C
Cheryl Gillett
Researcher at King's College London
Publications - 146
Citations - 12223
Cheryl Gillett is an academic researcher from King's College London. The author has contributed to research in topics: Breast cancer & Cancer. The author has an hindex of 52, co-authored 136 publications receiving 11077 citations. Previous affiliations of Cheryl Gillett include Guy's and St Thomas' NHS Foundation Trust & Guy's Hospital.
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p53 immunoreactivity in breast cancer tissues does not deteriorate in stored paraffin sections
TL;DR: Results show that, contrary to recent reports, p53 antigenicity is not consistently diminished in optimally fixed stored sections if a sensitive immunohistochemical method is used with a robust, reliable p53 antibody.
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56P Lymph node germinal centres and B cell responses in triple-negative breast cancer
Elena Alberts,V. Boulat,F. Liu,Toni Hardiman,M.J. Li,Jelmar Quist,Cheryl Gillett,S. Pinder,Dinis Pedro Calado,Anita Grigoriadis +9 more
TL;DR: In this paper , the role of germinal centres (GCs) in the development of triple-negative breast cancer was investigated and characterisation of the B cell populations in human LNs and orthotopic in vivo models of TNBC.
APC mutations are sufficie colorectal adenomas
TL;DR: In this paper, the authors determined whether allelic loss at APC had any effect on the nearby a-catenin gene and found that loss on 5q in familial adenomatous polyposis adenomas rarely extended as far as c-Catenin.
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Multiscale deep learning framework captures systemic immune features in lymph nodes predictive of triple negative breast cancer outcome in large‐scale studies
Gregory Verghese,Mengyuan Li,Fangfang Liu,Amit Lohan,Nikhil Cherian Kurian,Swati Meena,Patrycja Gazinska,Aekta Shah,Aasiyah Oozeer,Mark Opdam,Sabine C. Linn,Cheryl Gillett,Elena Alberts,T. Hardiman,Samantha Jones,Selvam Thavaraj,J. Louise Jones,Roberto Salgado,Sarah E Pinder,Swapnil Rane,Amrita Sethi,Anita Grigoriadis +21 more
TL;DR: In this article , the authors used a deep learning framework to quantify morphological features in haematoxylin and eosin-stained lymph nodes on digitised whole slide images.
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Abstract P5-01-01: Multiscale Deep Learning framework to capture systemic immune features in lymph nodes predictive of triple negative breast cancer outcome
Gregory Verghese,Mengyuan Li,Fangfang Liu,Amit Lohan,Nikhil Cherian,Patrycja Gazinska,Aekta Shah,Aasiyah Oozeer,Cheryl Gillett,Elena Alberts,T. Hardiman,Roberto Salgado,Samantha Jones,Louise Jones,Selvam Thavaraj,Sarah E Pinder,Swapnil Rane,Amrita Sethi,Anita Grigoriadis +18 more
TL;DR: In this paper , a supervised multiscale deep learning framework named smuLymphNet was proposed to capture and quantify GCs and sinuses within lymph nodes from digitized Haematoxylin and Eosin-stained (H&E) whole slide images (WSIs) and show good concordance compared with an interpathologist Dice coefficient of manual annotations from four pathologists.