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Statistical Methods for Variable Selection and Prediction With Pathomic Features
Statistical Methods for Variable Selection and Prediction With Pathomic Features
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103108
- ISBN
- 9798280759596
- DDC
- 574
- 저자명
- Rubin, Jeremy.
- 서명/저자
- Statistical Methods for Variable Selection and Prediction With Pathomic Features
- 발행사항
- [Sl] : University of Pennsylvania, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 111 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
- 주기사항
- Advisor: Zee, Jarcy.
- 학위논문주기
- Thesis (Ph.D.)--University of Pennsylvania, 2025.
- 초록/해제
- 요약The current gold standard for kidney disease diagnoses is manual visual assessment by pathologists of kidney biopsy tissue. However, these manual assessments are time-consuming, subjective, and lack reproducibility. The generation of many quantitative features from digital whole slide images (WSI) of biopsy tissue, an emerging field known as pathomics, may allow for the identification of novel, objective and comprehensive biomarkers of kidney disease as well as better prediction of kidney function outcomes. For each subject's WSI, the same pathomic features are computed for each histologic object that has been identified and segmented by a deep learning model. In Chapters 2-3, we develop novel regression approaches to predict continuous outcomes of kidney function from these unbalanced, matrix-valued pathomic features and identify which features are most informative of the outcome. We illustrate in these chapters that our approaches can identify the informative pathomic features in simulation studies and provide improved predictive accuracy of kidney function outcomes in real data analyses of image features from patients with glomerular disease. In Chapter 4, we use the conformal prediction statistical framework to construct individual prediction intervals for continuous kidney function outcomes from the pathomic features. Through simulations and analysis of glomerular disease image feature data, we highlight that these prediction intervals reliably cover the true continuous kidney function outcomes. These statistical methods provide a suite of tools for discovering new pathomic biomarkers of kidney disease as well as predicting and quantifying the uncertainty of continuous kidney function outcomes.
- 일반주제명
- Biostatistics
- 일반주제명
- Biomedical engineering
- 일반주제명
- Histology
- 일반주제명
- Pathology
- 키워드
- Kidney disease
- 키워드
- Kidney function
- 기타저자
- University of Pennsylvania Epidemiology and Biostatistics
- 기본자료저록
- Dissertations Abstracts International. 86-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520260202103108
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■007cr#unu||||||||
■020 ▼a9798280759596
■035 ▼a(MiAaPQ)AAI31935894
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574
■1001 ▼aRubin, Jeremy.
■24510▼aStatistical Methods for Variable Selection and Prediction With Pathomic Features
■260 ▼a[Sl]▼bUniversity of Pennsylvania▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a111 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-12, Section: B.
■500 ▼aAdvisor: Zee, Jarcy.
■5021 ▼aThesis (Ph.D.)--University of Pennsylvania, 2025.
■520 ▼aThe current gold standard for kidney disease diagnoses is manual visual assessment by pathologists of kidney biopsy tissue. However, these manual assessments are time-consuming, subjective, and lack reproducibility. The generation of many quantitative features from digital whole slide images (WSI) of biopsy tissue, an emerging field known as pathomics, may allow for the identification of novel, objective and comprehensive biomarkers of kidney disease as well as better prediction of kidney function outcomes. For each subject's WSI, the same pathomic features are computed for each histologic object that has been identified and segmented by a deep learning model. In Chapters 2-3, we develop novel regression approaches to predict continuous outcomes of kidney function from these unbalanced, matrix-valued pathomic features and identify which features are most informative of the outcome. We illustrate in these chapters that our approaches can identify the informative pathomic features in simulation studies and provide improved predictive accuracy of kidney function outcomes in real data analyses of image features from patients with glomerular disease. In Chapter 4, we use the conformal prediction statistical framework to construct individual prediction intervals for continuous kidney function outcomes from the pathomic features. Through simulations and analysis of glomerular disease image feature data, we highlight that these prediction intervals reliably cover the true continuous kidney function outcomes. These statistical methods provide a suite of tools for discovering new pathomic biomarkers of kidney disease as well as predicting and quantifying the uncertainty of continuous kidney function outcomes.
■590 ▼aSchool code: 0175.
■650 4▼aBiostatistics
■650 4▼aBiomedical engineering
■650 4▼aHistology
■650 4▼aPathology
■653 ▼aKidney disease
■653 ▼aKidney biopsy tissue
■653 ▼aPathomic features
■653 ▼aGlomerular disease
■653 ▼aKidney function
■690 ▼a0308
■690 ▼a0541
■690 ▼a0414
■690 ▼a0571
■71020▼aUniversity of Pennsylvania▼bEpidemiology and Biostatistics.
■7730 ▼tDissertations Abstracts International▼g86-12B.
■790 ▼a0175
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17356960▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
