Multicenter Quantitative MRI Assessment of Breast Cancer Therapy Response

乳腺癌治疗反应的多中心定量 MRI 评估

• 批准号: 10520051
• 负责人: WEI HUANG
• 金额: $ 57.89万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10520051
• 关键词: Acceleration; Adjuvant Chemotherapy; Assessment tool; Benchmarking; Biological Process; Blood Vessels; Breast; Breast Cancer Treatment; Breast Cancer therapy; Breast Magnetic Resonance Imaging; Cancer Burden; Cell membrane; Clinical; Clinical Data; Clinical Decision Support Systems; Clinical Trials; Computer software; Contrast Media; Data; Data Analyses; Data Set; Diffusion; Diffusion Magnetic Resonance Imaging; Dimensions; Drug Kinetics; Evaluation; Future; Goals; Head and Neck Cancer; Health; Image; Imaging Device; In complete remission; Kinetics; MRI Scans; Magnetic Resonance Imaging; Malignant Neoplasms; Mammary Neoplasms; Maps; Measurement; Measures; Metabolic; Methods; Modeling; Multicenter Studies; Neoadjuvant Therapy; Online Systems; Oregon; Outcome; Pathologic; Patients; Performance; Perfusion; Permeability; Physiological; Prediction of Response to Therapy; Procedures; Prospective Studies; Protocols documentation; Research; Residual Cancers; Science; Signal Transduction; Site; Software Tools; Solid Neoplasm; Speed; System; Testing; Therapy Evaluation; Time; Tissues; Training; Translations; Treatment Protocols; Treatment-related toxicity; Universities; Validation; Variant; Vendor; Water; cancer imaging; cancer therapy; cancer type; clinical application; clinical decision support; clinical decision-making; clinical practice; clinical translation; contrast enhanced; data acquisition; digital; early phase clinical trial; human data; imaging biomarker; imaging modality; improved; individual patient; individual response; magnetic resonance imaging biomarker; malignant breast neoplasm; molecular marker; non-invasive imaging; patient subsets; pharmacokinetic model; precision medicine; predicting response; predictive marker; predictive modeling; predictive tools; prospective; quantitative imaging; research clinical testing; response; treatment response; tumor

Project Summary Quantitative imaging of tumor biological functions have been shown superior to imaging tumor size for prediction and evaluation of cancer response to therapy. Conventionally used as a noninvasive imaging method to assess microvascular perfusion and permeability, dynamic contrast-enhanced (DCE) MRI is increasingly employed in research and early phase clinical trial settings to measure and, importantly, predict tumor response to treatment. The standard two- or three-parameter Tofts models (TMs) are the most commonly used for pharmacokinetic (PK) modeling of DCE-MRI data to estimate quantitative imaging biomarkers such as Ktrans and ve. However, the TM is suboptimal in that it ignores the real physiological phenomenon of water exchange between tissue compartments when quantifying tissue concentration of contrast agent from MRI signal intensities. The Shutter-Speed Model (SSM) developed by the Oregon Health & Science University (OHSU) group is a more comprehensive PK model, taking into account the intercompartmental water exchange kinetics. Recent single-center OHSU studies have demonstrated superior ability of SSM DCE-MRI for prediction and evaluation of therapy response in breast cancer compared to the TM. Furthermore, it was recently discovered that the SSM-exclusive parameter, τi (mean intracellular water lifetime), is a new imaging biomarker of metabolic activity, and was the only baseline (pre-treatment) marker predictive of response to neoadjuvant chemotherapy (NAC) in breast cancer and overall survival in head and neck cancer. τi also has the advantage of being significantly less sensitive to variation in arterial input function (AIF) than the conventional PK parameters. Using the data acquisition and analysis protocols optimized by the OHSU group, the overall goal of this project is to validate the robustness of SSM DCE-MRI as a quantitative imaging tool for assessment of cancer therapy response in a prospective study under a multicenter setting across three major MRI scanner platforms, using NAC treatment of breast cancer as the testing clinical application. Specifically, we will (1) implement the optimized SSM DCE-MRI data acquisition and analysis protocols and perform QA/QC in a multicenter setting; (2) conduct the multicenter prospective study to validate the utility of SSM DCE-MRI for prediction and evaluation of breast cancer response to NAC; and (3) refine an OHSU-developed web-based clinical decision support system by developing and incorporating a predictive model of therapy response that integrates imaging markers with clinical and histopathological data, and evaluate the system adaptability in clinical workflow.

