Improving Melanoma Pathology Accuracy through Computer Vision Techniques - the IMPACT Study

通过计算机视觉技术提高黑色素瘤病理学的准确性 - IMPACT 研究

• 批准号: 9174605
• 负责人: JOANN G ELMORE
• 金额: $ 40.81万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9174605
• 关键词: Adult; Algorithms; Architecture; Area; Association Learning; Behavior; Benign; Biological Neural Networks; Biopsy; Caring; Cell Nucleus; Cessation of life; Characteristics; Clinical; Communities; Computational Technique; Computer Vision Systems; Computer-Assisted Image Analysis; Computers; Consensus; Data; Databases; Decision Making; Dermatopathology; Detection; Development; Diagnosis; Diagnostic; Diagnostic Errors; Diagnostic Imaging; Elderly; Evaluation; Event; Funding; Glass; Goals; Graph; Human; Image; Image Analysis; Incidence; Individual; International; Lead; Lesion; Machine Learning; Malignant Neoplasms; Methods; Microscopic; Mitotic; Pathologist; Pathology; Patients; Pattern; Performance; Physicians; Precancerous melanosis; Process; Property; Reference Standards; Research; Scanning; Skin; Slide; Specimen; Staging; System; Techniques; Technology; Tissues; Training; United States National Institutes of Health; Work; base; cancer diagnosis; diagnostic accuracy; digital imaging; improved; innovation; interest; maltreatment; melanocyte; melanoma; novel; skin lesion; stem; tool; visual tracking

ABSTRACT This proposal will help to improve the accuracy of diagnosing melanoma and melanocytic lesions. The incidence of melanoma is rising faster than any other cancer, and ~1 in 50 U.S. adults will be diagnosed with melanoma this year alone. Our research team has noted substantial diagnostic errors in interpreting skin biopsies of melanocytic lesions; pathologists disagree in up to 60% of cases of invasive melanoma, which can lead to substantial patient harm. Our proposal uses computer technology to analyze whole-slide digital images of glass slides in order to improve the diagnosis of melanocytic lesions. Using data from an ongoing NIH study, we will digitize and study a set of 240 skin biopsy cases that includes a full spectrum of benign to invasive melanoma diagnoses. Each biopsy case has a reference consensus diagnosis developed by a panel of three international experts in dermatopathology and new data will be available from 160 practicing U.S. community pathologists, providing a uniquely rich clinical database that is the largest of its kind. This project will include novel computational techniques, including the detection of both cellular-level and architectural entities, and the use of a combination of feature-based and deep neural network classifiers. Our specific aims are: 1. To detect cellular-level entities in digitized whole slide images of melanocytic skin lesions. 2. To detect structural (architectural) entities in digitized whole slide images of melanocytic skin lesions. 3. To develop an automated diagnosis system that can classify digitized slide images into one of five possible diagnostic classes: benign; atypical lesions; melanoma in situ; invasive melanoma stage T1a; and invasive melanoma stage ≥T1b. In our proposed study, we are innovatively using computer image analysis algorithms and machine learning. This technology has the potential to improve the diagnostic accuracy of pathologists by providing an analytical, undeviating review to assist humans in this difficult task.

抽象的 该提案将有助于提高黑色素瘤和黑色素细胞病变的诊断准确性。黑色素瘤的发病率上升速度比任何其他癌症都要快，仅今年就有大约五分之一的美国成年人被诊断出患有黑色素瘤。在解释黑色素细胞病变的皮肤活检时出现错误；病理学家对高达 60% 的侵袭性黑色素瘤病例存在不同意见，这可能会导致患者受到严重伤害。分析载玻片的全载玻片数字图像，以改进黑色素细胞病变的诊断。使用 NIH 正在进行的研究数据，我们将数字化和研究一组 240 个皮肤活检病例，其中包括全谱良性至侵袭性黑色素瘤。每个活检病例都有一个由三名国际皮肤病理学专家组成的小组制定的参考共识诊断，并且来自 160 名执业美国社区病理学家的新数据将提供独特的丰富临床信息。该项目将包括新颖的计算技术，包括细胞级和架构实体的检测，以及基于特征和深度神经网络分类器的组合的使用。 1. 检测黑素细胞皮肤病变的数字化全切片图像中的细胞级实体 2. 检测黑素细胞皮肤病变的数字化全切片图像中的结构（架构）实体 3. 开发可以分类的自动诊断系统。将幻灯片图像分为五种可能的诊断类别之一：良性；原位黑色素瘤；侵入性黑色素瘤阶段 T1a；和侵入性黑色素瘤阶段 ≥T1b。技术有潜力通过提供分析性、无偏差的审查来帮助人类完成这项艰巨的任务，从而提高病理学家的诊断准确性。