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

头后口干症风险分类系统

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要/摘要头颈癌（HNC）患者在肿瘤学治疗后由于增加而生存治疗的功效，人乳头瘤病毒相关HNC的发病率增加，数量减少吸烟和与烟草有关的肿瘤。但是，大多数患者困扰着持久或永久性口干（切除术），其治疗后的严重程度，发育率和解决方案幸存者之间的变化很大。但是，目前的口干预测模型存在内在问题到目前为止出现多种症状，人群之间以及整个时间的可变性，以及HNC和HNC和其他与空间依赖的癌症，进一步症状依赖于剂量的解剖位置有风险的器官，即唾液腺。我们建议开发经过验证的患者特异性模型，以解释唾液的辐射剂量腺体是为了为患者的个人治疗和护理决定提供信息。我们的数据科学方法通过考虑更复杂的剂量反应来规避艺术状态的限制口干模型，通过校准治疗室间的可变性，并通过预测症状发展和根据类似患者的同类，为新患者计算临床动作信号。拟议的补充申请扩展了父母奖的方法论方法合并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中进行的，由此产生的软件模型用户界面旨在增强并加速申请人的能力，申请人的能力来自生物医学的代表性不足的背景科学，过渡到指导和独立研究者的地位，从而增强了癌症研究根据该计划的劳动力多样性。