Khloud A Elsharawy1,2, Michael S Toss1, Sara Raafat1, Graham Ball3, Andrew R Green1, Mohammed A Aleskandarany1, Leslie W Dalton4, Emad A Rakha1. 1. Academic Pathology, Division of Cancer and Stem Cells, School of Medicine, University of Nottingham, Nottingham, UK. 2. Department of Zoology, Faculty of Science, Damietta University, Damietta, Egypt. 3. School of Science and Technology, Nottingham Trent University, Nottingham, UK. 4. Department of Histopathology, South Austin Hospital, Austin, TX, USA.
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.
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.
Authors: Khloud A Elsharawy; Maryam Althobiti; Omar J Mohammed; Abrar I Aljohani; Michael S Toss; Andrew R Green; Emad A Rakha Journal: Breast Cancer Res Treat Date: 2020-11-08 Impact factor: 4.872
Authors: Anne-Marie Lüchtenborg; Patrick Metzger; Miguel Cosenza Contreras; Victor Oria; Martin L Biniossek; Franziska Lindner; Klemens Fröhlich; Ambrus Malyi; Thalia Erbes; Nicole Gensch; Jochen Maurer; Andreas Thomsen; Melanie Boerries; Oliver Schilling; Martin Werner; Peter Bronsert Journal: Breast Cancer Res Date: 2022-10-03 Impact factor: 8.408
Authors: Khloud A Elsharawy; Omar J Mohammed; Mohammed A Aleskandarany; Ayman Hyder; Hekmat L El-Gammal; Mohamed I Abou-Dobara; Andrew R Green; Leslie W Dalton; Emad A Rakha Journal: Br J Cancer Date: 2020-09-01 Impact factor: 9.075