Confocal video-mosaicking microscopy to guide surgery of superficially spreading skin cancers

共聚焦视频马赛克显微镜指导浅表扩散皮肤癌的手术

基本信息

批准号：
10309506
负责人：
Octavia Irma Camps
金额：
$ 8.12万
依托单位：
SLOAN-KETTERING INST CAN RESEARCH
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-01 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10309506
关键词：
Address Algorithms Area Benign Biopsy Caliber Clinical Code Collaborations Dermatologist Detection Development Diagnosis Diagnostic Diffuse Excision Feedback Grant Hutchinson&apos s Melanotic Freckle Hybrids Image Learning Lesion Machine Learning Malignant - descriptor Malignant Neoplasms Maps Memorial Sloan-Kettering Cancer Center Microscope Microscopy Modeling Morphologic artifacts Mosaicism Motion Noise Normal tissue morphology Operative Surgical Procedures Optics Patients Public Health Research Personnel Resolution Safety Sampling Skin Skin Cancer Skin Carcinoma Skin Tissue Specificity Speed Standardization Surface Surgeon Surgical Pathology Testing Time Tissues United States Centers for Medicare and Medicaid Services Universities Validation Video Microscopy Visual automated algorithm base blind classification algorithm clinical imaging deep learning design image guided imaging approach in vivo innovation learning network machine learning algorithm microscopic imaging noninvasive diagnosis novel older patient parent project preservation prospective test reflectance confocal microscopy segmentation algorithm

项目摘要

1 FROM PARENT PROJECT R01CA240771 2 Superficially spreading types of skin cancers such as lentigo maligna melanomas (LMMs) and non-melanoma 3 skin cancers (NMSCs) occur mostly on older patients, with diffuse sub-clinical sub-surface spread over large 4 areas and with poorly defined margins that are difficult to detect. To treat these cancers, dermatologists rou- 5 tinely perform a large number of mapping biopsies to determine the spread and margins, followed by surgical 6 excision with wide "safety" margins. Not surprisingly, such a "blind" approach results in under-sampling of the 7 margins, over-sampling of normal skin, too many false positives and false negatives, and too much loss of 8 normal skin tissue. What may help address this problem is reflectance confocal microscopy (RCM) imaging to 9 noninvasively delineate margins, directly on patients. RCM imaging detects skin cancers in vivo with sensitivity 10 of 85-95% and specificity 80-70%. In 2016, the Centers for Medicare and Medicaid Services granted reim- 11 bursement codes for RCM imaging of skin. RCM imaging is now being increasingly used to noninvasively 12 guide diagnosis, sparing patients from biopsies of benign lesions. While the two-decade effort to date was fo- 13 cused on imaging-guided diagnosis, emerging applications are in imaging to guide therapy. We propose to 14 create an approach called RCM video-mosaicking, to noninvasively map skin cancer margins over large areas 15 on patients, with increased sampling, accuracy and sparing of normal tissue. The parent project specific aims 16 are (1) to develop a real-time and robust RCM video-mosaicking approach and incorporate into a handheld 17 confocal microscope for use at the bedside, (2) to test the approach for image quality and clinical acceptability, 18 and (3) to prospectively test on 100 patients, with video-mosaicking of LMM margins and superficial NMSC 19 margins, followed by validation against post-surgical pathology. 20 PROPOSED DIVERSITY SUPPLEMENT PROJECT FOR Ms. ANABEL ALFONSO 21 The proposed project for Ms. Anabel Alfonso will build upon the parent project and extend it in a completely 22 novel direction. In the parent project, RCM video-mosaics will be visually read by Mohs surgeons during sur- 23 gery. The innovation in Anabel’s project will be the development of a new machine learning-based algorithm 24 for automated detection and mapping of LMM margins in video-mosaics, toward automating, standardizing and 25 increasing the speed and efficiency of video-mosaicking for Mohs surgeons at the bedside. The specific aims 26 for Anabel’s project are to (1) develop an image classification and segmentation algorithm that automatically 27 analyzes RCM video-mosaics, highlights diagnostically significant areas (malignant versus normal) in LMM 28 margins and provides real-time diagnostic feedback to Mohs surgeons; (2) to test and validate the algorithm on 29 RCM videos and video-mosaics obtained from LMMs on 50 patients. Acquisition of the videos and video- 30 mosaics will occur during the parent project.

1 来自父项目 R01CA240771 2 表面扩散类型的皮肤癌，例如恶性雀斑样痣黑色素瘤 (LMM) 和非黑色素瘤 3 皮肤癌 (NMSC) 主要发生在老年患者中，具有弥漫性亚临床亚表面下扩散为了治疗这些癌症，皮肤科医生需要对 4 个边缘不明确的区域进行治疗。 5 精细地进行大量活检以确定扩散和边缘，然后进行手术 6 具有广泛“安全”裕度的切除毫不奇怪，这种“盲目”方法会导致采样不足。 7个margin，正常皮肤过度采样，假阳性和假阴性太多，损失太多 8 正常皮肤组织的反射共聚焦显微镜 (RCM) 成像可能有助于解决此问题。 9 无创地描绘边缘，直接在患者身上进行 RCM 成像，可灵敏地检测体内皮肤癌。 10 85-95% 和特异性 80-70% 2016 年，医疗保险和医疗补助服务中心授予了重新调整权。皮肤 RCM 成像的 11 个支付代码 RCM 成像现在越来越多地用于无创。 12 指导诊断，使患者免受良性病变的活检，而迄今为止的两个十年的努力是为了- 13 根据影像引导诊断，新兴应用是影像指导治疗。 14 创建了一种称为 RCM 视频马赛克的方法，以无创方式绘制大面积皮肤癌边缘图 15 针对患者，增加了采样、准确性并保留了正常组织。 16 是 (1) 开发实时且强大的 RCM 视频马赛克方法并将其整合到手持设备中 17 床边使用的共焦显微镜，(2) 测试该方法的图像质量和临床可接受性， 18 和 (3) 对 100 名患者进行前瞻性测试，对 LMM 边缘和浅表 NMSC 进行视频拼接 19 个切缘，然后根据术后病理进行验证。 20 为 ANABEL ALFONSO 女士提议的多元化补充项目 21 Anabel Alfonso 女士的拟议项目将建立在母项目的基础上，并将其全面扩展。 22 新颖的方向在母项目中，RCM 视频马赛克将由莫氏外科医生在现场进行视觉阅读。 23 Anabel 项目的创新将是开发一种新的基于机器学习的算法。 24 用于自动检测和映射视频马赛克中的 LMM 边缘，实现自动化、标准化和 25 提高莫氏外科医生床边视频拼接的速度和效率。 Anabel 项目的 26 项任务是 (1) 开发一种图像分类和分割算法，自动 27 个嵌入式 RCM 视频马赛克，突出显示 LMM 中具有诊断意义的区域（恶性与正常区域） 28 个边缘并向莫氏外科医生提供实时诊断反馈；(2) 测试和验证算法；从 LMM 获得的 50 名患者的 29 个 RCM 视频和视频马赛克。父项目期间将出现 30 个马赛克。