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：
10203886
负责人：
Octavia Irma Camps
金额：
$ 64.2万
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/10203886
关键词：
Ablation Address Area Benign Biopsy Caliber Clinic Clinical Code Collaborations Computer Vision Systems Confocal Microscopy Dermatologist Diagnosis Diagnostic Diffuse 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 Mosaicism 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 Surface Surgeon Surgical Pathology 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 in vivo industry partner innovation interest learning network microscopic imaging noninvasive diagnosis novel older patient preservation prospective test reflectance confocal microscopy response vector

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.

浅表扩散类型的皮肤癌，例如恶性雀斑样痣黑色素瘤 (LMM) 和非黑色素瘤皮肤癌（NMSC）主要发生在老年患者身上，弥漫性亚临床亚表面下扩散到大范围内。为了治疗这些癌症，皮肤科医生需要进行常规治疗。精细地进行大量活检以确定扩散和边缘，然后进行手术毫不奇怪，这种“盲目”方法会导致采样不足。边缘、正常皮肤的过度采样、太多的假阳性和假阴性以及太多的损失反射共焦显微镜（RCM）成像可能有助于解决这个问题。 RCM 成像直接在患者身上无创地描绘边缘，可灵敏地检测体内皮肤癌。 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 成像的支付代码 RCM 成像现在越来越多地用于无创性。指导诊断，使患者免于对良性病变进行不必要的活检，同时花费了两年的努力。导致授予这些代码的重点是影像引导诊断，新兴应用正在我们建议创建一种称为 RCM 视频马赛克的方法，以非侵入性方式进行治疗。绘制患者大面积皮肤癌边缘图，提高采样率、准确性并减少正常情况创新将在于设计高度稳健的（防止组织扭曲和运动伪影）用于 RCM 视频马赛克的高速（实时，秒）方法：我们将开发一种光流应用程序采用新颖的混合三阶段深度学习网络，该网络包含 8 个参数，可对全局进行建模以及局部刚性和非刚性组织运动动力学，学习并适应可变组织和散斑噪声控制根据我们的 PAR-18-009 的要求，预测并自动检测运动模糊伪影。学术与工业合作伙伴关系将向工头提供 RCM 视频马赛克，以便实时实施床边（转化新颖性）。我们提出的应用程序是用于指导手术切除，但方法。将产生更广泛的影响，指导新兴的浅表皮肤微创非手术治疗在一项初步研究中，我们展示了实时速度（125 毫秒）的 RCM 视频马赛克。每帧 8 帧），相对于视场的配准误差为 1.02 ± 1.3 像素 1000 x 1000 像素。我们的具体目标是 (1) 开发实时且强大的 RCM 视频马赛克应用程序。接近并合并到手持式共焦显微镜中以在床边使用，(2) 测试该方法图像质量和临床可接受性，以及 (3) 通过术前视频对 100 名患者进行前瞻性测试 LMM 边缘和浅表 NMSC 边缘的镶嵌，然后针对术后 pa 进行验证我们是来自东北大学纪念斯隆凯特琳癌症中心的一支高度协同的团队。 versity 和 Calibre Imaging and Diagnostics（前身为 Lucid Inc.），拥有 13 年的合作记录。