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

• 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
• 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' 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

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.

表面扩散类型的皮肤癌，例如恶性雀斑样黑色素瘤（LMM）和非黑色素瘤 皮肤癌（NMSC）主要发生在老年患者身上，具有弥漫性亚临床亚表面扩散， 难以检测的区域和边界定义不清的区域。为了治疗这些癌症，皮肤科医生- 定期进行大量的标测活检，以确定扩散和边缘，然后进行手术 切除术有很宽的“安全”边界。毫不奇怪，这种“盲目”的方法会导致 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 正常皮肤组织可以帮助解决这个问题的是反射共焦显微镜（RCM）成像， 直接在病人身上非侵入性地描绘边缘。RCM成像灵敏地检测体内皮肤癌 特异性80- 70%。2016年，医疗保险和医疗补助服务中心授予了reim- 用于皮肤的RCM成像的负担代码。RCM成像现在越来越多地用于非侵入性地 引导诊断，避免患者对良性病变进行不必要的活检。虽然二十年的努力 导致授予这些代码的重点是成像引导诊断，新兴的应用是在 成像来指导治疗。我们建议创建一种称为RCM视频拼接的方法， 在患者的大面积皮肤癌边缘上绘制地图，增加采样，准确性和nor- 组织异常创新将是在设计一个高度鲁棒（对组织扭曲和运动伪影） 和高速度（实时，秒）的方法，为RCM视频拼接：我们将开发一个光流ap. 使用一种新型的混合3阶段深度学习网络进行研究，该网络由8个参数组成， 和局部刚性和非刚性组织运动动力学，学习和适应可变的组织和斑点噪声控制， 条件，并预测和自动检测运动模糊伪影。根据PAR-18-009的要求，我们的 学术界和工业界的合作伙伴关系将为临床医生提供RCM视频拼接，以便在 床旁（翻译新奇）。我们提出的应用是指导手术切除，但方法 将产生更广泛的影响，用于指导新的和新兴的浅表皮肤微创非手术治疗 癌的在初步研究中，我们展示了实时速度（125毫秒）的RCM视频拼接。 每秒8帧），配准误差为1.02 ± 1.3像素（相对于 1000 x 1000像素。我们的具体目标是：（1）开发一个实时和强大的RCM视频拼接AP， 接近并纳入手持式共聚焦显微镜用于床边，（2）测试方法， 图像质量和临床可接受性，以及（3）对100名患者进行前瞻性测试，并提供术前视频- LMM边缘和浅表NMSC边缘的镶嵌，然后根据术后病理进行验证。 神学我们是一个高度协同的团队，来自纪念斯隆凯特琳癌症中心，东北大学， Versity和Caliber Imaging and Diagnostics（前身为Lucid Inc.），有着13年的合作记录