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Ian O. Ellis
Researcher at University of Nottingham
Publications - 1071
Citations - 84964
Ian O. Ellis is an academic researcher from University of Nottingham. The author has contributed to research in topics: Breast cancer & Cancer. The author has an hindex of 126, co-authored 1051 publications receiving 75435 citations. Previous affiliations of Ian O. Ellis include Mansoura University & Curie Institute.
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Journal Article
High Levels of Allele Loss at the FHIT and ATM Genes in Non-Comedo Ductal Carcinoma in Situ and Grade I Tubular Invasive Breast Cancers
TL;DR: Findings indicate that allele loss at FHIT and ATM may be an important early event in the development of sporadic breast cancer.
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The positive predictive value of mammographic signs: A review of 425 non-palpable breast lesions
Helen C. Burrell,Sarah E Pinder,A.R.M. Wilson,Andrew Evans,L.J. Yeoman,C. W. Elston,Ian O. Ellis +6 more
TL;DR: The mammographic features of non-palpable breast lesions are reviewed to identify factors which may improve the specificity of mammographic interpretation and reduce the number of open surgical biopsies for benign lesions.
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Predicting Invasion in Mammographically Detected Microcalcification
Mark J.C. Bagnall,Andrew Evans,A. Robin M. Wilson,Sarah E Pinder,H. Denley,J.G. Geraghty,Ian O. Ellis +6 more
TL;DR: Identification of those clusters diagnosed as DCIS by percutaneous biopsy which are likely to harbour an invasive component is possible and it would seem reasonable to consider staging the axilla at therapeutic surgery in these patients.
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A validated gene expression profile for detecting clinical outcome in breast cancer using artificial neural networks
Lee Lancashire,Lee Lancashire,Desmond G. Powe,Jorge S. Reis-Filho,Emad A. Rakha,Christophe Lemetre,Britta Weigelt,Tma Abdel-Fatah,Anthony R Green,R Mukta,Roger W. Blamey,E.C. Paish,Robert C. Rees,Ian O. Ellis,Graham Ball +14 more
TL;DR: The principal prognosticator, CA9, showed that it is capable of selecting an aggressive subgroup of patients who are known to have poor prognosis, using an artificial neural network bioinformatic approach.
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Cytological grading of breast carcinoma--a feasible proposition?
TL;DR: The aim of this study was to devise a system for grading breast carcinoma based on cytological features alone and to recommend patients for appropriate therapy.