Artificial intelligence in digital breast pathology: Techniques and applications

被引：123

作者：

Ibrahim, Asmaa ^{[1
,2
]}

Gamble, Paul ^{[3
]}

Jaroensri, Ronnachai ^{[3
]}

Abdelsamea, Mohammed M. ^{[4
]}

Mermel, Craig H. ^{[3
]}

Chen, Po-Hsuan Cameron ^{[3
]}

Rakha, Emad A. ^{[1
,2
]}

机构：

[1] Univ Nottingham, Sch Med, Div Canc & Stem Cells, Dept Histopathol, Nottingham NG5 1PB, England

[2] Nottingham Univ Hosp NHS Trust, Nottingham City Hosp, Nottingham NG5 1PB, England

[3] Google, Google Hlth, Palo Alto, CA USA

[4] Birmingham City Univ, Sch Comp & Digital Technol, Birmingham, W Midlands, England

来源：

BREAST | 2020年 / 49卷

关键词：

Breast cancer; AI; (Artificial intelligence); ML; (Machine learning); DL; (Deep learning); WSI; (Whole slide image); Digital; Pathology; Breast pathology; Applications; IMAGE-ANALYSIS; PRIMARY DIAGNOSIS; CANCER; CHALLENGES; VARIABILITY; CONCORDANCE; VALIDATION; EXPRESSION; HER-2/NEU; SPECIMENS;

D O I：

10.1016/j.breast.2019.12.007

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

Breast cancer is the most common cancer and second leading cause of cancer-related death worldwide. The mainstay of breast cancer workup is histopathological diagnosis - which guides therapy and prognosis. However, emerging knowledge about the complex nature of cancer and the availability of tailored therapies have exposed opportunities for improvements in diagnostic precision. In parallel, advances in artificial intelligence (AI) along with the growing digitization of pathology slides for the primary diagnosis are a promising approach to meet the demand for more accurate detection, classification and prediction of behaviour of breast tumours. In this article, we cover the current and prospective uses of AI in digital pathology for breast cancer, review the basics of digital pathology and AI, and outline outstanding challenges in the field. (C) 2019 The Author(s). Published by Elsevier Ltd.

引用

页码：267 / 273

页数：7

共 77 条

[31]

Haykin S., 1998, Neural Networks: A Comprehensive Foundation

[32] Relevance of Spatial Heterogeneity of Immune Infiltration for Predicting Risk of Recurrence After Endocrine Therapy of ER plus Breast Cancer [J].