权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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.

项目总结/文摘