Spectroscopic Photoacoustic Molecular Imaging for Breast Lesion Characterization

用于乳腺病变表征的光谱光声分子成像

基本信息

批准号：
9314864
负责人：
Katheryne E Wilson
金额：
$ 7.6万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-06-01 至 2019-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9314864
关键词：
Advisory Committees Algorithms Antibodies Benign Binding Binding Proteins Bioinformatics Biological Neural Networks Biopsy Breast Breast Cancer Detection Breast Cancer Model CD276 gene Callback Cancer Biology Cancer Detection Cessation of life Classification Clinic Clinical Contrast Media Data Detection Development Digital Signal Processing Dimensions Early Diagnosis Education Ethics Excision FDA approved Faculty Female Fluorescence Fluorescent Dyes Frozen Sections Goals Health Histologic Hyperplasia Image Individual Indocyanine Green Institution Lesion Linear Regressions Machine Learning Malignant - descriptor Malignant Neoplasms Mammary Gland Parenchyma Mammography Measures Medical Mentors Meta-Analysis Methodology Methods Modality Modeling Molecular Molecular Target Mus Noise Noninfiltrating Intraductal Carcinoma Operative Surgical Procedures Optics Patients Postoperative Period Predictive Value Preparation Prospective Studies ROC Curve Recurrence Research Research Personnel Scheme Sensitivity and Specificity Signal Transduction Specificity Statistical Methods Supervision Technical Expertise Techniques Testing Therapeutic Time Tissue Model Tissues Training Transgenic Mice Transgenic Organisms Translating Ultrasonography United States Validation Woman absorption antibody conjugate base breast imaging breast lesion breast surgery cancer biomarkers career career development clinically actionable clinically translatable contrast enhanced cost course development design diagnostic accuracy economic need fluorescence imaging formal learning image processing imaging agent imaging approach imaging modality improved in vivo informal learning malignant breast neoplasm molecular imaging mortality mouse model novel optical imaging overexpression prototype screening targeted agent targeted imaging tool tumor

项目摘要

PROJECT SUMMARY/ABSTRACT Claiming more than 40,000 lives in the United States in 2015, breast cancer presents an important health focus. Mammography and ultrasound, current screening methods, suffer from low sensitivity and low positive predictive value, respectively, particularly in patients with dense breast tissues. Therefore, a non-invasive method of distinguishing between benign and malignant lesions that could be incorporated with current screening modalities is critically needed. With more advanced screening methods, there is an increase in the detection of early malignant lesions, for which breast-conserving treatment has become more routine. However, intraoperative frozen-section margin assessment is time consuming and suffers from low sensitivity, while post-operative histological analysis leaves potential for positive margins, strongly correlated with reoccurrence. Therefore, a real-time method to detect tumor margins intraoperatively is critically needed. We propose using spectroscopic photoacoustic and fluorescence molecular imaging combined with a clinically- translatable contrast agent targeted to a novel breast cancer marker (B7-H3) to non-invasively distinguish normal from malignant tissues both during screening (aim 1) and intraoperatively during surgical resection (aim 3). The sensitivity of this imaging method will be increased with the use of machine learning post-processing algorithms to autonomously detect molecular B7-H3 signal (aim 2). In summary, this proposal will result in significant change to current clinical breast imaging and surgical resection practice to improve the detection and treatment of focal breast lesions. The training portion of this plan, required to accomplish these research goals, has been designed with trainee mentors with specific technical expertise. Dr. Willmann is an expert in translational molecular imaging and contrast agent use, while Dr. Rubin is an expert in bioinformatics, image processing, and machine learning for medical imagine purposes. Additionally, the project is supported by a diverse advisory committee with experts in clinical breast imaging (Dr. Debra Ikeda), optical imaging and intraoperative guidance (Dr. Christopher Contag), and clinical breast surgery (Dr. Irene Wapnir). To date, the candidate has developed expertise in photoacoustic, ultrasound, and fluorescence molecular imaging and molecular contrast agent development and in vivo use during her graduate and postdoctoral research. Her long term career goals include developing clinically translatable combined spectroscopic photoacoustic and fluorescence molecular imaging methods combined with novel contrast agents for cancer detection and differentiation. Additionally, her research will focus on developing machine learning algorithms for increasing the sensitivity of the molecular imaging approach as well as adapting her method for therapeutic purposes. In preparation for her independent research career, the training plan includes formal education in machine learning, digital signal processing, optical imaging, and cancer biology, as well as in career development classes and ethical conduct of research.

项目概要/摘要 2015 年，乳腺癌在美国夺去了超过 40,000 人的生命，是一种重要的健康问题重点。目前的筛查方法包括乳房X光检查和超声检查，但灵敏度低、阳性率低分别具有预测价值，特别是对于乳腺组织致密的患者。因此，一种非侵入式区分良性和恶性病变的方法可以与当前的相结合迫切需要筛查方式。随着更先进的筛查方法的出现，发现早期恶性病变，为此保乳治疗已变得更加常规。然而，术中冰冻切片切缘评估耗时且灵敏度低，而术后组织学分析留下了阳性切缘的潜力，与再次发生。因此，迫切需要一种术中实时检测肿瘤边缘的方法。我们建议使用光谱光声和荧光分子成像结合临床针对新型乳腺癌标志物 (B7-H3) 的可翻译造影剂，可进行非侵入性区分在筛查期间（目标 1）和手术切除期间的术中（目标 3）。随着机器学习后处理的使用，这种成像方法的灵敏度将会提高自主检测分子 B7-H3 信号的算法（目标 2）。总而言之，该提案将导致当前临床乳腺成像和手术切除实践的重大改变，以提高检测能力以及局部乳腺病变的治疗。完成这些研究目标所需的该计划的培训部分是与受训者一起设计的具有特定技术专长的导师。 Willmann 博士是转化分子成像领域的专家造影剂的使用，而 Rubin 博士是生物信息学、图像处理和机器学习方面的专家医学想象的目的。此外，该项目得到了由专家组成的多元化咨询委员会的支持临床乳腺成像（Debra Ikeda 博士）、光学成像和术中指导（Christopher 博士） Contag）和临床乳腺外科（Irene Wapnir 博士）。迄今为止，候选人已经发展了以下方面的专业知识：光声、超声和荧光分子成像和分子造影剂的开发和在她的研究生和博士后研究期间体内使用。她的长期职业目标包括发展临床可转化的光谱光声和荧光分子成像组合方法与新型造影剂结合用于癌症检测和分化。此外，她的研究将专注于开发机器学习算法以提高分子成像的灵敏度方法以及调整她的方法以达到治疗目的。为她的独立做准备研究生涯，培训计划包括机器学习、数字信号处理、光学成像、癌症生物学，以及职业发展课程和研究的道德行为。