Multi-parametric 4-D Imaging Biomarkers for Neoadjuvant Treatment Response

新辅助治疗反应的多参数 4-D 成像生物标志物

• 批准号: 9895669
• 负责人: Despina Kontos
• 金额: $ 48.68万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-19 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9895669
• 关键词: 4D Imaging; Address; American College of Radiology Imaging Network; Anatomy; Biological Markers; Breast; Cancer Burden; Characteristics; Complex; Computer software; Data; Descriptor; Diffuse; Disease; Disease-Free Survival; Gene Expression; Heterogeneity; Hot Spot; Image; In complete remission; Kinetics; Knowledge Discovery; Lesion; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Maps; Measures; Methods; Modeling; Monitor; Morphology; Neoadjuvant Therapy; Normal tissue morphology; Operative Surgical Procedures; Pathologic; Patient-Focused Outcomes; Pattern; Performance; Phenotype; Physiological; Play; Prediction of Response to Therapy; Predictive Value; Property; Registries; Residual Cancers; Role; Scanning; Signal Transduction; Soft Tissue Neoplasms; Structure; Testing; Texture; Time; Tissues; Training; Training Support; Treatment outcome; Tumor Expansion; Tumor Markers; Tumor Subtype; Tumor Tissue; Tumor Volume; United States National Institutes of Health; Visit; Woman; angiogenesis; arm; base; breast imaging; chemotherapy; computerized tools; experimental arm; falls; feature extraction; follow-up; high dimensionality; image registration; imaging biomarker; imaging study; improved; indexing; individual patient; individualized medicine; machine learning method; malignant breast neoplasm; molecular marker; multimodality; new therapeutic target; novel; open source; personalized medicine; predicting response; predictive marker; predictive modeling; public health relevance; receptor; response; secondary endpoint; serial imaging; spatiotemporal; support vector machine; tool; treatment effect; treatment response; tumor; tumor heterogeneity

 DESCRIPTION (provided by applicant): Imaging plays a critical role in evaluating tumor response to treatment; however the currently used methods remain significantly limited. For example, standards such as the RECIST are subjective and cannot be used to adequately characterize irregular lesions; tumor volume measures alone do not account for detailed structural changes; and features from selected tumor regions, such as "hot-spot" peak-enhancement, do not capture information from the entire tumor. As such, current approaches fall short of capturing the multi-faceted effects of treatment, including phenotypic tumor heterogeneity and its longitudinal change during treatment, which is increasingly recognized as an important predictive indicator. To date, few studies have explored using richer imaging descriptors, which could result in more powerful predictive markers. Moreover, fewer have attempted to combine multi-modal biomarkers, such as imaging with histopathologic and molecular markers, to develop enhanced predictive models for specific tumor sub-types and individual patients. We propose to develop advanced computational tools that will enable to i) extract novel multi-parametric imaging signatures and ii) accurately characterize their longitudinal patterns of change during neoadjuvant treatment via deformable image registration. Our approach is thus geared towards knowledge discovery, for determining which imaging parameters have the highest predictive value out of many possible ways to quantify information provided by imaging. In SA1 we will develop robust 4D deformable image registration methods, based on principles of mutual saliency, for estimating transformations that will enable us to robustly register serial imaging scans and obtain anatomically precise spatio-temporal parametric maps of longitudinal tissue effects induced by treatment. In SA2 we will analyze whole-tumor and normal tissue effects by performing multi- parametric feature extraction, including a rich set of morphologic, textural, kinetic and parenchymal tissue descriptors, which in conjunction to registration will allow us to comprehensively capture the dynamically evolving imaging phenotype during treatment. In SA3 we will test our method in a major breast imaging study, the I-SPY 1/ACRIN 6657 trial. We will apply machine learning tools to identify high-dimensional associations of imaging patterns, in conjunction to histopathologic tumor subtyping, that can best predict pathologic complete response (pCR) and 5-year disease free survival (DFS). In SA4 we will independently test our models with the I-SPY 2/ACRIN 6698 trial, where we will also evaluate the robustness of our features to a diverse range of treatments. Our methods hold the promise to shift the current paradigm in personalizing neoadjuvant treatment by 1) improving the current standards of imaging-based assessment and 2) introducing new imaging biomarkers that can be of higher value as early predictors of treatment response and survival. Our tools will be shared as open-source software via NIH/NCI tool registries and open-challenge activities.

 描述（由申请人提供）：影像学在评估肿瘤对治疗的反应中发挥着关键作用；然而，目前使用的方法仍然非常有限，例如，RECIST 等标准是主观的，不能用于充分表征不规则的肿瘤体积。单独的测量不能解释详细的结构变化；并且来自选定肿瘤区域的特征，例如“热点”峰值增强，不能捕获整个肿瘤的信息，因此，当前的方法无法捕获多方面的信息。治疗效果，包括表型肿瘤异质性及其在治疗过程中的纵向变化，这越来越被认为是一个重要的预测指标，迄今为止，很少有研究探索使用更丰富的成像描述符，这可能会产生更强大的预测标志物。此外，很少有研究尝试结合多种预测指标。 -模态生物标志物，例如组织病理学和分子标志物成像，为特定肿瘤亚型和个体患者开发增强的预测模型。我们建议开发先进的计算工具，以实现i）提取新的多参数成像特征和ii。 ）通过可变形图像配准准确地表征新辅助治疗期间的纵向变化模式，因此我们的方法适合知识发现，用于在 SA1 中开发量化成像信息的多种可能方法中确定哪些成像参数具有最高的预测价值。基于相互显着性原理的鲁棒 4D 可变形图像配准方法，用于估计变换，这将使我们能够鲁棒地配准串行成像扫描，并获得解剖学上精确的纵向组织效应的时空参数图在 SA2 中，我们将通过执行多参数特征提取来分析整个肿瘤和正常组织的影响，包括一组丰富的形态、纹理、动力学和实质组织描述符。 与配准相结合将使我们能够全面捕获治疗期间动态变化的成像表型。在 SA3 中，我们将在一项主要的乳腺成像研究 I-SPY 1/ACRIN 6657 试验中测试我们的方法，我们将应用机器学习工具来识别高水平。影像模式的三维关联与组织病理学肿瘤亚型相结合，可以最好地预测病理完全缓解 (pCR) 和 5 年无病生存 (DFS) 在 SA4 中，我们将使用I-SPY 2/ACRIN 6698 试验，我们还将评估我们的特征对各种治疗的稳健性，我们的方法有望通过 1) 提高当前的影像标准来改变当前的个性化新辅助治疗模式。 2) 引入新的成像生物标志物，作为治疗反应和生存的早期预测因子，我们的工具将作为开源软件通过 NIH/NCI 工具注册和共享。开放挑战活动。

项目成果

Expert tumor annotations and radiomics for locally advanced breast cancer in DCE-MRI for ACRIN 6657/I-SPY1.

ACRIN 6657/I-SPY1 的 DCE-MRI 中局部晚期乳腺癌的专家肿瘤注释和放射组学。

• 发表时间: 2022-07-23
• 影响因子: 9.8
• 作者: Chitalia, Rhea;Pati, Sarthak;Bhalerao, Megh;Thakur, Siddhesh Pravin;Jahani, Nariman;Belenky, Vivian;McDonald, Elizabeth S;Gibbs, Jessica;Newitt, David C;Hylton, Nola M;Kontos, Despina;Bakas, Spyridon
• 通讯作者: Bakas, Spyridon