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

摘要 这一建议将有助于提高诊断黑色素瘤和黑色素细胞病变的准确性。黑色素瘤的发病率上升速度比任何其他癌症都快，仅今年一年，每50名美国成年人中就有1人被诊断出患有黑色素瘤。我们的研究团队注意到在解释黑色素细胞病变的皮肤活检时存在大量的诊断错误;病理学家对高达60%的侵袭性黑色素瘤病例不同意，这可能导致患者严重伤害。我们的建议使用计算机技术来分析玻璃载玻片的全载玻片数字图像，以提高黑素细胞病变的诊断。利用NIH正在进行的一项研究的数据，我们将对一组240例皮肤活检病例进行分析和研究，其中包括良性到侵袭性黑色素瘤诊断的全谱。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开发的参考共识诊断，160名执业美国社区病理学家将提供新的数据，提供一个独特丰富的临床数据库，这是同类数据库中最大的。该项目将包括新的计算技术，包括细胞级和建筑实体的检测，以及基于特征和深度神经网络分类器的组合使用。我们的具体目标是：1.在黑色素细胞皮肤病变的数字化全切片图像中检测细胞水平实体。2.检测黑色素细胞皮肤病变数字化全切片图像中的结构（建筑）实体。3.开发一种自动诊断系统，可将数字化载玻片图像分类为五种可能的诊断类别之一：良性;非典型病变;原位黑色素瘤;侵袭性黑色素瘤T1 a期;侵袭性黑色素瘤≥ T1 b期。在我们提出的研究中，我们创新地使用计算机图像分析算法和机器学习。这项技术有可能通过提供分析性的、不偏离的回顾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。

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.