Abram, T.J., Floriano, P.N., Christodoulides, N. et al. (30 more authors) (2016) ‘Cytology-on-a-chip’ based sensors for monitoring of potentially malignant oral lesions. Oral Oncology, 60. pp. 103-111. ISSN 1368-8375
Abstract
Despite significant advances in surgical procedures and treatment, long-term prognosis for patients with oral cancer remains poor, with survival rates among the lowest of major cancers. Better methods are desperately needed to identify potential malignancies early when treatments are more effective. Objective To develop robust classification models from cytology-on-a-chip measurements that mirror diagnostic performance of gold standard approach involving tissue biopsy. Materials and methods Measurements were recorded from 714 prospectively recruited patients with suspicious lesions across 6 diagnostic categories (each confirmed by tissue biopsy -histopathology) using a powerful new ‘cytology-on-a-chip’ approach capable of executing high content analysis at a single cell level. Over 200 cellular features related to biomarker expression, nuclear parameters and cellular morphology were recorded per cell. By cataloging an average of 2000 cells per patient, these efforts resulted in nearly 13 million indexed objects. Results Binary “low-risk”/“high-risk” models yielded AUC values of 0.88 and 0.84 for training and validation models, respectively, with an accompanying difference in sensitivity + specificity of 6.2%. In terms of accuracy, this model accurately predicted the correct diagnosis approximately 70% of the time, compared to the 69% initial agreement rate of the pool of expert pathologists. Key parameters identified in these models included cell circularity, Ki67 and EGFR expression, nuclear-cytoplasmic ratio, nuclear area, and cell area. Conclusions This chip-based approach yields objective data that can be leveraged for diagnosis and management of patients with PMOL as well as uncovering new molecular-level insights behind cytological differences across the OED spectrum.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 Elsevier. This is an author produced version of a paper subsequently published in Oral Oncology. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
Keywords: | Cytology; Oral cancer; Oral epithelial dysplasia; Microfluidic; High content analysis; Machine learning; Random forest; LASSO |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Clinical Dentistry (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 01 Aug 2016 15:22 |
Last Modified: | 22 Jul 2017 09:48 |
Published Version: | http://dx.doi.org/10.1016/j.oraloncology.2016.07.0... |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.oraloncology.2016.07.002 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:103196 |
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Filename: OA Oral Oncology 2016 - v60 p103-111 .pdf
Licence: CC-BY-NC-ND 4.0