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

项目摘要 横纹肌肉瘤（RMS）是儿童期最常见的软组织肿瘤， 美国的正确分类RMS亚型并对患者预后进行展望是至关重要的 决定治疗方案。本提案的目标是设计和开发信息学工具， 亚型分类和患者预后预测。这背后的理由是， 建议是，深度学习工具的开发将提供客观的测量和判断， 疾病，并使病理学家和医生更好地了解，以作出准确的诊断和治疗建议。的 目标将通过追求两个具体目标来实现：（1）开发信息学工具来分析整个载玻片成像数据， 小儿RMS。(2)开发和验证基于病理图像的RMS结局预测模型。拟议研究 它的完成将提供可行的工具，以帮助病理学家和医生，以提高RMS的诊断 和治疗，它可以扩展到其他恶性疾病。总之，我们已经收集了一个多- 学科研究团队，具有互补的研究专长。我们将充分利用发展， 母公司NCI ITCR U 01授权1U 01 CA 249245，“用于分析和建模单个切片图像数据的信息学工具 细胞水平”（资助期：09/01/2021 - 08/31/2024）。我们还将充分利用我们积累的数据和广泛的 解决病理成像分析和结果的计算算法开发挑战的经验 儿童RMS的预测。这将极大地促进个体RMS患者的治疗计划， 对临床护理产生重要影响。

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.