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.

项目概要/摘要 该学术-工业合作伙伴关系 (AIP) 项目的目标是展示专用技术的实用性 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乳腺癌的反应 新辅助化疗（NAC）同时进行动态对比增强磁共振成像（DCE-MRI）。 描述了 NAC 引起的肿瘤形态和血管分布的变化，dbPET 提供了补充 有关肿瘤代谢的信息可以在治疗过程的早期有力地预测治疗反应 我们将该项目的重点放在 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，可以准确绘制肿瘤内代谢变化图 仅需全身 PET 一半的放射性示踪剂剂量即可检测小病灶 重要的是，这种新系统可扩展 PET 的规模。 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。 I-SPY 2 TRIAL 的成像负责人（PI：Nola Hylton），这是一项旨在识别新疗法的临床试验 对于乳腺癌，我们处于独特的地位，可以集成和测试 FDG-dbPET 作为早期治疗的性能 作为学术-工业合作伙伴，UCSF 和 OncoVision 将共同努力 开发一种用户友好且经济高效的 dbPET 技术，可轻松应用于临床 在具体目标 1 中，我们将开发软件功能以实现标准化。 dbPET 图像配准和量化，以准确量化治疗的纵向变化。 具体目标 2，我们将获取 I-SPY 2 患者子集治疗前和治疗后 FDG-dbPET 图像 并临床评估肿瘤代谢指标（即优化的标准化摄取值，SUV）是否来自 dbPET 可以作为病理完全缓解的早期预测因子——相比或组合 我们将测试 DCE-MRI 的功能性肿瘤体积 (FTV) 指标的生物标志物性能。 我们还将探讨使用逻辑回归预测模型的 dbPET SUV 和组合 SUV+FTV。 将 dbPET 和 DCE-MRI 放射组学特征与乳腺癌生物标志物关联起来，以便识别 此外，我们的前瞻性研究的 dbPET 数据将用于 评估特定目标 1 中开发的软件功能。我们期望成功完成此任务 AIP 项目旨在在常规乳腺癌管理中使用 MAMMI dbPET 并生产一套 与肿瘤生物学相关的成像生物标志物及其对治疗的反应的变化。