Automated Image Guidance for Diagnosing Skin Cancer With Confocal Microscopy

使用共焦显微镜诊断皮肤癌的自动图像引导

• 批准号: 9315773
• 负责人: Jennifer G Dy
• 金额: $ 61.95万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9315773
• 关键词: Address; Adolescent; Adopted; Adoption; Adult; Algorithms; Area; Benign; Biometry; Biopsy; Cellular Morphology; Cessation of life; Child; Classification; Clinic; Clinical; Clinical Research; Collaborations; Computers; Confocal Microscopy; Cutaneous Lymphoma; Darkness; Data; Dermal; Dermatologist; Dermoscopy; Detection; Diagnosis; Diagnostic; Drops; Early Diagnosis; Ensure; Epidemiology; Glass; Hypersensitivity skin testing; Image; Image Analysis; Imaging technology; International; Italy; Lateral; Learning; Lesion; Machine Learning; Malignant - descriptor; Malignant Neoplasms; Memorial Sloan-Kettering Cancer Center; Methods; Microscope; Modality; Modeling; Mosaicism; Neoplasm Metastasis; Optics; Pathologist; Pathology; Patients; Pattern; Pigments; Protocols documentation; Public Health; Reader; Reading; Reproducibility; Research Personnel; Resolution; Site; Skin; Skin Cancer; Skin Carcinoma; Specificity; Standardization; Survival Rate; Technology; Testing; Time; Tissues; Training; Universities; Validation; Visual; Work; base; burden of illness; cancer diagnosis; cohort; diagnostic accuracy; image guided; innovation; melanoma; microscopic imaging; noninvasive diagnosis; optical imaging; public health relevance; quantitative imaging; reflectance confocal microscopy; screening; skin disorder; success; tool

 DESCRIPTION (provided by applicant): Melanoma is diagnosed in approximately 124,000 people and is responsible for about 10,000 deaths every year, in the USA. Dermatologists rely on visual and dermatoscopic examination to discriminate benign melanocytic lesions from malignant, resulting in high and highly variable benign-to-malignant biopsy ratios from 8:1 to 47:1, and millions of unnecessary biopsies of benign lesions. Reflectance confocal microscopy (RCM) imaging has been proven for noninvasively guiding diagnosis of melanoma in several large clinical studies. RCM imaging at the dermal-epidermal junction (DEJ) provides sensitivity of 92-88% and specificity of 71-84%. The specificity is 2 times superior to that of dermatoscopy. RCM imaging at the DEJ is now being implemented to rule out malignancy, reduce biopsy and guide treatment. However, this is currently at only a few sites, where there are highly trained experts who can ensure that imaging is appropriately performed and images are read correctly. These experts are a small international cohort of "early adopter" clinicians, who have worked with RCM technology during the past decade and have become highly skilled readers. For novice (non-expert) clinicians in the wider cohort who are keen to adopt RCM, learning to read images is challenging and requires substantial effort and time. Two major technical barriers underlie the dramatic variability in diagnostic accuracy among novice clinicians. Together they limit utility, reproducibility and wider adoption of RCM. The first is user dependent subjective variability in depths near the DEJ at which images are acquired, and the second is variability in interpretation of images. We propose to address these barriers with computational "multi-faceted" classification modeling (innovation), image analysis and machine learning algorithms. Our specific aims are: (1) to develop and evaluate algorithms for both dermatoscopic images and RCM depth-stacks, to enable automated standardized and consistent acquisition of RCM mosaics at the DEJ in melanocytic lesions; (2) to develop and evaluate algorithms to discriminate patterns of cellular morphology at the DEJ into two classes, benign lesions versus malignant (dysplastic lesions and melanoma); and (3) to test our algorithms on patients for acquisition of RCM mosaics and classification into those two groups, with statistical validation against pathology, with statistical validation against pathology. Preliminary studies show that our algorithms can delineate the DEJ with accuracy in the range ~3-13 μm in strongly pigmented dark skin and ~5-20 μm in lightly pigmented fair skin, and can detect cellular morphologic patterns with sensitivity in the range 67-80% and specificity 78-99%. Melanocytic lesions can be distinguished from the surrounding normal skin at the DEJ with 80% classification accuracy. We are a team of researchers from Memorial Sloan-Kettering Cancer Center, Northeastern University and University of Modena. Our success will produce standardized imaging and analysis approaches, to advance RCM for noninvasive detection of melanoma. Furthermore, these approaches can be useful for non-melanoma skin cancers, cutaneous lymphoma and other skin disorders (wider impact).

 描述（由申请人提供）：在美国，约有124，000人被诊断患有黑色素瘤，每年约有10，000人死亡。皮肤科医生依靠视觉和皮肤镜检查来区分良性黑色素细胞病变与恶性黑色素细胞病变，导致良性与恶性活检比例从8：1到47：1的高和高度可变，以及数百万不必要的良性病变活检。反射共聚焦显微镜（RCM）成像已被证明是非侵入性的指导诊断黑色素瘤在几个大型的临床研究。真皮-表皮交界处（DEJ）的RCM成像提供了92-88%的灵敏度和71- 84%的特异性。特异性比皮肤镜检查高2倍上级。在DEJ的RCM成像现在正在实施，以排除恶性肿瘤，减少活检和指导治疗。然而，目前只有少数几个地点有训练有素的专家，他们可以确保适当执行成像并正确读取图像。这些专家是一小群国际“早期采用者”临床医生，他们在过去十年中一直使用RCM技术，并已成为高度熟练的读者。对于热衷于采用RCM的更广泛人群中的新手（非专家）临床医生来说，学习阅读图像具有挑战性，需要大量的努力和时间。两个主要的技术障碍导致新手临床医生的诊断准确性存在巨大差异。它们一起限制了RCM的实用性、可重复性和更广泛的采用。第一个是在获取图像的DEJ附近的深度中的用户依赖的主观可变性，第二个是图像的解释中的可变性。我们建议通过计算“多方面”分类建模（创新），图像分析和机器学习算法来解决这些障碍。我们的具体目标是：（1）开发和评估皮肤镜图像和RCM深度叠加的算法，以实现在黑素细胞病变中DEJ处自动标准化和一致地采集RCM马赛克;（2）开发和评估将DEJ处的细胞形态模式区分为两类（良性病变与恶性病变）的算法（发育异常性病变和黑素瘤）;以及（3）测试我们的算法对患者的RCM马赛克的获取和分类到这两组中，具有针对病理学的统计学验证，具有针对病理学的统计学验证。初步研究表明， 算法可以描绘DEJ，在强烈着色的深色皮肤中精确度在~3-13 μm范围内，在轻度着色的白色皮肤中精确度在~5-20 μm范围内，并且可以检测细胞形态模式，灵敏度在67-80%范围内，特异性在78- 99%范围内。在DEJ上，黑素细胞病变可以与周围正常皮肤区分开来，分类准确率为80%。我们是来自纪念斯隆-凯特琳癌症中心，东北大学和摩德纳大学的研究人员团队。我们的成功将产生标准化的成像和分析方法，以推进RCM用于黑色素瘤的非侵入性检测。此外，这些方法可用于非黑色素瘤皮肤癌，皮肤淋巴瘤和其他皮肤疾病（影响更广泛）。