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

> **NIH NIH U01** · UT SOUTHWESTERN MEDICAL CENTER · 2022 · $246,000

## Abstract

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.

## Key facts

- **NIH application ID:** 10594240
- **Project number:** 3U01CA249245-02S1
- **Recipient organization:** UT SOUTHWESTERN MEDICAL CENTER
- **Principal Investigator:** Guanghua Xiao
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $246,000
- **Award type:** 3
- **Project period:** 2021-09-15 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10594240

## Citation

> US National Institutes of Health, RePORTER application 10594240, Informatics Tools To Analyze And Model Whole Slide Image Data At The Single Cell Level (3U01CA249245-02S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10594240. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*