National Center for Tumor Diseases, German Cancer Research Center, Heidelberg, Germany.
Department of Dermatology, Heidelberg University, Mannheim, Germany; Skin Cancer Unit, German Cancer Research Center, Heidelberg, Germany.
Eur J Cancer. 2019 Sep;118:91-96. doi: 10.1016/j.ejca.2019.06.012. Epub 2019 Jul 18.
The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports on 25-26% of discordance for classifying a benign nevus versus malignant melanoma. A recent study indicated the potential of deep learning to lower these discordances. However, the performance of deep learning in classifying histopathologic melanoma images was never compared directly to human experts. The aim of this study is to perform such a first direct comparison.
A total of 695 lesions were classified by an expert histopathologist in accordance with current guidelines (350 nevi/345 melanoma). Only the haematoxylin & eosin (H&E) slides of these lesions were digitalised via a slide scanner and then randomly cropped. A total of 595 of the resulting images were used to train a convolutional neural network (CNN). The additional 100 H&E image sections were used to test the results of the CNN in comparison to 11 histopathologists. Three combined McNemar tests comparing the results of the CNNs test runs in terms of sensitivity, specificity and accuracy were predefined to test for significance (p < 0.05).
The CNN achieved a mean sensitivity/specificity/accuracy of 76%/60%/68% over 11 test runs. In comparison, the 11 pathologists achieved a mean sensitivity/specificity/accuracy of 51.8%/66.5%/59.2%. Thus, the CNN was significantly (p = 0.016) superior in classifying the cropped images.
With limited image information available, a CNN was able to outperform 11 histopathologists in the classification of histopathological melanoma images and thus shows promise to assist human melanoma diagnoses.
大多数癌症的诊断都是由经过委员会认证的病理学家根据显微镜下的组织活检做出的。最近的研究显示，个别病理学家之间存在很大的分歧。对于黑色素瘤，文献报告称，在将良性痣与恶性黑色素瘤分类时，有 25-26%的不一致。最近的一项研究表明，深度学习有潜力降低这些分歧。然而，深度学习在分类组织病理学黑色素瘤图像方面的性能从未与人类专家进行过直接比较。本研究旨在进行这样的首次直接比较。
一名专家病理学家根据当前指南对总共 695 个病变进行分类（350 个痣/345 个黑色素瘤）。仅对这些病变的苏木精和伊红（H&E）切片进行数字化处理，通过切片扫描仪，然后随机裁剪。总共 595 张图像用于训练卷积神经网络（CNN）。另外 100 张 H&E 图像用于测试 CNN 的结果，与 11 名病理学家进行比较。三个联合的 McNemar 检验用于比较 CNN 的测试运行在敏感性、特异性和准确性方面的结果，以检验其显著性（p<0.05）。
CNN 在 11 次测试运行中的平均敏感性/特异性/准确性为 76%/60%/68%。相比之下，11 名病理学家的平均敏感性/特异性/准确性为 51.8%/66.5%/59.2%。因此，CNN 在分类裁剪图像方面具有显著优势（p=0.016）。
在可用的图像信息有限的情况下，CNN 能够在组织病理学黑色素瘤图像的分类中优于 11 名病理学家，因此有望辅助人类黑色素瘤诊断。
