Predicting Radiotherapy Outcomes by Combining Physical and Biological Factors

描述(由申请人提供)：许多放射治疗(RT)治疗对正常组织具有严重并发症的显著风险。以前，预测结果的努力要么使用物理因素(例如，高剂量照射的体积)，要么使用生物因素(例如，固有的辐射敏感性)，但不能同时使用两者。我们假设，将物理治疗数据与患者特定的生物标志物相结合将增加RT结果模型的预测能力。我们将专门验证这一假说以预测肺癌患者的放射性肺炎(RP)。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，并收集候选物理和生物学数据进行建模。根据同行评议文献选择的生物标志物家族主要包括：(A)DNA损伤检测和修复蛋白的DNA末端结合复合体的水平，它与放射敏感性密切相关；以及(B)治疗前血液中炎性细胞因子白介素家族(IL-1α和IL-6)和ACE酶的水平，它们也与RP相关。要收集的物理数据包括正常肺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。我们还将在放疗过程中(放疗中期和放疗结束时)对肿瘤消退进行成像，以便更好地描述肿瘤消退后正常肺组织所接受的高剂量。在SA2中，我们通过结合生物和物理数据来测试结果预测方面的假设改进。为了选择最好的数学模型，我们将适应和验证一种新的统计建模形式，即基于核的学习。根据我们的初步结果，基于核的方法可能会为理解不同物理和生物变量之间的相互作用提供一个自然的框架，从而产生一个有效的最优预测模型。总之，我们建议测试生物/生物标记物数据和剂量数据的组合，以提高我们预测放射治疗并发症的能力。特别是，我们将使用可检测的基于血液的生物标记物以及治疗剂量分布特征来潜在地提高我们预测(或避免)放射性肺炎的能力。

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.