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: 10410192
• 负责人: Pranav Lakshminarayanan
• 金额: $ 12.1万
• 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: 2021-06-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10410192
• 关键词: Acceleration; Accounting; Aftercare; Agreement; Anatomy; Award; Benchmarking; Caring; Classification; Clinical; Common Terminology Criteria for Adverse Events; Community Clinical Oncology Program; Complex; Computer software; Data; Data Science; Data Set; Databases; Decision Trees; Dependence; Development; Development Plans; Disease; Dose; Economic Burden; Engineering; Ensure; Event; Evolution; Feedback; Gland; Guidelines; Head Cancer; Head and Neck Cancer; Health Insurance Portability and Accountability Act; Human Papillomavirus; Image; Incidence; Individual; Institution; Judgment; Legal patent; Licensing; Location; Machine Learning; Malignant Neoplasms; Medical Records; Medicine; Mentors; Methodology; Modeling; Morbidity - disease rate; Neck Cancer; Organ; Outcome; Parents; Patients; Performance; Phase; Population; Probability; Quality of life; ROC Curve; Radiation; Radiation Dose Unit; Radiation Oncology; Radiation therapy; Randomized Clinical Trials; Records; Reporting; Research Personnel; Residual state; Resolution; Risk; Salivary Glands; Science; Sensitivity and Specificity; Severities; Signal Transduction; Site; Small Business Innovation Research Grant; Smoking; Survivors; Symptoms; System; Technology; Testing; Time; Tobacco; Toxic effect; Training; Treatment Efficacy; Universities; Validation; Xerostomia; anticancer research; base; cancer radiation therapy; cancer therapy; career development; classification trees; clinical application; clinical care; clinical decision support; cloud based; cohort; commercialization; design; experience; head and neck cancer patient; inclusion criteria; individual patient; innovation; insight; interpatient variability; model building; parent project; patient subsets; predictive modeling; prevent; product development; programs; radiation-induced injury; radiomics; regression trees; side effect; software as a service; treatment planning; tumor

Project Summary/Abstract Head and neck cancer (HNC) patients survive years after oncologic therapy due to increased efficacy of therapy, increased incidences of human papilloma virus related HNC, and decreased numbers of smoking and tobacco related tumors. However, the majority of patients are plagued with long lasting or permanent dry mouth (xerostomia), whose severity, rate of development and resolution after treatment vary largely between survivors. However, current prediction models for dry mouth have intrinsic issues which so far have prevented their practical use in clinical care, including missing or incomplete data, co- occurence of multiple symptoms, variability across populations and across time, and, in the case of HNC and other spatially-dependent cancers, further symptom dependency on the anatomical location of dose within to organs at risk, namely, salivary glands. We propose to develop validated, patient-specific models to interpret radiation dose to salivary glands in order to inform individual treatment and care decisions for patients. Our data science approach circumvents limitations in the state of the art by accounting for more complex dose-reponse models of dry mouth, by calibrating for inter-patient variability, and by predicting symptom development and computing clinical action signals for a new patient based on cohorts of similar patients. The proposed supplement application extends the methodological approach of the parent award by incorporating validation data from an alternative facility, item assessment, and user experience testing of the resultant software model user interface, undertaken within a career development plan designed to enhance and accelerate the capacity of the applicant, who is from a background underrepresented in biomedical sciences, to transition to mentored and independent investigator status, thus enhancing cancer research workforce diversity under this program.

项目总结/摘要 头颈癌（HNC）患者在肿瘤治疗后存活数年， 治疗效果，人乳头瘤病毒相关HNC的发病率增加， 吸烟和烟草相关的肿瘤。然而，大多数患者都受到长期或长期的困扰。 永久性口干（口干症），其严重程度、发展速度和治疗后的消退 幸存者之间的差异很大。然而，目前的口干预测模型存在内在问题 迄今为止，这些问题阻碍了它们在临床护理中的实际应用，包括缺失或不完整的数据， 多种症状的发生率，不同人群和不同时间的变异性，以及HNC和 其他空间依赖性癌症，进一步的症状依赖于剂量的解剖位置， 危险器官，即唾液腺。 我们建议开发经验证的患者特异性模型来解释唾液的辐射剂量 腺体，以便为患者提供个人治疗和护理决策。我们的数据科学 这种方法通过考虑更复杂的剂量反应， 口干模型，通过校准患者间的变异性，并通过预测症状的发展， 基于相似患者的群组计算新患者的临床动作信号。 拟议的补充申请扩展了母裁决的方法， 结合来自替代设施的验证数据、项目评估和用户体验测试， 在职业发展计划范围内进行，旨在加强 并加快申请人的能力，谁是从背景代表性不足，在生物医学 科学，过渡到指导和独立研究者地位，从而加强癌症研究 在这一计划下的劳动力多样性。