Dedicated breast PET and MRI for characterization of breast cancer and its response to therapy

专用乳腺 PET 和 MRI，用于表征乳腺癌及其对治疗的反应

• 批准号: 10092115
• 负责人: Nola M. Hylton-Watson
• 金额: $ 58.01万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10092115
• 关键词: Adopted; Aftercare; Algorithms; Blood Vessels; Breast; Breast Diseases; Breast Magnetic Resonance Imaging; Cancer Burden; Cancer Center; Characteristics; Clinical; Clinical Management; Clinical Trials Design; Companions; Computer software; Data; Development; Disease; Dose; Early treatment; Enrollment; Evaluation; Genomics; Goals; Image; Image Analysis; In complete remission; Industrialization; Intervention; Investigation; Lead; Lesion; Logistic Regressions; Magnetic Resonance Imaging; Malignant Neoplasms; Mammography; Measurement; Metabolic; Metastatic breast cancer; Molecular; Monitor; Morphology; Neoadjuvant Therapy; Nonmetastatic; Outcome; Output; Pathologic; Patients; Performance; Phase II Clinical Trials; Positioning Attribute; Positron-Emission Tomography; Prediction of Response to Therapy; Prospective Studies; Research Personnel; Residual Cancers; Resolution; Role; Scanning; Selection for Treatments; Signal Transduction; Software Tools; Standardization; System; Technology; Testing; Texture; Time; Tracer; Translating; Translations; Tumor Biology; Tumor Volume; Work; attenuation; base; biological heterogeneity; biomarker performance; cancer biomarkers; cancer subtypes; chemotherapy; contrast enhanced; cost; cost effective; cytotoxicity; effective intervention; effective therapy; experience; image processing; image registration; imaging biomarker; improved; in vivo imaging system; industry partner; interest; malignant breast neoplasm; novel; novel therapeutics; performance tests; predicting response; predictive modeling; predictive test; prognostic value; radiologist; radiomics; radiotracer; research clinical testing; response; serial imaging; software development; tool; treatment response; tumor; tumor metabolism; uptake; user-friendly

PROJECT SUMMARY/ABSTRACT The objective of this academic-industrial partnership (AIP) project is to demonstrate the utility of dedicated breast positron emission tomography (dbPET) for characterizing primary breast cancers and their response to neoadjuvant chemotherapy (NAC). While dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) depicts changes in tumor morphology and vascularity in response to NAC, dbPET provides complementary information about tumor metabolism that can powerfully predict treatment response earlier in the course of therapy. We focus this project on MAMMI dbPET by OncoVision because it provides the crucial combination of high spatial resolution and sensitivity that can enable accurate intratumoral mapping of metabolic changes in small lesions with only half the radiotracer dose of whole-body PET. Importantly, this new system scales PET technology to be both economically and clinically feasible in the early (pre-metastatic) breast cancer setting. As the imaging lead (PI: Nola Hylton) of the I-SPY 2 TRIAL, a clinical trial designed to identify novel therapeutics for breast cancer, we are in a unique position to integrate and test the performance of FDG-dbPET as an early marker for treatment response. As academic-industrial partners, UCSF and OncoVision will work together to develop a user-friendly and cost-effective dbPET technology that can be easily adopted into the clinical workflow of most breast cancer centers. In Specific Aim 1, we will develop software capabilities to standardize dbPET image registration and quantification to accurately quantify longitudinal changes with treatment. In Specific Aim 2, we will acquire pre- and post-treatment FDG-dbPET images of a subset of I-SPY 2 patients and clinically evaluate whether tumor metabolic metrics (i.e., optimized standardized uptake values, SUV) from dbPET can act as early predictors of pathologic complete response — in comparison to, and in combination with, the functional tumor volume (FTV) metric from DCE-MRI. We will test the biomarker performance of dbPET SUV and combined SUV+FTV using logistic regression predictive models. We will also explore the association of dbPET and DCE-MRI radiomic features with breast cancer biomarkers in order to identify imaging features with prognostic value. In addition, our prospective study’s dbPET data will be used to evaluate the software capabilities developed in Specific Aim 1. We expect the successful completion of this AIP project to enable the use of MAMMI dbPET in routine breast cancer management and to produce a set of imaging biomarkers relevant to tumor biology and its change in response to treatment.

项目总结/摘要 这个学术-工业伙伴关系（AIP）项目的目标是展示专用 乳腺正电子发射断层扫描（dbPET）用于表征原发性乳腺癌及其对 新辅助化疗（NAC）。动态增强磁共振成像（DCE-MRI） 描述了肿瘤形态学和血管分布对NAC的反应，dbPET提供了补充 关于肿瘤代谢的信息，可以有力地预测治疗过程中早期的治疗反应。 疗法我们把这个项目的重点放在OncoVision的MAMMI dbPET上，因为它提供了关键的组合， 具有高空间分辨率和灵敏度，可以实现代谢变化的准确肿瘤内映射， 只有全身PET放射性示踪剂剂量的一半的小病变。重要的是，这个新系统可以扩展PET 该技术在早期（转移前）乳腺癌环境中在经济上和临床上都是可行的。作为 I-SPY 2试验的成像负责人（PI：Nola Hylton），该临床试验旨在确定新的治疗方法 对于乳腺癌，我们处于一个独特的位置，可以整合和测试FDG-dbPET的性能，作为早期诊断乳腺癌的方法。 治疗反应的标志物。作为学术-工业合作伙伴，UCSF和OncoVision将共同努力， 开发一种用户友好和具有成本效益的dbPET技术，可以很容易地应用于临床 大多数乳腺癌中心的工作流程。在具体目标1中，我们将开发软件功能， dbPET图像配准和量化，以准确量化治疗的纵向变化。在 具体目标2，我们将采集I-SPY 2患者子集的治疗前和治疗后FDG-dbPET图像 并临床评估肿瘤代谢度量（即，优化的标准化摄取值，SUV）， dbPET可以作为病理完全缓解的早期预测因子-与之相比，以及与之组合 与来自DCE-MRI的功能性肿瘤体积（FTV）度量。我们将测试 dbPET SUV和组合SUV+FTV使用逻辑回归预测模型。我们亦会探讨 dbPET和DCE-MRI放射组学特征与乳腺癌生物标志物的相关性， 具有预后价值的影像学特征。此外，我们的前瞻性研究的dbPET数据将用于 评估在具体目标1中开发的软件功能。我们期待着这一工作的圆满完成。 AIP项目，使MAMMI dbPET能够用于常规乳腺癌管理，并制作一套 与肿瘤生物学相关的成像生物标志物及其响应于治疗的变化。