자료유형  

 학위논문

Control Number  

0016935066

International Standard Book Number  

9798380325066

Dewey Decimal Classification Number  

574

Main Entry-Personal Name  

Liu, Yingzhou.

Publication, Distribution, etc. (Imprint  

[S.l.] : The University of Wisconsin - Madison., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(198 p.)

General Note  

Source: Dissertations Abstracts International, Volume: 85-03, Section: B.

General Note  

Advisor: Yu, Menggang.

Dissertation Note  

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2023.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약Cancer staging is a crucial process that determines the severity of an individual's cancer based on specific risk factors and clinical outcomes, such as time-to-event outcomes or the presence of a disease. This process involves classifying a heterogeneous set of cancer patients into several homogeneous groups. Accurately classifying the cancer stages helps doctors identify patients for clinical trials, understand the disease's severity and prognosis, and facilitate clinical decision-making on therapy and surveillance.Tree methods have emerged as promising tools for cancer staging due to their ease of interpretation and ability to handle complex datasets with minimal assumptions [Bre+17; LWC13; Lin+16]. However, integrating multiple risk factors into cancer staging using tree methods presents several challenges. First, it is unclear how to leverage the ordering indicated by ordinal risk factors. Second, with a high number of categories defined by risk factors, it remains unknown whether patients in each category have a distinct prognosis. If not, it is unclear how to combine them into one stage. Finally, allowing a general grouping pattern is challenging, as most approaches have restrictions on the patterns of groupings. For instance, the classification and regression tree (CART) method only permits straight-line groupings on a partially ordered two-way grid.To address the limitations of tree methods with ordinal variables, we introduce a new method: Ordering Partially Ordered Set Elements by Recursive Amalgamation (OPERA)[Wan20]. This approach utilizes ordered information and accommodates general grouping patterns. OPERA, combined with pruning, demonstrates improved performance compared to traditional tree methods without pruning. This data-driven tool simplifies staging by condensing multiple groups into a single stratum based on a distinct prognosis, enhancing ease of use and interpretation. A well-trained tool can also accurately classify cancer stage and predict clinical outcomes. Beyond cancer staging, this method has implications for clustering in healthcare, aiding the identification of homogeneous patients for clinical trials and resource prioritization.

Subject Added Entry-Topical Term  

Biostatistics.

Subject Added Entry-Topical Term  

Oncology.

Subject Added Entry-Topical Term  

Bioinformatics.

Index Term-Uncontrolled  

Cancer staging

Index Term-Uncontrolled  

Iteratively reweighted least Squares

Index Term-Uncontrolled  

Ordinal risk factors

Index Term-Uncontrolled  

Partial ordering

Index Term-Uncontrolled  

Pruning

Added Entry-Corporate Name  

The University of Wisconsin - Madison Biomedical Data Science

Host Item Entry  

Dissertations Abstracts International. 85-03B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:640807