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）：成像在评价肿瘤对治疗的反应中起着关键作用;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病变;肿瘤体积测量本身不能解释详细的结构变化;来自选定肿瘤区域的特征，如“热点”峰值增强，不能捕获整个肿瘤的信息。因此，目前的方法无法捕捉治疗的多方面效果，包括表型肿瘤异质性及其在治疗期间的纵向变化，这越来越被认为是一个重要的预测指标。到目前为止，很少有研究探索使用更丰富的成像描述符，这可能导致更强大的预测标记。此外，很少有人尝试将联合收割机多模式生物标志物（例如成像与组织病理学和分子标志物）组合以开发针对特定肿瘤亚型和个体患者的增强的预测模型。我们建议开发先进的计算工具，使i）提取新的多参数成像签名和ii）通过变形图像配准在新辅助治疗期间准确表征其纵向变化模式。因此，我们的方法是面向知识发现，用于确定哪些成像参数具有最高的预测价值的许多可能的方式来量化成像提供的信息。在SA 1中，我们将开发强大的4D可变形图像配准方法，基于相互显着性的原则，用于估计变换，这将使我们能够鲁棒地配准序列成像扫描，并获得由治疗引起的纵向组织效应的解剖学上精确的时空参数图。在SA 2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学、纹理、动力学和实质组织描述符， 结合配准将允许我们在治疗期间全面捕获动态演变的成像表型。在SA 3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成像模式的高维关联，结合组织病理学肿瘤亚型，可以最好地预测病理完全缓解（pCR）和5年无病生存期（DFS）。在SA 4中，我们将通过I-SPY 2/ACRIN 6698试验独立测试我们的模型，在该试验中，我们还将评估我们的功能对各种治疗的稳健性。我们的方法有望通过以下方式改变当前个性化新辅助治疗的模式：1）改善当前基于成像的评估标准，2）引入新的成像生物标志物，这些生物标志物可以作为治疗反应和生存的早期预测因子，具有更高的价值。我们的工具将通过NIH/NCI工具注册和开放挑战活动作为开源软件共享。

项目成果

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

• DOI: 10.1038/s41597-022-01555-4
• 发表时间: 2022-07-23
• 期刊: SCIENTIFIC DATA
• 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