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（由适用提供）：成像在评估肿瘤对治疗的反应中起着至关重要的作用；但是，当前使用的方法仍然显着有限。例如，诸如recist之类的标准是主观的，不能用来充分表征不规则的病变。仅肿瘤体积测量不能说明详细的结构变化；以及所选肿瘤区域的特征，例如“热点”峰值增强，不会从整个肿瘤中捕获信息。因此，当前的方法无法捕获治疗的多方面影响，包括表型肿瘤异质性及其在治疗过程中的纵向变化，这越来越被认为是重要的预测指标。迄今为止，很少有研究使用更丰富的成像描述符探索，这可能会导致更强大的预测标记。此外，更少的尝试将多模式生物标志物（例如成像与组织病理学和分子标记）相结合，以开发针对特定肿瘤亚型和个体患者的增强预测模型。我们建议开发先进的计算工具，以使i）提取新颖的多参数成像特征，ii）ii）准确地表征了他们在新辅助处理期间通过可变形图像注册的纵向变化模式。因此，我们的方法是针对知识发现的，用于确定哪些成像参数具有最高的预测价值，以量化成像提供的信息。在SA1中，我们将基于相互级别的原理开发可靠的4D可变形图像登记方法，用于估算转化，这将使我们能够稳健地注册序列成像扫描并获得由治疗所指出的纵向组织效应的解剖学上精确的时空参数图。在SA2中，我们将通过进行多参数特征提取来分析全肿瘤和正常组织效应 注册的结合将使我们能够在治疗过程中全面捕获动态发展的成像表型。在SA3中，我们将在一项主要的乳房成像研究中测试我们的方法I-SPY 1/ACRIN 6657试验。我们将应用机器学习工具来确定成像模式的高维关联，并结合组织病理学肿瘤亚型，可以最好地预测病理完全反应（PCR）和5年无疾病生存（DFS）。在SA4中，我们将通过I-SPY 2/ACRIN 6698试验独立测试我们的模型，在那里我们还将评估我们的特征对潜水员治疗范围的鲁棒性。我们的方法有望通过1）提高基于成像的评估的当前标准以及2）引入新成像生物标志物的当前范例来转移当前的新辅助治疗，以提高当前的标准，这些新成像生物标志物可以作为治疗反应和生存的早期预测指标和生存的早期预测指标。我们的工具将通过NIH/NCI工具注册表和开放式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