Artificial intelligence in breast cancer histopathology. Issue 1 (8th December 2022)

• Record Type: Journal Article
• Authors: Chan, Ronald CK; To, Chun Kit Curtis; Cheng, Ka Chuen Tom; Yoshikazu, Tada; Yan, Lai Ling Amy; Tse, Gary M
• Other Names: Tse Gary M guestEditor.; Tan Puay Hoon guestEditor.; Rakha Emad A guestEditor.
• Is Part Of: Histopathology. Volume 82:Issue 1(2023)
• Page Start: 198
• Page End: 210
• Publication Date: 2022-12-08
• Subjects: artificial intelligence -- breast cancer -- literature review; Histology, Pathological -- Periodicals; 611.018
• DOI: 10.1111/his.14820
• Languages: English
• ISSNs: 0309-0167

Abstract

Abstract : This is a review on the use of artificial intelligence for digital breast pathology. A systematic search on PubMed was conducted, identifying 17, 324 research papers related to breast cancer pathology. Following a semimanual screening, 664 papers were retrieved and pursued. The papers are grouped into six major tasks performed by pathologists—namely, molecular and hormonal analysis, grading, mitotic figure counting, ki‐67 indexing, tumour‐infiltrating lymphocyte assessment, and lymph node metastases identification. Under each task, open‐source datasets for research to build artificial intelligence (AI) tools are also listed. Many AI tools showed promise and demonstrated feasibility in the automation of routine pathology investigations. We expect continued growth of AI in this field as new algorithms mature. Abstract : Artificial intelligence involvement in the assessment of breast cancer has come a long way and researchers have attempted to facilitate almost every step in routine histopathological assessment and are further able to identify novel prognostic factors. The graphical abstract has summerise all major uses and each will be elaborated in the main text.