|Awarded On||February 15, 2023|
|Title||Integrate whole slide imaging and genomic data to study pediatric rhabdomyosarcoma|
|Award Mechanism||Individual Investigator Research Awards for Cancer in Children and Adolescents|
|Institution/Organization||The University of Texas Southwestern Medical Center|
|Principal Investigator/Program Director||Guanghua Xiao|
Rhabdomyosarcoma (RMS), which usually begins in muscles that are attached to bones, is the most common type of soft tissue sarcoma in children. Having an outlook of the patient prognosis is crucial for determining treatment options. The objective of this proposal is to design and develop deep learning tools to provide RMS patient prognosis prediction from whole slide images (WSIs), genetic data and clinical data. 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 real...