Monitoring and early warning of ovarian cancer using high-dimensional non-parametric EWMA control chart based on sliding window

Author:

Wu Bin1,Zhong Wen2,Ren Yixing3,Zhou Zhongli1,Liu Liu1

Affiliation:

1. Chengdu University of Technology

2. Sichuan Tourism University

3. Affiliated Hospital of North Sichuan Medical College

Abstract

Abstract

Ovarian tumors are one of the common ovarian dysfunctions in women, which to some extent affect women's normal work and life. Although ovarian tumors are usually sensitive to chemotherapy, and typically show initial efficacy with platinum/taxane treatment, the postoperative recurrence rate in advanced patients is very high. There have been a considerable number of researchers working to establish new methods to monitor and warn about the progression and prognosis of this malignant tumor disease. One commonly used approach is to first reduce the dimensionality of the data using methods such as principal component analysis, LASSO, deep learning, etc., to select several features most relevant to malignant tumors. Then, either a one-dimensional control chart is used for multiple monitoring and warning of different indicators, or a multivariate control chart is directly used to monitor and warn about the selected indicators. However, in actual data, different features are not completely independent of each other. Using a one-dimensional control chart for multiple monitoring and warning of different indicators may overlook the interactions between different features. Additionally, reducing the dimensionality of the data may result in the loss of some data information, leading to the omission of details, which may affect the accuracy of the model and result in delayed alarms and poor predictive performance. Therefore, this paper proposes a non-parametric monitoring scheme based on high-dimensional empirical likelihood test and sliding window model of EWMA type, for direct online monitoring and warning of high-dimensional ovarian tumor data. We compared this approach with a multivariate EWMA control chart after dimensionality reduction, and Monte Carlo numerical simulation results showed that the high-dimensional non-parametric EWMA monitoring scheme indeed detects tumor data changes faster and issues alarms more promptly than the dimensionality-reduced multivariate EWMA control chart. Furthermore, we further validated the effectiveness of the high-dimensional non-parametric EWMA monitoring scheme in monitoring and warning using tumor resection data from the Third Affiliated Hospital of Soochow University as an example.

Publisher

Research Square Platform LLC

Reference35 articles.

1. Global scenario on ovarian cancer–Its dynamics, relative survival, treatment, and epidemiology;Shabir S;Adesh University Journal of Medical Sciences & Research,2020

2. Risk factors for epithelial ovarian carcinoma in India: a case control study in low-incidence population;Shanmughapriya S;International Journal of Cancer Research,2016

3. Ovarian cancer statistics, 2018;Torre LA;CA: a cancer journal for clinicians,2018

4. The American Cancer Society’s facts & figures: 2020 edition;Viale PH;Journal of the Advanced Practitioner in Oncology,2020

5. Ovarian cancer[J];Jayson GC;The Lancet,2014

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3