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

• 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
• 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

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.

项目摘要/摘要 乳腺癌在2015年在美国夺走了40,000多人的生命，这是一个重要的健康 重点。乳房X线摄影和超声检查，当前筛查方法，敏感性低，阳性低 预测价值分别，特别是在乳房密集组织的患者中。因此，无创 区分良性和恶性病变的方法，可以与电流合并 严重需要筛选方式。通过更高级的筛选方法，该方法有所增加 检测早期恶性病变，为此，为乳腺癌治疗变得更加常规。 然而，术中冷冻部分的边距评估耗时，并且患有低灵敏度， 术后组织学分析留下了正缘的潜力，但与 再发生。因此，需要一种实时方法检测术中术中肿瘤边缘的方法。我们 建议使用光谱光声和荧光分子成像与临床上 - 针对新型乳腺癌标记物（B7-H3）的可翻译造影剂，无创区分 在筛查期间（AIM 1）和手术切除过程中术中的恶性组织正常（AIM） 3）。通过使用机器学习后处理，将提高这种成像方法的敏感性 自主检测分子B7-H3信号的算法（AIM 2）。总而言之，该提议将导致 当前的临床乳房成像和手术切除实践的重大变化以改善检测 和局灶性乳房病变的治疗。 实现这些研究目标所需的该计划的培训部分是由学员设计的 具有特定技术专长的指导者。威尔曼博士是翻译分子成像的专家 对比代理使用，而鲁宾博士是生物信息学，图像处理和机器学习专家 医疗想象目的。此外，该项目得到了一个多元化的咨询委员会的支持 在临床乳房成像（Debra Ikeda博士）中，光学成像和术中指导（Christopher博士 contag）和临床乳房手术（Irene Wapnir博士）。迄今为止，候选人已经建立了专业知识 光声，超声和荧光分子成像以及分子对比剂的发展以及 在她的研究生和博士后研究期间使用体内。她的长期职业目标包括发展 临床上可以翻译的光谱光声和荧光分子成像方法 结合新颖的对比剂用于癌症检测和分化。此外，她的研究还将 专注于开发机器学习算法以提高分子成像的灵敏度 用于治疗目的的方法以及适应她的方法。为她的独立准备 研究职业，培训计划包括机器学习，数字信号处理方面的正规教育， 光学成像，癌症生物学以及职业发展阶层和研究的道德行为。