权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Informatics Tools To Analyze And Model Whole Slide Image Data At The Single Cell Level

在单细胞水平上分析和建模整个幻灯片图像数据的信息学工具

基本信息

批准号：
10594240
负责人：
Guanghua Xiao
金额：
$ 24.6万
依托单位：
UT SOUTHWESTERN MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-15 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10594240
关键词：
Algorithms Alveolar Architecture Artificial Intelligence Biological Biological Markers Cells Characteristics Child Childhood Childhood Rhabdomyosarcoma Classification Clinical Clinical Data Communities Computational algorithm Computer Analysis Data Data Analyses Data Set Development Diagnosis Disease Elements Failure Funding Genomics Goals Grant Histologic Histology Image Individual Interdisciplinary Study Lead Malignant - descriptor Malignant Childhood Neoplasm Malignant Neoplasms Measurement Modeling Modernization Morphology Muscle Parents Pathologic Pathologist Patient-Focused Outcomes Patients Pediatric Oncology Group Performance Physicians Play Prospective Studies Recurrent disease Research Research Institute Resolution Rhabdomyosarcoma Risk Role Soft Tissue Neoplasms Soft tissue sarcoma Suggestion Survival Rate Technology Texas Tissues United States arm base bone cancer prevention cell type clinical care cohort data integration deep learning deep learning model design digital imaging disorder risk experience high risk image processing improved individual patient individualized medicine informatics tool insight model development novel outcome prediction pathology imaging patient prognosis patient stratification pediatric patients precision medicine predictive marker predictive modeling prognostic model risk stratification tool treatment planning tumor heterogeneity user-friendly whole slide imaging

项目摘要

Project Summary Rhabdomyosarcoma (RMS), the most common soft tissue tumor in childhood, occurs in 350 children annually in the United States. Correctly classifying the RMS subtypes and having an outlook for patient prognosis is crucial for determining treatment options. The objective of this proposal is to design and develop informatics tools to provide RMS subtype classification and patient prognosis prediction from whole slide images (WSIs). The rationale underlying this proposal is that the development of the deep learning tools will provide objective measurements and judgements of the disease and make pathologists and physicians better informed to make precise diagnosis and treatment suggestions. The goal will be realized by pursuing two specific aims: (1) Develop informatics tools to analyze whole slide imaging data for pediatric RMS. (2) Develop and validate pathology image-based RMS outcome prediction models. The proposed research is significant as the completion of it will provide viable tools to aid pathologists and physicians to improve RMS diagnosis and treatments, and it could be extendable to other malignant diseases. In summary, we have assembled a multi- disciplinary research team with complementary research expertise. We will fully leverage the development from the parent NCI ITCR U01 grant 1U01CA249245, “Informatics Tools To Analyze And Model Whole Slide Image Data At The Single Cell Level” (Funding Period: 09/01/2021 – 08/31/2024). We will also fully utilize our accumulated data and extensive experience to solve the challenge of developing computational algorithms for pathology imaging analysis and outcome prediction for pediatric RMS. This will greatly facilitate treatment planning for individual RMS patients and will have an important impact on clinical care.

项目总结