巢癌对于及时治疗至关重要。目前已探索多种早期诊断方法，包括关注源自蛋白质质谱的特征，但这些方法往往忽略了蛋白质表达水平之间复杂的相互作用。我们提出了一种创新方法，通过分析质谱中固有的尺度特征来自动搜索诊断特征。我们比较了两种利用小波包分解的尺度行为估计信号自相似性的技术。这些方法采用滑动窗口方式应用于质谱，得到一组捕捉蛋白质相互作用的自相似性指标，这些指标可能指示卵巢癌。随后，从该集合中选取最具判别性的尺度描述符用于分类算法。为评估其在卵巢癌早期诊断中的有效性，我们将这些技术应用于美国国家癌症研究所的两个数据集。与现有基于小波的方法进行对比评估表明，对于其中一个分析数据集，基于小波包的方法提升了诊断性能（测试准确率分别为95.67%与96.78%）。这突显了基于小波包的方法在捕捉与卵巢癌相关的新诊断信息方面的潜力。这种创新方法有望改善卵巢癌的早期检测并提升患者预后。