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

基本信息

批准号：
10524196
负责人：
GUADALUPE CANAHUATE
金额：
$ 11.57万
依托单位：
UNIVERSITY OF ILLINOIS AT CHICAGO
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：
2021-03-01 至 2026-02-28
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10524196
关键词：
Accounting Address Affect Age American Joint Committee on Cancer Anatomy Big Data Biological Biological Response Modifier Therapy Biomedical Computing Biomedical Engineering Cancer Center Characteristics 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 group Extramural Activities Funding Head and Neck Cancer Head and neck structure 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 Squamous cell carcinoma Staging Substance abuse problem Time Toxic effect Training Treatment outcome Treatment-related toxicity United States Update Validation Xerostomia age group base 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

Accounting Address Affect Age American Joint Committee on Cancer Anatomy Big Data Biological Biological Response Modifier Therapy Biomedical Computing Biomedical Engineering Cancer Center Characteristics 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 group Extramural Activities Funding Head and Neck Cancer Head and neck structure 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 Squamous cell carcinoma Staging Substance abuse problem Time Toxic effect Training Treatment outcome Treatment-related toxicity United States Update Validation Xerostomia age group base 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

展开

项目摘要

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.