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.