权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Risk prediction and longitudinal assessment of cardiotoxicity and functional capacity trajectory in NSCLC patients

NSCLC 患者心脏毒性和功能能力轨迹的风险预测和纵向评估

基本信息

批准号：
10641877
负责人：
ZHONGXING LIAO
金额：
$ 76.42万
依托单位：
UNIVERSITY OF TX MD ANDERSON CAN CTR
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 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10641877
关键词：
3-Dimensional Accounting Activities of Daily Living Aftercare Apoptosis Biological Markers Blood Blood specimen Brain natriuretic peptide C-reactive protein Cancer Patient Cardiac Cardiotoxicity Cardiovascular system Categories Chest Clinical Complication DNA Repair Data Data Set Decision Making Disease Disease Outcome Dose Event Functional disorder Genes Genetic Genetic Predisposition to Disease Goals Heart Heart Injuries Immunotherapy Incidence Individual Inflammation Lung Malignant Neoplasms Malignant neoplasm of lung Mediating Mission Modality Modeling Multivariate Analysis National Cancer Institute National Heart, Lung, and Blood Institute Non-Small-Cell Lung Carcinoma Normal tissue morphology Operative Surgical Procedures Organ Outcome Patient Outcomes Assessments Patient Selection Patient risk Patient-Focused Outcomes Patients Pattern Performance Pharmaceutical Preparations Photons Physicians Population Study Probability Process Prospective cohort Prospective, cohort study Protons Radiation Radiation Dose Unit Radiation therapy Randomized Registries Reporting Risk Risk Factors Selection for Treatments Shapes Single Nucleotide Polymorphism Statistical Models Structure Techniques Testing Tissues Toxic effect Troponin T Validation Vascular calcification cancer survival cancer therapy cancer type cardiovascular injury cardiovascular risk factor chemoradiation chemotherapy clinical practice clinical risk cohort comorbidity coronary calcium scoring demographics fitness heart damage heart function high risk improved individual patient optimal treatments participant enrollment patient population personalized risk prediction population based predictive modeling prevent prospective proton therapy radiation risk randomized trial response risk prediction risk stratification tool treatment planning trial comparing tumor

项目摘要

Project Summary/Abstract Non-small cell lung cancer (NSCLC) is the most prevalent form of lung cancer, accounting for approximately 85% of all lung cancers, which is one of the deadliest types of cancers. Standard NSCLC treatments include surgery, immunotherapy, chemotherapy, and radiation therapy. Radiation therapy can be delivered by either photons or protons; however, both types of radiation therapy to the chest can result in cardiac injury. To date, no available clinical tool exists to guide physicians in choosing the best type of radiation therapy according to an individual’s risk for radiation-mediated cardiac injury. To plan radiation therapy, normal tissue complication probability (NTCP) models are commonly used and take into consideration differences in geometric shape or volumes between tumor and non-tumor tissue, as well as tissue dose constraints. However, these patient population-reliant models are based only on photon radiation therapy data (not proton), do not consider the differences in radiation vulnerability of organ substructures, and do not consider the individual NSCLC patient’s risk for a specific toxicity (e.g., cardiac toxicity). Hence, this proposal tests the hypothesis that chemoradiation- related cardiac toxicity can be minimized by dose optimization and individual pre-existing cardiovascular risk- stratification for choosing appropriate radiation modality. Pre-existing cardiovascular risk factors, such as individual genetic predisposition, cardiac injury blood biomarkers, and extent of vascular calcification will be correlated with chemoradiation-associated cardiac toxicity and overall survival (OS) in Aim 1. Data on pre- existing cardiovascular risk factors will be retrospectively collected from two prospective, randomized comparisons of photons vs. protons and from a registry trial, which included proton-treated patients not enrolled into the randomized trial. Associations between pre-existing cardiovascular events and radiation therapy- mediated cardiac events as well as OS will be used as parameters to generate a one-of-a-kind NTCP model (Aim 2). In Aim 3, a prospective cohort registration trial will be developed to longitudinally assess cardiac function, cardiac fitness, and model implementation. During model implementation, two maximally optimized radiation therapy plans for each enrolled patient will be developed: 1) using standard population-based dose constraints; and 2) using personalized dose constraints based on individual risk. A predefined NTCP goal will be set to evaluate both plans. If the personalized plan improves the NTCP goal by 15%, the patient will be treated using the personalized plan. The model performance will be continuously assessed and improved using the data accumulated from the trial. The long-term objectives of this proposed project are to minimize cardiovascular injury while optimizing NSCLC patient outcomes, based on individual patient risk to cardiac injury after concurrent chemoradiation therapy by multivariable model selection of radiation therapy modality and technique. Preventing cardiovascular injury in cancer patients so that individuals can live longer, and more fulfilling lives is in direct alignment with the mission of both the National Cancer Institute and the National Heart, Lung, and Blood Institute.

项目总结/文摘

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Geometric and dosimetric accuracy of deformable image registration between average-intensity images for 4DCT-based adaptive radiotherapy for non-small cell lung cancer.

DOI：
10.1002/acm2.13341
发表时间：
2021-08
期刊：
Journal of applied clinical medical physics
影响因子：
2.1
作者：
He Y;Cazoulat G;Wu C;Peterson C;McCulloch M;Anderson B;Pollard-Larkin J;Balter P;Liao Z;Mohan R;Brock K
通讯作者：
Brock K

Correcting CT misregistration in data-driven gated (DDG) PET with PET self-gating and deformable image registration.

通过 PET 自门控和可变形图像配准，纠正数据驱动门控 (DDG) PET 中的 CT 重合失调。