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：
10651700
负责人：
Octavia Irma Camps
金额：
$ 63.68万
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 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10651700
关键词：
Ablation Address Area Benign Biopsy Caliber Clinic Clinical Code Collaborations Computer Vision Systems Confocal Microscopy Dermatologist Diagnosis Diagnostic Diffuse Engineering Excision Funding Opportunities Grant Hutchinson&apos s Melanotic Freckle Hybrids Image Lasers Learning Lesion Machine Learning Malignant Neoplasms Maps Memorial Sloan-Kettering Cancer Center Microscope Microscopy Modeling Morbidity - disease rate Morphologic artifacts Morphology Motion Noise Normal tissue morphology Operative Surgical Procedures Optics Pathologist Pathology Patients Pharmacotherapy Procedures Process Public Health Radiation therapy Research Research Personnel Safety Sampling Skin Skin Cancer Skin Carcinoma Skin Tissue Specificity Speed Standardization Surgeon Testing Time Tissue Model Tissues United States Centers for Medicare and Medicaid Services Universities Validation Video Microscopy Visit Visual blind cellular imaging clinical practice deep learning design expectation human imaging image guided image guided therapy imaging approach imaging detection in vivo industry partner innovation interest learning network meter microscopic imaging millisecond mosaic noninvasive diagnosis novel older patient preservation prospective test reflectance confocal microscopy response vector video delivery

项目摘要

Superficially spreading types of skin cancers such as lentigo maligna melanomas (LMMs) and non-melanoma skin cancers (NMSCs) occur mostly on older patients, with diffuse sub-clinical sub-surface spread over large areas and with poorly defined margins that are difficult to detect. To treat these cancers, dermatologists rou- tinely perform a large number of mapping biopsies to determine the spread and margins, followed by surgical excision with wide "safety" margins. Not surprisingly, such a "blind" approach results in under-sampling of the margins, over-sampling of normal skin, too many false positives and false negatives, and too much loss of normal skin tissue. What may help address this problem is reflectance confocal microscopy (RCM) imaging to noninvasively delineate margins, directly on patients. RCM imaging detects skin cancers in vivo with sensitivity of 85-95% and specificity 80-70%. In 2016, the Centers for Medicare and Medicaid Services granted reim- bursement codes for RCM imaging of skin. RCM imaging is now being increasingly used to noninvasively guide diagnosis, sparing patients from unnecessary biopsies of benign lesions. While the two-decade effort leading to the granting of these codes was focused on imaging-guided diagnosis, emerging applications are in imaging to guide therapy. We propose to create an approach called RCM video-mosaicking, to noninvasively map skin cancer margins over large areas on patients, with increased sampling, accuracy and sparing of nor- mal tissue. The innovation will be in designing a highly robust (against tissue warping and motion artifacts) and high speed (real-time, seconds) approach for RCM video-mosaicking: we will develop an optical flow ap- proach with a novel hybrid 3-stage deep learning network comprising of 8 parameters that will model global and local rigid and non-rigid tissue motion dynamics, learn and adapt to variable tissue and speckle noise con- ditions in patients, and predict and automatically detect motion blur artifacts. As required by PAR-18-009, our academic-industrial partnership will deliver RCM video-mosaicking to clinicians for real-time implementation at the bedside (translational novelty). Our proposed application is for guiding surgical excision, but the approach will have wider impact, for guiding new and emerging less invasive non-surgical treatments for superficial skin cancers. In a preliminary study, we demonstrated RCM video-mosaicking with real-time speed (125 millisec- onds per frame, 8 frames per second), and registration errors of 1.02 ± 1.3 pixels relative to field-of-view of 1000 x 1000 pixels. Our specific aims are (1) to develop a real-time and robust RCM video-mosaicking ap- proach and incorporate into a handheld confocal microscope for use at the bedside, (2) to test the approach for image quality and clinical acceptability, and (3) to prospectively test on 100 patients, with pre-surgical video- mosaicking of LMM margins and superficial NMSC margins, followed by validation against post-surgical pa- thology. We are a highly synergistic team from Memorial Sloan Kettering Cancer Center, Northeastern Uni- versity, and Caliber Imaging and Diagnostics (formerly, Lucid Inc.), with a 13-year record of collaboration.

表面散布的皮肤癌类型，例如Lentigo Maligna Melanomas（LMMS）和非黑色素瘤皮肤癌（NMSC）主要发生在老年患者上，弥散的亚链式地下表面分布在大型上区域以及难以检测到的边缘的定义较差。为了治疗这些癌症，皮肤科医生小巧执行大量的映射活检以确定扩散和边缘，然后进行手术切除宽阔的“安全”边缘。毫不奇怪，这种“盲目”方法导致了不足的采样边距，正常皮肤过度采样，过多的假阳性和假否定性以及太多的损失正常的皮肤组织。可能有助于解决此问题的是反射率共聚焦显微镜（RCM）成像直接对患者进行无创的划定边缘。 RCM成像以敏感性检测体内皮肤癌 85-95％和特异性80-70％。 2016年，Medicare和Medicaid服务中心授予了Reim- BULSEMENT代码用于皮肤的RCM成像。 RCM成像现在越来越多地用于非侵入性指导诊断，从不必要的良性病变活检中保留患者。而两个十年的努力导致授予这些代码的重点是成像引导的诊断，新兴应用程序正在成像指导治疗。我们建议创建一种称为RCM视频智能的方法，以无创在大区域上的皮肤癌边缘在患者的大面积上，随着采样，准确性和保留的提高麦芽组织。创新将是设计高度强大的（针对组织翘曲和运动伪像） RCM视频磁性的高速（实时，秒）方法：我们将开发光流ap- 使用新型混合三阶段深度学习网络完成8个参数，这些参数将模拟全局以及局部刚性和非韧性组织运动动力学，学习并适应可变的组织和斑点噪声患者的区分，并预测并自动检测运动模糊伪影。根据Par-18-009的要求，我们的学术工业合作伙伴关系将向临床医生提供RCM视频 - 以进行实时实施床边（翻译新颖）。我们提出的申请是指导外科手术惊喜，但是方法将产生更广泛的影响，以指导新的和新兴的侵入性非手术治疗浅表皮肤癌症。在一项初步研究中，我们以实时速度证明了RCM视频磁性（125毫秒 - 每帧的ONDS，每秒8帧），相对于视野的1.02±1.3像素的注册错误为1.02±1.3像素 1000 x 1000像素。我们的具体目的是（1）开发一种实时且强大的RCM视频效果。在床边使用并纳入手持式共聚焦显微镜，（2）测试该方法图像质量和临床可接受性，（3）对100名患者进行前瞻性测试，并具有手术前视频 - LMM边缘和浅表NMSC边缘的座椅，然后对后手术后PA-进行验证 Thology。我们是纪念斯隆·凯特林癌症中心的一支高度协同的团队，东北大学 Versity和Caliber Imaging and Diagnostics（以前是Lucid Inc.），合作记录为13年。