Predicting Neoadjuvant Chemo Response/Prognosis Using Imaging Biomarkers

描述（由申请人提供）：乳腺癌新辅助化疗（NAC）的最佳目标是以最小的毒性达到病理完全缓解（pCR）。随着更有效的治疗方法的出现，一个挑战是如何及时评估肿瘤的反应，从而为患者提供最佳的治疗方案。目前迫切需要能够监测和预测NAC反应的可靠成像方法。除了治疗反应监测，影像学参数与癌症的表型相关，并可作为预后标志物。我们已经进行了近10年的NAC研究，在这次更新中，我们将继续开发和验证用于评估和预测治疗反应以及预测预后的成像标记物。提出了三个目标。Aim-1将跟踪自2003年以来所有先前入组的NAC患者（超过180例），以获得他们的预后信息。除了之前分析的影像学参数外，我们将使用我们开发的综合定量方法分析两种具有预后价值的新型影像学指标，即乳腺密度变化（纤维腺组织体积）和间质组织对比增强。主要分析是使用影像学参数预测局部复发，但将收集无病生存和总生存并用于探索性分析。Aim-2和3将使用一项新的临床试验前瞻性收集的数据进行，该试验将75名NAC患者纳入一项影像学监测研究，该研究使用MRI和科学乳房x线摄影（MR-SMM）联合系统。尽管研究人员对复杂的多参数成像方法的作用感兴趣，但总是需要一种简单的方法来准确测量肿瘤的大小；SMM有很大的潜力。Aim-2将比较NAC前、中、后不同时间SMM和MRI的诊断性能。此外，SMM和MRI测量的nac后残留病变的诊断准确性将以病理检查结果为金标准进行比较。Aim-3将基于治疗前和第一次随访影像的早期变化，评估MRI和SMM各自和联合预测病理完全缓解（pCR）的能力。通过对NAC中不同阶段、不同时间的肿瘤进行MRI和SMM的诊断结果，将为建立单独的SMM系统以及MR-SMM联合系统在乳腺癌患者管理中的作用提供重要信息。

DESCRIPTION (provided by applicant): The optimal goal of neoadjuvant chemotherapy (NAC) for breast cancer is to achieve pathologic complete response (pCR) with the least toxicity. As more effective therapies become available, one challenge is how to evaluate the response of tumor in a timely manner, so an optimal regimen can be given to the patient. Reliable imaging methods that can monitor and predict NAC response are critically needed. In addition to therapy response monitoring, imaging parameters are associated with phenotype of cancer and may be used as prognostic markers. We have been performing NAC studies for close to 10 years, and in this renewal we will continue to develop and validate imaging markers for evaluating and predicting therapeutic response, as well as for predicting prognosis. Three aims are proposed. Aim-1 will follow all previously enrolled NAC patients since 2003 (more than 180 patients) to obtain their prognostic information. Other than previously analyzed imaging parameters, two novel imaging markers that are shown to have prognostic value, i.e. the change of breast density (fibroglandular tissue volume) and the stromal tissue contrast enhancement, will be analyzed using a comprehensive quantitative method developed by us. The primary analysis is to use imaging parameters to predict local recurrence, but the disease-free and overall survival will be collected and used in exploratory analyses. Aim-2 and -3 will be performed using the prospectively collected data from a new clinical trial to enroll 75 NAC patients into an imaging monitoring study by using a combined MRI and scinti-mammography (MR-SMM) system. Despite of the research interest in investigating the role of complex multi- parametric imaging methods, there is always a need of an easy method that can measure the tumor size accurately; and SMM has a great potential. Aim-2 will compare the diagnostic performance of SMM and MRI at different times before, during, and after NAC. Also, the diagnostic accuracy of the post-NAC residual disease measured by SMM and MRI will be determined by comparing to pathological examination results as the goldstandard. Aim-3 will evaluate the respective and combined ability of MRI and SMM, based on pre-treatment and early changes in the first follow-up imaging, for predicting pathologic complete response (pCR). The diagnostic results obtained using MRI and SMM from tumors of varying stages at different times during NAC will provide important information for establishing the role of a standalone SMM system, as well as the combined MR-SMM system, for management of breast cancer patients.