项目摘要 肿瘤生物学功能的定量成像已经显示出上级于肿瘤大小的成像， 预测和评估癌症对治疗的反应。常规用作非侵入性成像 动态对比增强（DCE）MRI是评估微血管灌注和渗透性的一种方法， 越来越多地用于研究和早期临床试验环境，以测量，重要的是， 肿瘤对治疗的反应。标准的两参数或三参数Tofts模型（TM）是最常见的 通常用于DCE-MRI数据的药代动力学（PK）建模以估计定量成像 生物标志物如Ktranss和ve。然而，TM是次优的，因为它忽略了真实的生理 当定量组织浓度时，组织隔室之间的水交换现象 MRI信号强度。俄勒冈州卫生部开发的快门速度模型（SSM） 与科学大学（OHSU）组是一个更全面的PK模型，考虑到 隔室间水交换动力学。最近的单中心OHSU研究表明上级 SSM DCE-MRI预测和评价乳腺癌治疗反应的能力与 TM.此外，最近发现SSM专用参数τi（平均细胞内水） 是代谢活动的新的成像生物标志物，并且是唯一的基线（治疗前）标志物 预测乳腺癌对新辅助化疗（NAC）的反应和头部和 颈部癌症τi还具有对动脉输入功能的变化显著不敏感的优点 (AIF)常规PK参数。使用优化的数据采集和分析协议， OHSU小组，本项目的总体目标是验证SSM DCE-MRI作为定量 一项多中心前瞻性研究中评估癌症治疗反应的成像工具 在三个主要的MRI扫描仪平台上，使用NAC治疗乳腺癌作为临床试验， 应用程序.具体而言，我们将（1）实现优化的SSM DCE-MRI数据采集和分析 方案，并在多中心环境中进行QA/QC;（2）进行多中心前瞻性研究，以验证 SSM DCE-MRI用于预测和评价乳腺癌对NAC的反应的实用性;以及（3）改进 OHSU开发的基于网络的临床决策支持系统，通过开发和整合预测 将成像标志物与临床和组织病理学数据相结合的治疗反应模型，以及 评价系统在临床工作流程中的适应性。

项目成果

Evaluation of a Deep Learning Reconstruction for High-Quality T2-Weighted Breast Magnetic Resonance Imaging.

评估高质量T2加权乳腺磁共振成像的深度学习重建。

• DOI: 10.3390/tomography9050152
• 发表时间: 2023-10-18
• 期刊: Tomography (Ann Arbor, Mich.)
• 作者: Allen TJ;Henze Bancroft LC;Unal O;Estkowski LD;Cashen TA;Korosec F;Strigel RM;Kelcz F;Fowler AM;Gegios A;Thai J;Lebel RM;Holmes JH
• 通讯作者: Holmes JH

Automated Placement of Scan and Pre-Scan Volumes for Breast MRI Using a Convolutional Neural Network.

• DOI: 10.3390/tomography9030079
• 发表时间: 2023-05-10
• 期刊: Tomography (Ann Arbor, Mich.)
• 作者: Allen TJ;Henze Bancroft LC;Wang K;Wang PN;Unal O;Estkowski LD;Cashen TA;Bayram E;Strigel RM;Holmes JH
• 通讯作者: Holmes JH

An Anthropomorphic Digital Reference Object (DRO) for Simulation and Analysis of Breast DCE MRI Techniques.

• DOI: 10.3390/tomography8020081
• 发表时间: 2022-04-02
• 期刊: Tomography (Ann Arbor, Mich.)

Statistical considerations for repeatability and reproducibility of quantitative imaging biomarkers.

• DOI: 10.1259/bjro.20210083
• 发表时间: 2022
• 期刊: BJR open

Quantitative DCE-MRI prediction of breast cancer recurrence following neoadjuvant chemotherapy: a preliminary study.

新辅助化疗后乳腺癌复发的定量 DCE-MRI 预测：初步研究。

• DOI: 10.1186/s12880-022-00908-0
• 发表时间: 2022-10-20
• 期刊: BMC MEDICAL IMAGING
• 影响因子: 2.7
• 作者: Thawani, Rajat;Gao, Lina;Mohinani, Ajay;Tudorica, Alina;Li, Xin;Mitri, Zahi;Huang, Wei
• 通讯作者: Huang, Wei