Predicting Radiotherapy Outcomes by Combining Physical and Biological Factors

通过结合物理和生物因素预测放射治疗结果

• 批准号: 7622168
• 负责人: Issam M. El Naqa
• 金额: $ 13.15万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-05-15 至 2012-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7622168
• 关键词: 3-Dimensional; Address; Age; Algorithms; Animal Model; Archives; Area; Arts; Binding; Binding Proteins; Biological; Biological Assay; Biological Factors; Biological Markers; Biological Markers; Biology; Blood; Blood specimen; Breathing; Cancer Patient; Categories; Cell Culture Techniques; Characteristics; Clinical; Clinical Treatment; Clinical Trials; Collection; Common Terminology Criteria for Adverse Events; Complex; Complication; DNA Damage; Data; Data Collection; Data Set; Databases; Detection; Development; Diagnostic Neoplasm Staging; Dose; Electrophoretic Mobility Shift Assay; Environment; Environmental Risk Factor; Enzymes; Event; Family; Gender; Genetic; Goals; Grant; Histology; Housing; Image; Imaging Techniques; Inflammation; Inflammatory; Information Theory; Interleukin-1 alpha; Interleukin-6; Interleukins; Investigation; Knowledge; Learning; Link; Literature; Location; Logistic Regressions; Lung; Machine Learning; Malignant neoplasm of lung; Maps; Measures; Methods; Metric; Modeling; Morphologic artifacts; Motion; Non-Small-Cell Lung Carcinoma; Non-linear Models; Normal tissue morphology; Nuclear; Organ; Outcome; Outcome Study; Pathology; Patients; Peer Review; Peptidyl-Dipeptidase A; Phase; Physics; Play; Pneumonia; Population; Proteins; Pulmonary Function Test/Forced Expiratory Volume 1; Pulmonary function tests; Radiation; Radiation Pneumonitis; Radiation Tolerance; Radiation therapy; Radiotherapy Research; Relative (related person); Relative Risks; Reporting; Resistance; Review Literature; Risk; Risk Factors; Sampling; Scanning; Serum; Shapes; Shorthand; Source; Staging; Statistical Models; Structure of parenchyma of lung; System; Techniques; Technology; Testing; Time; Transforming Growth Factor beta; Transforming Growth Factors; Tumor stage; Uncertainty; Validation; Work; X-Ray Computed Tomography; base; cytokine; data mining; data modeling; follow-up; high risk; improved; insight; mathematical model; model design; open source; predictive modeling; prospective; protein complex; repaired; response; treatment planning; tumor

DESCRIPTION (provided by applicant): Many radiotherapy (RT) treatments carry a significant risk of serious complications to normal tissues. Previously, efforts to predict outcome have used either physical factors (e.g., volume irradiated to high dose) or biological factors (e.g., inherent radiosensitivity), but not both. We hypothesize that combining physical treatment data with patient-specific biomarkers will increase the predictive power of RT outcomes models. We will test this hypothesis specifically for prediction of radiation pneumonitis (RP) for lung cancer patients. Because no appropriate dataset exists to test this hypothesis, under Specific Aim 1, we will conduct a prospective clinical trial for non-small-cell lung cancer patients and collect candidate physical and biological data for modeling. Biomarker families selected based on peer-reviewed literature will mainly include: (a) the levels of DNA-end binding complexes of DNA damage detection and repair proteins, which has been well correlated with radiosensitivity; and (b) pretreatment blood levels of the interleukin family of inflammatory cytokines (IL-1 alpha and IL-6) and ACE enzymes, which have also been correlated to RP. Physical data to be collected includes volumes irradiated to varying doses of the normal lung (dose-volume histogram data, collected using 4-D methods to avoid breathing artifacts), and the spatial location of the high-dose regions, both of which have been correlated with risk of RP. We will also image tumor regression over the course of RT (mid-course and at the end of RT) in order to better characterize the high doses received by normal lung tissue after tumor regression. In SA2, we test the hypothesized improvement in outcome prediction by combining biological and physical data. To select the best mathematical model, we will adapt and validate a new form of statistical model-building known as kernel-based learning. Based on our preliminary results, the kernel-based methods will likely provide a natural framework for understanding the interactions among the different physical and biological variables resulting in an effectively optimal predictive model. In summary, we propose to test the combination of biological/biomarker data and dose data to improve our ability to predict radiotherapy complications. In particular, we will use detectable blood-based biomarkers as well as treatment dose distribution characteristics to potentially improve our ability to predict (or avoid) radiation pneumonitis.

描述（由申请人提供）： 许多放射疗法（RT）治疗对正常组织具有严重并发症的显著风险。以前，预测结果的努力使用了物理因素（例如，照射到高剂量的体积）或生物因素（例如，固有的放射敏感性），但不是两者。我们假设将物理治疗数据与患者特异性生物标志物相结合将增加RT结局模型的预测能力。我们将专门针对肺癌患者放射性肺炎（RP）的预测来检验这一假设。由于没有合适的数据集来检验这一假设，在特定目标1下，我们将对非小细胞肺癌患者进行前瞻性临床试验，并收集候选的物理和生物学数据用于建模。基于同行评审文献选择的生物标志物家族将主要包括：（a）DNA损伤检测和修复蛋白的DNA末端结合复合物水平，其与放射敏感性密切相关;和（B）炎性细胞因子（IL-1 α和IL-6）和ACE酶的白细胞介素家族的治疗前血液水平，其也与RP相关。要收集的物理数据包括正常肺的不同剂量照射的体积（剂量体积直方图数据，使用4-D方法收集，以避免呼吸伪影），以及高剂量区域的空间位置，这两者都与RP的风险相关。我们还将在RT过程中（RT中期和结束时）对肿瘤消退进行成像，以更好地表征肿瘤消退后正常肺组织接受的高剂量。在SA 2中，我们通过结合生物学和物理学数据来测试假设的结局预测改善。为了选择最好的数学模型，我们将采用并验证一种新的统计模型构建形式，称为基于内核的学习。根据我们的初步结果，基于核的方法可能会提供一个自然的框架，用于理解不同物理和生物变量之间的相互作用，从而产生有效的最佳预测模型。总之，我们建议测试生物/生物标志物数据和剂量数据的组合，以提高我们预测放疗并发症的能力。特别是，我们将使用可检测的血液生物标志物以及治疗剂量分布特征，以潜在地提高我们预测（或避免）放射性肺炎的能力。