Rhazes Lab, Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota; Kimia Lab, University of Waterloo, Waterloo, Ontario, Canada.
Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota.
Mod Pathol. 2024 Feb;37(2):100381. doi: 10.1016/j.modpat.2023.100381. Epub 2023 Nov 7.
Breast cancer is one of the most common cancers affecting women worldwide. It includes a group of malignant neoplasms with a variety of biological, clinical, and histopathologic characteristics. There are more than 35 different histologic forms of breast lesions that can be classified and diagnosed histologically according to cell morphology, growth, and architecture patterns. Recently, deep learning, in the field of artificial intelligence, has drawn a lot of attention for the computerized representation of medical images. Searchable digital atlases can provide pathologists with patch-matching tools, allowing them to search among evidently diagnosed and treated archival cases, a technology that may be regarded as computational second opinion. In this study, we indexed and analyzed the World Health Organization breast taxonomy (Classification of Tumors fifth ed.) spanning 35 tumor types. We visualized all tumor types using deep features extracted from a state-of-the-art deep-learning model, pretrained on millions of diagnostic histopathology images from the Cancer Genome Atlas repository. Furthermore, we tested the concept of a digital "atlas" as a reference for search and matching with rare test cases. The patch similarity search within the World Health Organization breast taxonomy data reached >88% accuracy when validating through "majority vote" and >91% accuracy when validating using top n tumor types. These results show for the first time that complex relationships among common and rare breast lesions can be investigated using an indexed digital archive.
乳腺癌是全球女性最常见的癌症之一。它包括一组具有多种生物学、临床和组织病理学特征的恶性肿瘤。乳房病变有超过 35 种不同的组织学形式，可以根据细胞形态、生长和结构模式进行组织学分类和诊断。最近，人工智能领域的深度学习在医学图像的计算机表示方面引起了广泛关注。可搜索的数字图谱可以为病理学家提供补丁匹配工具，使他们能够在显然已诊断和治疗的存档病例中进行搜索，这项技术可以被视为计算辅助诊断。在这项研究中，我们对涵盖 35 种肿瘤类型的世界卫生组织乳腺分类法（第五版）进行了索引和分析。我们使用从癌症基因组图谱存储库中数百万张诊断组织病理学图像预训练的最先进深度学习模型提取的深度特征来可视化所有肿瘤类型。此外，我们还测试了数字“图谱”作为搜索和匹配罕见病例的参考的概念。在通过“多数表决”验证时，对世界卫生组织乳腺分类法数据中的斑块相似性搜索的准确率>88%，当使用前 n 种肿瘤类型验证时准确率>91%。这些结果首次表明，使用索引数字档案可以研究常见和罕见乳房病变之间的复杂关系。
