Prediction of Neoadjuvant Chemo Pathological Response Using MRI Markers

描述（申请人提供）：新辅助或术前化疗正在成为乳腺癌治疗和管理的重要组成部分。与此同时，必须制定可靠的监测方法来评估应对措施。动态对比增强MRI（DCE-MRI）已被证明是预测残留肿瘤反应的最准确的成像方式。在本研究中，我们将应用系列DCE-MRI研究来评价治疗前病变的特征性特征，沿着随访MRI研究中确定的早期缓解模式，旨在准确预测病理完全缓解（pCR），这是接受新辅助化疗患者中最相关的预后因素。 在我们的机构，我们有一个积极的正在进行的新辅助化疗方案。在以前的患者队列中，我们已经证明MRI非常有助于提供反应监测信息，以便及时调整治疗方案。根据这些结果，我们确定了本研究中使用的治疗方案。患者将接受2个周期的AC治疗（多柔比星和环磷酰胺），然后接受4个周期的Taxane方案（Nab-紫杉醇联合卡铂，联合曲妥珠单抗治疗HER 2阳性癌症，贝伐珠单抗治疗HER 2阴性癌症）。在目前的提案中，我们将进一步发挥MRI的作用，研究治疗前肿瘤特征，包括形态学，纹理和血管参数以及代谢信息，是否可以用于预测患者是否会实现pCR。我们获得的信息将揭示这两组之间的差异，并可用于建立模型来预测实现pCR的可能性。如果预期患者反应良好，则可以按原样提供方案。如果预期患者不会表现出达到pCR的良好应答，则应考虑修改方案。反应监测数据还可以提供用于修改方案以达到改善的功效的信息。将评价2个周期AC后的早期缓解，然后评价添加1个周期紫杉烷后的早期缓解。总体而言，我们将评估使用治疗前MRI特征参数的准确性，以及治疗过程中的早期肿瘤缓解模式，以区分达到pCR的患者与未达到pCR的患者。假设与单独使用任一数据集相比，使用组合的治疗前病变特征和早期肿瘤缓解模式可以实现更高的pCR预测准确度。

DESCRIPTION (provided by applicant): Neoadjuvant or preoperative chemotherapy is becoming an important part in breast-cancer treatment and management. In parallel it is important to develop reliable monitoring methods to evaluate the response. Dynamic contrast enhanced MRI (DCE-MRI) has been proven as the most accurate imaging modality to predict residual tumor responses. In this study we will apply serial DCE-MRI studies to evaluate the pre-treatment lesion characteristic features, along with the early response patterns determined in follow-up MRI studies to aim for accurate prediction of pathologic complete remission (pCR), which is the most relevant prognostic factor in patients receiving neoadjuvant chemotherapy. At our institution we have an active on-going neoadjuvant chemotherapy protocol. In previous patient cohorts we have demonstrated that MRI is very helpful to provide response-monitoring information for timely adjustment of regimens. Based on these results we have determined the treatment protocols to be used in the current study. Patients will receive 2 cycles AC treatment (doxorubicin and cyclophosphamide) then followed by 4 cycles Taxane regimen (Nab-paclitaxel with Carboplatin, with trastuzumab for HER2-positive cancer and bevacizumab for HER2-negative cancer). In the current proposal we will take the role of MRI one step further, to investigate whether the pre-treatment tumor characteristics, including morphology, texture, and vascular parameters, as well as metabolic information, can be used to predict whether a patient will achieve pCR. The information that we obtain will reveal the differences between these two groups, and that can be used to build a model to predict the likelihood of achieving pCR. If a patient is expected to show a good response, the protocol can be given as it is. If a patient is expected not to show a good response to reach pCR, a modified protocol should be considered. The response monitoring data may also provide information for modifying the regimen to reach an improved efficacy. The early response after 2 cycles AC, then after addition of 1 cycle of taxane will be evaluated. Overall we will evaluate the accuracy of using pre-treatment MRI characteristic parameters, and early tumor response patterns during the treatment course, to differentiate between patients who achieve pCR vs. those who do not. It is hypothesized that a higher accuracy in prediction of pCR can be achieved using combined pre-treatment lesion characteristics and early tumor response patterns compared to that using either set of data alone.