Quantitative Image Analysis for Assessing Response to Breast Cancer Therapy

 描述（由申请人提供）：本研究的目标是开发乳腺癌肿瘤的基于定量图像的替代标记物，用于预测对治疗的反应，并最终帮助患者管理。女性乳腺癌的临床表现有很大的差异，并且已经表明，在许多情况下，生物学特征，即，原发性肿瘤的特征与结果相关。方法 然而，评估这些生物特征以预测结果可能是侵入性的、昂贵的或不广泛可用的。我们的假设是，通过定量图像分析获得的基于MRI的特征将被证明是有用的非侵入性生物标志物的评估和预测，乳腺癌对新辅助治疗的反应。我们建议使用来自ACRIN 6657临床试验的乳腺肿瘤的磁共振（MR）图像来验证这种基于图像的生物标志物，其中包括病理反应数据。具体而言，（1）我们将 探讨乳腺癌治疗结果与基于MR图像的肿瘤特征的关系（特征），以及这些特征随时间的变化，使用芝加哥大学数据库和来自接受新辅助治疗的患者的乳腺癌肿瘤的ACRIN 6657 I-SPY临床试验数据集，（2）我们将开发和评估乳腺癌肿瘤的MRI衍生“特征”，使用ACRIN 6657数据集对治疗反应进行评估，以及（3）我们将进行初步的初始分层，并将MRI特征与癌症亚型和来自ACRIN数据集的其他临床/组织病理学数据相关联。我们将在我们25年的临床创新历史的基础上，将我们先前的计算机辅助诊断定量图像分析方法的开发、验证和翻译扩展到诊断后预测部分，以评估对新辅助治疗的反应。我们的研究致力于使用现有的ACRIN 6657数据集开发和验证算法，目标是“提高定量测量靶向肿瘤对治疗的反应的能力”。我们提出的研究符合QIN U 01 PAR-11-150的目标，包括稳健性研究和多中心试验数据（UChicago和ACRIN）。通过这项QIN资助，我们在QIN社区的参与将产生可交付成果，包括一个开放平台系统，该系统将提供用于链接分割/特征提取/分类的工具，用于比较采集和/或分析系统的性能指标，以及通过降维技术进行发现。我们的研究将产生一组经验证的病变特征，这些特征将作为定量工具用于临床研究/试验，以预测和/或评估肿瘤缓解。鉴于其他研究/试验可能使用不同的治疗方法，我们将向QIN社区提供我们的工具，用于培训，测试和呈现定量特征，以便确定一系列治疗方法的预测特征。

 DESCRIPTION (provided by applicant): The goal of this research is to develop quantitative image-based surrogate markers of breast cancer tumors for use in predicting response to therapy and ultimately aiding in patient management. There is a large variation in the clinical presentation of breast cancer in women, and it has been shown that in many instances, biological characteristics, i.e., features, of the primary tumor correlate with outcome. Methods to assess such biological features for the prediction of outcome, however, may be invasive, expensive or not widely available. Our hypothesis is that MRI-based features obtained through quantitative image analysis will prove useful as non-invasive biomarkers for the assessment of, and prediction of, the response of breast cancer to neoadjuvant therapy. We propose to validate such image-based biomarkers using magnetic resonance (MR) images of breast tumors from the ACRIN 6657 clinical trial, which includes pathological response data. Specifically, (1) We will investigate the relationship of breast cancer therapy outcome and MR image-based tumor characteristics (features), and changes in these features over time, using a University of Chicago database and the ACRIN 6657 I-SPY clinical trial dataset of breast cancer tumors from patients who have undergone neoadjuvant treatment, (2) We will develop and evaluate the MRI-derived `signatures' of breast cancer tumors for the prediction of, and assessment of, response to therapy using the ACRIN 6657 dataset, and (3) We will conduct preliminary, initial stratification and association of the MRI features with cancer subtype and other clinical/histopathological data from the ACRIN dataset. We will build on our 25-year history of taking innovation to the clinical setting by extending our prior development, validation, and translation of quantitative image analysis methods for computer- aided diagnosis to the post-diagnosis, predictive component in order to assess response to neoadjuvant therapy. Our research addresses the development and validation of algorithms using the existing ACRIN 6657 dataset with the goal of "improving the ability to measure the response of targeted tumors to therapy quantitatively". Our proposed research is aligned with the QIN U01 PAR-11-150 goals of including robustness investigations and multi-site trial data (UChicago and ACRIN). Through this QIN grant, our participation in the QIN community will yield deliverables including an open-platform system that will provide tools for linking segmentation/feature extraction/classification, for comparing performance metrics across acquisition and/or analysis systems, and for discovery through dimension reduction techniques. Our research will yield a set of validated lesion signatures that will serve as quantitative tools for use in clinical studies/trials to predict and/or assess tumor response. Given that other studies/trials may use different treatments, we will make available to the QIN community our tools for training, testing, and presenting the the quantitative signatures so that predictive signatures for a range of treatments can be determined.