1
|
Karimi Jafarbigloo S, Danyali H. Nuclear atypia grading in breast cancer histopathological images based on CNN feature extraction and LSTM classification. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2021. [DOI: 10.1049/cit2.12061] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Affiliation(s)
- Sanaz Karimi Jafarbigloo
- Department of Electrical and Electronics Engineering Shiraz University of Technology Shiraz Iran
| | - Habibollah Danyali
- Department of Electrical Engineering‐Communication System Shiraz University of Technology Shiraz Iran
| |
Collapse
|
2
|
Elsharawy KA, Toss MS, Raafat S, Ball G, Green AR, Aleskandarany MA, Dalton LW, Rakha EA. Prognostic significance of nucleolar assessment in invasive breast cancer. Histopathology 2020; 76:671-684. [PMID: 31736094 DOI: 10.1111/his.14036] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 11/07/2019] [Accepted: 11/14/2019] [Indexed: 12/17/2022]
Abstract
AIMS Nucleolar morphometric features have a potential role in the assessment of the aggressiveness of many cancers. However, the role of nucleoli in invasive breast cancer (BC) is still unclear. The aims of this study were to investigate the optimal method for scoring nucleoli in IBC and their prognostic significance, and to refine the grading of breast cancer (BC) by incorporating nucleolar score. METHODS AND RESULTS Digital images acquired from haematoxylin and eosin-stained sections from a large BC cohort were divided into training (n = 400) and validation (n = 1200) sets for use in this study. Four different assessment methods were evaluated in the training set to identify the optimal method associated with the best performance and significant prognostic value. These were: (i) a modified Helpap method; (ii) counting prominent nucleoli (size ≥2.5 µm) in 10 field views (FVs); (iii) counting prominent nucleoli in five FVs; and (iv) counting prominent nucleoli in one FV. The optimal method was applied to the validation set and to an external validation set, i.e. data from The Cancer Genome Atlas (n = 743). Scoring prominent nucleoli in five FVs showed the highest interobserver concordance rate (intraclass correlation coefficient of 0.8) and a significant association with BC-specific survival (P < 0.0001). A high nucleolar score was associated with younger age, larger tumour size, and higher grade. Incorporation of nucleolar score in the Nottingham grading system resulted in a higher significant association with survival than the conventional grade. CONCLUSIONS Quantification of nucleolar prominence in five FVs is a cost-efficient and reproducible morphological feature that can predict BC behaviour and can provide an alternative to pleomorphism to improve BC grading performance.
Collapse
Affiliation(s)
- Khloud A Elsharawy
- Academic Pathology, Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, UK.,Department of Zoology, Faculty of Science, Damietta University, Damietta, Egypt
| | - Michael S Toss
- Academic Pathology, Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, UK
| | - Sara Raafat
- Academic Pathology, Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, UK
| | - Graham Ball
- School of Science and Technology, Nottingham Trent University, Nottingham, UK
| | - Andrew R Green
- Academic Pathology, Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, UK
| | - Mohammed A Aleskandarany
- Academic Pathology, Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, UK
| | - Leslie W Dalton
- Department of Histopathology, South Austin Hospital, Austin, TX, USA
| | - Emad A Rakha
- Academic Pathology, Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, UK
| |
Collapse
|