基于小波包缩放描述符的卵巢癌诊断
Ovarian Cancer Diagnostics using Wavelet Packet Scaling Descriptors
AI总结：
针对现有卵巢癌早期诊断方法忽略蛋白表达互作的问题，提出基于小波包缩放描述符的诊断方法，经美国国家癌症研究所数据集验证，其中一种技术在单数据集上准确率优于传统小波方法，为卵巢癌早诊提供新方向。
摘要：
准确高效地检测早期卵巢癌对于及时治疗至关重要。目前研究人员已探索了多种早期诊断方法，其中包括聚焦于从蛋白质质谱中提取特征的方法，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，通过分析蛋白质质谱的固有缩放特性，自动在这些谱图中搜寻诊断特征。我们对比了两种利用信号的小波包分解缩放行为来估计信号自相似性的技术。这些方法通过滑动窗口法应用于质谱数据，生成一组捕捉蛋白质相互作用的自相似性指数，这些指数可能是卵巢癌的潜在表征。随后，从这组指数中选出最具区分度的缩放描述符，用于分类算法。为评估这些方法在卵巢癌早期诊断中的有效性，我们将其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对比评估显示，其中一种基于小波包的技术在其中一个分析数据集上提升了诊断性能（测试准确率分别为95.67%与96.78%）。这凸显了基于小波包的方法在捕捉卵巢癌相关新型诊断信息方面的潜力。这种创新方法有望实现更优的卵巢癌早期检测，改善患者预后。
英文摘要：
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.
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