Wu Bin, Zhong Wen, Ren Yixing, Zhou Zhongli, Liu Liu
College of Management Science, Chengdu University of Technology, Chengdu, 610059, China.
School of Big Data and Statistics, Sichuan Tourism College, Chengdu, 610100, China.
Sci Rep. 2025 Mar 17;15(1):9046. doi: 10.1038/s41598-025-86576-w.
Ovarian tumors are a common ovarian dysfunction that affects women's daily lives. Although ovarian tumors are generally sensitive to chemotherapy and initially respond well to platinum/taxane-based treatments, the postoperative recurrence rate remains high in advanced cases. Many researchers are dedicated to developing new methods for monitoring and predicting malignant tumors. Traditional approaches use dimensionality reduction techniques, like principal component analysis and deep learning, to select relevant features, followed by univariate or multivariate control charts for monitoring. However, these methods may overlook interactions between features and dimensionality reduction can result in loss of information, potentially affecting the accuracy of the model and leading to delayed alerts and reduced predictive performance. Therefore, this paper develops a new sliding window EWMA control chart based on high-dimensional empirical likelihood ratio tests. This control chart not only monitors data with unknown underlying distributions but is also applicable to high-dimensional data, allowing for monitoring without dimensionality reduction, thus simplifying the process and avoiding information loss. Monte Carlo results show that this method detects changes in indicators and issues alerts more rapidly than the dimensionality-reduced multivariate EWMA control charts. In addition, we further validated the effectiveness of this method through analysis of a tumor resection data example.
卵巢肿瘤是一种常见的卵巢功能障碍，会影响女性的日常生活。虽然卵巢肿瘤通常对化疗敏感，并且最初对铂类/紫杉烷类治疗反应良好，但在晚期病例中，术后复发率仍然很高。许多研究人员致力于开发监测和预测恶性肿瘤的新方法。传统方法使用降维技术，如主成分分析和深度学习，来选择相关特征，然后使用单变量或多变量控制图进行监测。然而，这些方法可能会忽略特征之间的相互作用，并且降维可能会导致信息丢失，从而可能影响模型的准确性，导致警报延迟和预测性能下降。因此，本文基于高维经验似然比检验开发了一种新的滑动窗口EWMA控制图。该控制图不仅可以监测具有未知潜在分布的数据，还适用于高维数据，无需降维即可进行监测，从而简化了过程并避免了信息丢失。蒙特卡罗结果表明，该方法比降维后的多变量EWMA控制图能更快地检测指标变化并发出警报。此外，我们通过分析一个肿瘤切除数据实例进一步验证了该方法的有效性。
