A System for Xerostomia Risk Classification after Head & Neck Cancer Radiotherapy

头后口干症风险分类系统

• 批准号: 10255864
• 负责人: Pranav Lakshminarayanan
• 金额: $ 39.99万
• 依托单位: ONCOSPACE, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10255864
• 关键词: Acceleration; Aftercare; Agreement; Anatomy; Architecture; Award; Benchmarking; Classification; Clinical; Collaborations; Common Terminology Criteria for Adverse Events; Community Clinical Oncology Program; Computer software; Data; Data Science; Data Set; Databases; Decision Trees; Development; Disease; Dose; Eating; Economic Burden; Engineering; Ensure; Evaluation; Event; Feedback; Gland; Guidelines; Head Cancer; Head and Neck Cancer; Health Insurance Portability and Accountability Act; Healthcare; Image; Injury; Institution; Judgment; Knowledge; Label; Legal patent; Licensing; Machine Learning; Medical Records; Medicine; Modeling; Morbidity - disease rate; Neck Cancer; Organ; Outcome; Patients; Performance; Phase; Plant Leaves; Population; Positioning Attribute; Prevalence; Probability; Process; Quality of life; ROC Curve; Radiation; Radiation Oncologist; Radiation Oncology; Radiation therapy; Randomized Clinical Trials; Records; Reporting; Risk; Safety; Salivary Glands; Sensitivity and Specificity; Site; Small Business Innovation Research Grant; System; Technology; Testing; Time; Toxic effect; Training; Universities; Validation; Visit; Work; Xerostomia; base; cancer radiation therapy; cancer therapy; classification trees; clinical application; clinical decision support; cloud based; commercialization; data curation; design; follow-up; head and neck cancer patient; high risk; improved; inclusion criteria; individual patient; innovation; irradiation; model building; patient subsets; predictive modeling; prevent; product development; quantitative imaging; radiation-induced injury; radiomics; regression trees; side effect; software as a service; standard of care; treatment planning

Summary Radiotherapy (RT) is a major component in the treatment of most head and neck cancer (HNC) cases. During irradiation, sensitive regions such as the salivary glands can sustain injury, resulting in xerostomia (dry mouth). This side effect is common and can significantly reduce quality of life during and post-treatment. The focus of this application is prediction during treatment planning of whether patients will suffer high-grade xerostomia (NCI CTCAE Grade 2-3) at the time of their first post-treatment follow-up visit, typically 3-6 months after RT (prevalence is approximately 40%). Predictions will enable clinicians to carry out treatment planning with improved knowledge of the likelihood of high-grade xerostomia development and allow better-informed and more timely anticipation of consequences such as eating difficulty. In this Phase 1 project, Oncospace Inc. will develop a Classification and Regression Tree (CART) prediction model using over 1200 complete HNC patient records. Associations between high-grade xerostomia and a wide range of dosimetric, clinical and demographic features will be automatically discovered and the features with the strongest associations will populate the nodes of a decision tree. The terminal leaf nodes will each contain the probability of high-grade xerostomia for the subset of patients in that node. In addition, leaf nodes will be assigned binary class labels designating a high- or low risk of high-grade xerostomia. This type of model provides transparency and interpretability, which are beneficial for clinical acceptance and for demonstration of safety to regulatory agencies. The software will be built using the Microsoft Azure cloud architecture and be deployed via a Software as a Service (SaaS) model. There are three distinct aims of this project: 1. Populate Oncospace Inc.’s Microsoft Azure CosmosDB database with data licensed from Johns Hopkins University, including steps such as patient de-identification, data curation, and additional dataset feature engineering 2. Perform CART modeling and test model accuracy, using separate training and test datasets and a variety of performance metrics, including sensitivity, specificity, AUC, and F1-score. 3. Design a clinically acceptable risk classification strategy and a user interface (UI) to communicate model results. Expert input from a team of UI consultants and three radiation oncologists will be an integral part of the development, testing, and evaluation processes. The successful completion of these aims will demonstrate the clinical and commercial feasibility of a xerostomia prediction model for HNC. Further development in Phase 2 will include deeper model personalization via incorporation of advanced image features (radiomics), as well as validation of model generalizability and commercial viability via the curation and use in model building of data from other institutions. Oncospace, formed in 2018, is uniquely positioned to carry out this work as the team includes the creators of the Pinnacle radiation therapy planning system, Tomotherapy radiation treatment delivery system, and HealthMyne Quantitative Imaging Decision Support platform. Oncospace has close clinical collaboration with Johns Hopkins University (JHU) for clinical feedback, validation and initial deployment. Oncospace has licensed three patents and subscription to complete patient treatment records for over 6,000 radiation oncology patients from JHU. The company has won the Microsoft Innovation Acceleration Award for its innovative platform to deliver AI-enabled healthcare solutions to the radiation oncology community. Page 1 of 1

总结 放射治疗（RT）是治疗大多数头颈癌（HNC）病例的主要组成部分。期间 在辐射的情况下，敏感区域如唾液腺可能会受到损伤，导致口腔干燥症（口干）。 这种副作用是常见的，可以显着降低治疗期间和治疗后的生活质量。的焦点 该应用是在治疗计划期间预测患者是否将遭受高度口干症 (NCI首次治疗后随访访视时（通常为RT后3-6个月），CTCAE 2-3级 （约占40%）。预测将使临床医生能够进行治疗计划， 提高对高度口干症发展可能性的认识，并使患者更了解情况， 及时预测后果，如进食困难。 在这个第一阶段项目中，Oncospace Inc.将开发分类和回归树（CART）预测 使用超过1200个完整的HNC患者记录模型。高度口干症与广泛性口腔溃疡之间的关系 将自动发现剂量测定、临床和人口统计学特征的范围， 最强关联将填充决策树的节点。每个终端叶节点将包含 该节点中患者子集的高度口干症的概率。此外，叶节点将被分配 二进制分类标签指定高度口干症的高风险或低风险。这种模式提供了 透明度和可解释性，这有利于临床接受和证明安全性， 监管机构。该软件将使用Microsoft Azure云架构构建，并通过 软件即服务（SaaS）模式。 该项目有三个明确的目标： 1.填充Oncospace Inc.的Microsoft Azure CosmosDB数据库，数据从约翰霍普金斯获得许可 大学，包括患者去识别、数据管理和其他数据集功能等步骤 工程 2.使用单独的训练和测试数据集以及各种 性能指标，包括灵敏度、特异性、AUC和F1评分。 3.设计临床可接受的风险分类策略和用户界面（UI）来传达模型 结果来自UI顾问团队和三名放射肿瘤学家的专家输入将是不可或缺的一部分 开发、测试和评估过程。 这些目标的成功完成将证明口腔干燥症的临床和商业可行性 HNC预测模型第二阶段的进一步发展将包括更深入的模型个性化， 结合先进的图像特征（放射组学），以及验证模型的普遍性， 通过管理和使用其他机构的数据建立模型来提高商业可行性。 Oncospace成立于2018年，在开展这项工作方面具有独特的优势，因为该团队包括 Pinnacle放射治疗计划系统、Tomotherapy放射治疗输送系统和HealthMyne 定量成像决策支持平台。Oncospace与约翰霍普金斯有密切的临床合作 大学（JHU）的临床反馈，验证和初始部署。Oncospace已授权三项专利 并订阅JHU超过6，000名放射肿瘤患者的完整患者治疗记录。的 该公司因其创新平台而获得微软创新加速奖，该平台可提供支持AI的 为放射肿瘤学社区提供医疗保健解决方案。 页1的1