Prediction of Neoadjuvant Chemo Pathological Response Using MRI Markers

使用 MRI 标记预测新辅助化疗病理反应

基本信息

批准号：
7759648
负责人：
Min-Ying L. Su
金额：
$ 51.11万
依托单位：
UNIVERSITY OF CALIFORNIA-IRVINE
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-04-01 至 2013-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7759648
关键词：
3-Dimensional Anthracyclines Area Biological Markers Biological Neural Networks Blood Vessels Breast Breast Cancer Treatment Cancer Patient Carboplatin Cardiotoxicity Characteristics Chemotherapy-Oncologic Procedure Choline Clinical Clinical Research Cohort Studies Cyclophosphamide Cytotoxic agent Data Data Set Diagnostic Neoplasm Staging Disease remission Dose Doxorubicin ERBB2 gene Evaluation Failure Fibrosis Fluorescent in Situ Hybridization Fluorouracil Future Goals Guidelines Imaging Techniques In complete remission Individual Inflammatory Institution Kinetics Lesion Link Literature Locally Advanced Malignant Neoplasm Logistic Regressions Magnetic Resonance Imaging Malignant Neoplasms Mammary Ultrasonography Mammography Measures Metabolic Methods Modeling Monitor Morbidity - disease rate Morphology Neoadjuvant Therapy One-Step dentin bonding system Operative Surgical Procedures Outcome Paclitaxel Pathologic Patients Pattern Prognostic Factor Protocols documentation Publishing ROC Curve Radiation Reaction Regimen Regression Analysis Relapse Research Design Residual Tumors Resistance Role Survival Rate Taxane Compound Techniques Testing Texture Therapeutic Time Toxic effect Trastuzumab Treatment Protocols arm base bevacizumab cancer therapy chemotherapy cohort design docetaxel effective therapy follow-up imaging modality improved in vivo irinotecan malignant breast neoplasm mastitis novel therapeutics outcome forecast response soft tissue standard care success taxane tumor

项目摘要

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.

描述（由申请人提供）：新辅助或术前化疗正在成为乳腺癌治疗和管理中的重要组成部分。同时，开发可靠的监视方法以评估响应很重要。动态对比增强的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。在这项研究中，我们将应用串行DCE-MRI研究来评估治疗前病变的特征特征，以及在后续MRI研究中确定的早期反应模式，目的是准确预测病理完全缓解（PCR），这是接受新辅助化学疗法的患者中最相关的预后因素。在我们的机构中，我们有一种积极的新辅助化疗方案。在以前的患者队列中，我们已经证明MRI对提供响应监控信息非常有帮助，以及时调整治疗方案。基于这些结果，我们确定了当前研究中要使用的治疗方案。患者将接受2个周期AC治疗（阿霉素和环磷酰胺），然后接受4个周期紫杉烷治疗（NAB-甲米酰二昔烷含有卡泊粉素），曲妥珠单抗用于HER2阳性癌症和bevacizumab，用于HER2阴性癌）。在当前的提案中，我们将进一步提出MRI的作用，以研究是否可以使用包括形态学，纹理和血管参数在内的预处理肿瘤特征以及代谢信息，以预测患者是否会达到PCR。我们获得的信息将揭示这两组之间的差异，并且可以用于构建模型以预测实现PCR的可能性。如果期望患者表现出良好的反应，则可以按原样给出该方案。如果期望患者对达到PCR的良好反应，则应考虑修改的方案。响应监视数据还可以提供用于修改该方案以提高功效的信息。 2个周期AC后的早期响应，然后将评估1个紫杉烷周期后。总体而言，我们将评估使用预处理MRI特征参数和治疗过程中早期肿瘤反应模式的准确性，以区分实现PCR的患者与不具有的PCR的患者。假设与仅使用任何一组数据相比，使用合并的预处理病变特征和早期肿瘤反应模式可以实现更高的PCR预测准确性。