Longitudinal Spatial-Nonspatial Decision Support for Competing Outcomes in Head and Neck Cancer Therapy

头颈癌治疗竞争结果的纵向空间-非空间决策支持

• 批准号: 10582612
• 负责人: GUADALUPE CANAHUATE
• 金额: $ 46.93万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-01 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10582612
• 关键词: Accounting; Address; Affect; Age; American Joint Committee on Cancer; Anatomy; Big Data; Biological; Biomedical Computing; Biomedical Engineering; Cancer Center; Characteristics; Chemotherapy and/or radiation; Chronic; Clinic; Clinical; Clinical Management; Clinical Research; Complex; Computer Models; Computing Methodologies; Country; Data; Data Set; Decision Support Model; Decision Support Systems; Development; Diabetes Mellitus; Diagnosis; Disease; Dose; Epidemic; Equilibrium; Ethnic Origin; Ethnic Population; Extramural Activities; Funding; Head and Neck Cancer; Head and Neck Squamous Cell Carcinoma; Hybrids; Image; Individual; Learning; Left; Location; Malignant Neoplasms; Malignant neoplasm of brain; Malignant neoplasm of lung; Mental disorders; Methodology; Methods; Modeling; Modification; Morbidity - disease rate; Nausea; Oncologist; Operative Surgical Procedures; Organ; Outcome; Patient Outcomes Assessments; Patients; Probability; Process; Psychological reinforcement; Public Health; Quality of life; Radiation Dose Unit; Radiation Tolerance; Radiation therapy; Reporting; Risk; Selection for Treatments; Staging; Substance abuse problem; Therapeutic; Therapeutic Intervention; Time; Toxic effect; Training; Treatment outcome; Treatment-related toxicity; United States; Update; Validation; Xerostomia; age group; cancer diagnosis; cancer survival; cancer therapy; chemotherapy; clinical care; clinical decision support; cohort; computer science; computerized tools; data repository; diverse data; high dimensionality; improved; in silico; individual patient; individualized medicine; innovation; insight; mortality; novel; nutrition; optimal treatments; outcome prediction; patient stratification; personalized care; personalized medicine; predictive modeling; prospective; prototype; repository; response; risk prediction; risk prediction model; serial imaging; side effect; standard of care; support tools; survival outcome; symptom cluster; treatment planning; treatment response; treatment strategy; tumor

Cancers that depend on the spatial location of the disease affect all ethnicities and age groups, accounting for significant mortality and therapy-related side effects. In one instance, over 50,000 new cases of head and neck squamous carcinomas are diagnosed each year in the United States, leading to large, rich repositories of patient data. For each of these cases, oncologists need to anticipate survival, oncologic, and toxicity outcomes associated with treatment strategies in order to select a treatment which balances efficacy and toxicity. However, despite the wealth of data available, in the clinic decision support for cancer treatment is rudimentary and incorporates only a handful of patient characteristics, largely due to a lack of computational methodology and tools. We propose to construct a novel statistical and computational methodology for longitudinal and personalized treatment decisions over time, with specific application to head and neck cancer therapy planning. Simultaneous incorporation of complex factors---such as radiation dose location with respect to radiosensitive organs or patient reported side effects affecting quality of life---into treatment decisions over the course of cancer therapy requires the development of novel methodology. This methodology is revolutionary in that it is the first in the field to include both imaging and nonimaging data, while taking into account large-scale biological and clinical correlates. The approach is innovative through its leverage of big data repositories and through its unique blend of computational modeling principles from bioengineering and computer science. These methods allow us to incorporate diverse data types and model competing outcomes. From a clinical perspective, this integrative approach is novel in the field of cancer therapy. The resulting clinical decision support methodology will mark a significant advance in biomedical computing because it will be able to identify, for the first time, actionable timepoints for therapy and toxicity modification, based on a patient’s characteristics and quality of life indicators. The empirically-derived treatment decision support methodology developed in this project has the potential to directly improve the standard of care and the quality of life of surviving patients with a grave, often fatal and debilitating illness.

取决于疾病的空间位置的癌症会影响所有种族和年龄段， 考虑了重大死亡率和与治疗相关的副作用。在一个实例中，超过50,000 每年在联合诊断出头部和颈部鳞状癌的新病例 各州，导致大量，丰富的患者数据存储库。对于每种情况，肿瘤学家 需要预测与治疗相关的生存，肿瘤学和毒性结果 策略以选择平衡效率和毒性的治疗方法。但是，需求 可用数据的财富，在诊所的癌症治疗支持中是基本的 并且仅包含少数患者特征，这主要是由于缺乏计算 方法和工具。 我们建议为纵向和 随着时间的推移，个性化治疗决策，并针对头颈癌进行了特定的应用 治疗计划。同时纳入复杂因素的行业 - 例如辐射剂量 关于放射敏感器官或患者报告的副作用的位置，影响质量 生活---在癌症治疗过程中进行治疗决策需要发展 新方法。这种方法是革命性的，因为它是该领域的第一个包括 成像和非成像数据，同时考虑到大规模的生物学和临床 相关。该方法通过大数据存储库的杠杆作用和通过 它从生物工程和计算机中的计算建模原理的独特融合 科学。这些方法使我们能够合并潜水员的数据类型并模拟竞争 结果。 从临床角度来看，这种综合方法在癌症治疗领域是新颖的。这 由此产生的临床决策支持方法将标志着生物医学的重大进步 计算是因为它将能够首次识别可操作的治疗时间点 基于患者的特征和生活质量指标，毒性修饰。这 该项目中开发的经验衍生的治疗决策支持方法论 有可能直接提高护理标准和生存患者的生活质量 坟墓，通常是致命的和使人衰弱的疾病。