Quantitative Image Analysis for Assessing Response to Breast Cancer Therapy

用于评估乳腺癌治疗反应的定量图像分析

• 批准号: 9249507
• 负责人: Maryellen L. Giger
• 金额: $ 50.37万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-15 至 2020-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9249507
• 关键词: Address; Aftercare; Algorithms; American College of Radiology Imaging Network; Biologic Characteristic; Biological; Biological Markers; Biometry; Blinded; Breast Cancer Treatment; Breast Cancer therapy; Characteristics; Chicago; Classification; Clinical; Clinical Research; Clinical Trials; Communities; Complement; Comprehensive Cancer Center; Computer software; Computer-Assisted Diagnosis; Computing Methodologies; Data; Data Set; Databases; Development; Diagnosis; Dimensions; Feedback; Goals; Grant; Image; Image Analysis; Informatics; Investigation; Kinetics; Lesion; Link; Magnetic Resonance Imaging; Mammary Neoplasms; Measures; Methods; Morphology; Neoadjuvant Therapy; Outcome; Pathologic; Pathology; Patient Education; Patients; Performance; Prediction of Response to Therapy; Predictive Value; Primary Neoplasm; Protocols documentation; Recording of previous events; Research; Stratification; Supervision; Surrogate Markers; System; Systems Analysis; Techniques; Testing; Texture; Training; Translations; Universities; Validation; Variant; Woman; base; breast imaging; cancer imaging; cancer subtypes; improved; innovation; malignant breast neoplasm; multi-site trial; oncology; outcome prediction; predicting response; predictive marker; predictive signature; public health relevance; quantitative imaging; response; therapy outcome; time use; tool; treatment response; tumor; Address; Aftercare; Algorithms; American College of Radiology Imaging Network; Biologic Characteristic; Biological; Biological Markers; Biometry; Blinded; Breast Cancer Treatment; Breast Cancer therapy; Characteristics; Chicago; Classification; Clinical; Clinical Research; Clinical Trials; Communities; Complement; Comprehensive Cancer Center; Computer software; Computer-Assisted Diagnosis; Computing Methodologies; Data; Data Set; Databases; Development; Diagnosis; Dimensions; Feedback; Goals; Grant; Image; Image Analysis; Informatics; Investigation; Kinetics; Lesion; Link; Magnetic Resonance Imaging; Mammary Neoplasms; Measures; Methods; Morphology; Neoadjuvant Therapy; Outcome; Pathologic; Pathology; Patient Education; Patients; Performance; Prediction of Response to Therapy; Predictive Value; Primary Neoplasm; Protocols documentation; Recording of previous events; Research; Stratification; Supervision; Surrogate Markers; System; Systems Analysis; Techniques; Testing; Texture; Training; Translations; Universities; Validation; Variant; Woman; base; breast imaging; cancer imaging; cancer subtypes; improved; innovation; malignant breast neoplasm; multi-site trial; oncology; outcome prediction; predicting response; predictive marker; predictive signature; public health relevance; quantitative imaging; response; therapy outcome; time use; tool; treatment response; tumor

 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.

 描述（由适用提供）：这项研究的目的是开发基于定量图像的乳腺癌肿瘤的替代标志物，以预测对治疗的反应，并最终有助于患者管理。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，原发性肿瘤的生物学特征（即特征）与预后相关。方法 但是，评估此类生物学特征以预测结果，可能是侵入性的，昂贵的或不可广泛的。我们的假设是，通过定量图像分析获得的基于MRI的特征将被证明可作为无创生物标志物用于评估乳腺癌对新辅助治疗的反应的非侵入性生物标志物。我们建议使用ACRIN 6657临床试验的乳腺肿瘤的磁共振（MR）图像验证基于图像的生物标志物，其中包括病理反应数据。具体，（1）我们将 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数据集和（3）我们将从ACRIN数据集中进行MRI特征与癌症亚型和其他临床/组织病理学数据的初步分层和关联。我们将通过扩展我们先前的开发，验证和对计算机辅助诊断后诊断的定量图像分析方法进行创新的25年历史，以进行诊断，预测性成分，以评估对新辅助治疗的反应。我们的研究使用现有的ACRIN 6657数据集解决了算法的开发和验证，其目的是“提高定量治疗的靶向肿瘤对治疗的反应”。我们提出的研究与QIN U01 PAR-11-150的目标保持一致，即鲁棒性调查和多站点试验数据（Uchicago和Acrin）。通过这项QIN赠款，我们参与QIN社区将产生可交付成果，包括开放平台系统，该系统将提供用于链接细分/特征提取/分类的工具，以比较跨采集和/或分析系统的性能指标，以及通过减少维度的技术进行发现。我们的研究将产生一系列经过验证的病变特征，这些病变将用作临床研究/试验的定量工具，以预测和/或评估肿瘤反应。鉴于其他研究/试验可能会使用不同的治疗方法，我们将为QIN社区提供我们的培训，测试和介绍定量签名的工具，以便可以确定一系列治疗方法的预测签名。