Development of Magnetic Resonance Fingerprinting (MRF) to Assess Response to Neoadjuvant Chemotherapy in Breast Cancer

开发磁共振指纹图谱 (MRF) 来评估乳腺癌新辅助化疗的反应

• 批准号: 10713097
• 负责人: Yong Chen
• 金额: $ 56.39万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10713097
• 关键词: 3-Dimensional; Acceleration; Adjuvant Chemotherapy; Adoption; Antibodies; Biological; Brain Mapping; Breast; Cancer Etiology; Cessation of life; Characteristics; Clinical; Computers; Consumption; Data; Development; Dictionary; Diffusion; Diffusion Magnetic Resonance Imaging; ERBB2 gene; Early Diagnosis; Eligibility Determination; Exhibits; Female; Fingerprint; Genetic; Goals; Heterogeneity; Image; In complete remission; Lesion; Magnetic Resonance; Magnetic Resonance Imaging; Mammary Gland Parenchyma; Mammary Neoplasms; Maps; Measurement; Measures; Methods; Monitor; Morphology; Multiparametric Analysis; Neoadjuvant Therapy; Operative Surgical Procedures; Pathologic; Patient Care; Patients; Phenotype; Physicians; Physics; Physiological; Prediction of Response to Therapy; Preparation; Prone Position; Relaxation; Reproducibility; Resolution; Sampling; Scanning; Standardization; Techniques; Therapeutic; Time; Treatment outcome; Variant; Visualization; Woman; algorithm training; breast exam; breast imaging; breast lesion; breast surgery; cancer diagnosis; chemotherapy; clinical translation; cloud based; cohort; convolutional neural network; cost; deep learning; diagnostic accuracy; early detection biomarkers; experimental study; healthy volunteer; hormone therapy; imaging biomarker; improved; individualized medicine; ineffective therapies; invention; learning strategy; magnetic field; magnetic resonance imaging biomarker; malignant breast neoplasm; novel; partial response; patient response; quantitative imaging; reconstruction; research clinical testing; research study; response; side effect; targeted treatment; therapy outcome; tissue mapping; tool development; treatment planning; treatment response; trend; triple-negative invasive breast carcinoma; tumor; volunteer

Abstract In women, breast cancer is the most commonly diagnosed cancer and leading cause of cancer related deaths worldwide, with approximately 2.3 million new cases and 685,000 deaths in 2020. Neoadjuvant chemotherapy (NAC) is commonly applied to reduce the tumor size before surgery for breast neoplasms. Unfortunately, due to the genetic and phenotypic heterogeneity of breast tumors, not all patients respond to conventional NAC. Currently, only about 22% of patients show pathologic complete response (pCR), while the remaining non-pCR patients show either partial response (54% of all patients) or no response to chemotherapy. Early prediction of tumor response to chemotherapy to identify non-responders could 1) reduce unnecessary side effects and costs related to ineffective therapy, and 2) help physicians tailor the treatment plan earlier to achieve better therapeutic outcomes and improve survival. Monitoring tumor response to chemotherapy is currently based on tumor size measured by physical exam, which is subjective, difficult to quantify, and most importantly, temporally delayed compared to underlying biological changes. Quantitative, repeatable and objective methods that could provide an early detection of tumor physiological changes before size changes could significantly improve treatment outcome and the quality of patient care. However, quantitative imaging poses significant technical challenges, which is rarely performed in the clinical setting. Here, we propose to leverage Magnetic Resonance Fingerprinting (MRF), a revolutionary new platform for quantitative MR that was invented by our team, to develop new imaging biomarkers for early assessment of treatment response in women with breast cancer. Our team has developed a breast MRF method to simultaneously generate quantitative 3D T1 and T2 maps in ~6 minutes with excellent reproducibility. We have also expanded our MRF method to simultaneous quantify T1, T2 and ADC maps of the brain with no image distortion. Here, we plan on optimizing this new relaxometry / diffusion MRF method specifically for women with breast cancer (Aim 1). Novel deep learning methods will be developed to provide a fast (<5 minute) and high resolution (1.2 mm isotropic) acquisition for whole-breast coverage along with an efficient post-processing pipeline based on cloud computation (Aim 2). Finally, we will evaluate the developed method for early prediction of treatment response in two patient cohorts with either HER2-positive or triple negative breast cancers (Aim 3). Upon successful completion of this project, the developed MRF technique will provide a practical quantitative breast exam for early prediction of treatment response to NAC and other treatment methods (hormone therapy, antibody-based target therapy, etc.) for women with breast cancer, with the ultimate goal to reduce ineffective treatment in eligible subjects and tailor the treatment methods for optimum therapeutic outcomes.

摘要 在女性中，乳腺癌是最常见的诊断癌症，也是癌症相关死亡的主要原因 2020年全球约有230万新发病例和68.5万例死亡。新辅助化疗 (NAC)通常用于在乳腺肿瘤手术前减小肿瘤大小。可惜由于 由于乳腺肿瘤的遗传和表型异质性，并非所有患者都对常规NAC有反应。 目前，只有约22%的患者显示病理完全缓解（pCR），而其余的非pCR患者则表现出病理完全缓解（pCR）。 患者显示对化疗的部分反应（所有患者的54%）或无反应。早期预测 肿瘤对化疗的反应，以确定无反应者可以1）减少不必要的副作用， 与无效治疗相关的成本，以及2）帮助医生更早地制定治疗计划， 治疗效果和提高生存率。监测肿瘤对化疗的反应目前是基于 通过体检测量的肿瘤大小是主观的，难以量化，最重要的是， 与潜在的生物学变化相比，时间延迟。定量、可重复和客观的方法 这可以提供肿瘤生理变化的早期检测， 改善治疗效果和患者护理质量。然而，定量成像技术 技术挑战，这在临床环境中很少进行。在这里，我们建议利用磁性 共振指纹（MRF）是一种革命性的定量MR新平台，由我们的 开发新的成像生物标志物，用于早期评估乳腺癌妇女的治疗反应， 癌我们的团队开发了一种乳腺MRF方法，可以同时生成定量3D T1和T2 在约6分钟内绘制地图，具有出色的再现性。我们还将MRF方法扩展到同时 量化大脑的T1、T2和ADC图，无图像失真。在这里，我们计划优化这个新的 弛豫/弥散MRF方法，专门用于乳腺癌女性（目的1）。新型深度学习 将开发方法，以提供快速（<5分钟）和高分辨率（1.2 mm各向同性）采集， 全乳房覆盖沿着基于云计算的高效后处理管道（目标2）。 最后，我们将在两个患者队列中评估所开发的早期预测治疗反应的方法 HER 2阳性或三阴性乳腺癌（Aim 3）。在成功完成该项目后， 所开发的磁共振成像技术将为早期预测治疗提供实用的定量乳腺检查 对NAC和其他治疗方法（激素治疗、基于抗体的靶向治疗等）的反应为 乳腺癌女性，最终目标是减少合格受试者的无效治疗， 最佳治疗效果的治疗方法。