Timothy J Abram1, Pierre N Floriano2, Nicolaos Christodoulides1, Robert James3, A Ross Kerr4, Martin H Thornhill5, Spencer W Redding6, Nadarajah Vigneswaran7, Paul M Speight8, Julie Vick3, Craig Murdoch5, Christine Freeman5, Anne M Hegarty9, Katy D'Apice9, Joan A Phelan4, Patricia M Corby10, Ismael Khouly11, Jerry Bouquot7, Nagi M Demian12, Y Etan Weinstock13, Stephanie Rowan6, Chih-Ko Yeh14, H Stan McGuff15, Frank R Miller16, Surabhi Gaur1, Kailash Karthikeyan1, Leander Taylor1, Cathy Le1, Michael Nguyen1, Humberto Talavera1, Rameez Raja1, Jorge Wong1, John T McDevitt17. 1. Rice University, Department of Bioengineering, Houston, TX, USA. 2. NeoTherma Oncology, Houston, TX, USA. 3. Rho Inc., Chapel Hill, NC, USA. 4. New York University College of Dentistry, Department of Oral and Maxillofacial Pathology, Radiology & Medicine, New York, NY, USA. 5. Academic Unit of Oral & Maxillofacial Medicine & Surgery, University of Sheffield School of Clinical Dentistry, Sheffield, UK. 6. The University of Texas Health Science Center at San Antonio, Department of Comprehensive Dentistry and Cancer Therapy and Research Center, San Antonio, TX, USA. 7. The University of Texas Health Science Center at Houston, Department of Diagnostic and Biomedical Sciences, Houston, TX, USA. 8. Academic Unit of Oral & Maxillofacial Pathology, University of Sheffield School of Clinical Dentistry, Sheffield, UK. 9. Unit of Oral Medicine, Charles Clifford Dental Hospital, Sheffield Teaching Hospitals National Health Service Foundation Trust, Sheffield, UK. 10. New York University School of Medicine, Department of Population Health and Radiation Oncology, New York, NY, USA. 11. New York University College of Dentistry, Bluestone Center for Clinical Research, New York, NY, USA. 12. The University of Texas Health Science Center at Houston, Department of Oral and Maxillofacial Surgery, Houston, TX, USA. 13. The University of Texas Health Science Center at Houston, Department of Otolaryngology-Head and Neck Surgery, Houston, TX, USA. 14. The University of Texas Health Science Center at San Antonio, Department of Comprehensive Dentistry and Cancer Therapy and Research Center, San Antonio, TX, USA; South Texas Veterans Health Care System, Geriatric Research, Education, and Clinical Center, San Antonio, TX, USA. 15. The University of Texas Health Science Center at San Antonio, Department of Pathology, San Antonio, TX, USA. 16. The University of Texas Health Science Center at San Antonio, Department of Otolaryngology-Head and Neck Surgery and Cancer Therapy and Research Center, San Antonio, TX, USA. 17. Rice University, Department of Bioengineering, Houston, TX, USA; Rice University, Department of Chemistry, Houston, TX, USA; New York University, Department of Biomaterials, New York, NY, USA. Electronic address: mcdevitt@nyu.edu.
Abstract
UNLABELLED: 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.
UNLABELLED: 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.
Authors: Paul M Speight; Timothy J Abram; Pierre N Floriano; Robert James; Julie Vick; Martin H Thornhill; Craig Murdoch; Christine Freeman; Anne M Hegarty; Katy D'Apice; A Ross Kerr; Joan Phelan; Patricia Corby; Ismael Khouly; Nadarajah Vigneswaran; Jerry Bouquot; Nagi M Demian; Y Etan Weinstock; Spencer W Redding; Stephanie Rowan; Chih-Ko Yeh; H Stan McGuff; Frank R Miller; John T McDevitt Journal: Oral Surg Oral Med Oral Pathol Oral Radiol Date: 2015-06-17
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Authors: Michael P McRae; Sayli S Modak; Glennon W Simmons; Denise A Trochesset; A Ross Kerr; Martin H Thornhill; Spencer W Redding; Nadarajah Vigneswaran; Stella K Kang; Nicolaos J Christodoulides; Craig Murdoch; Steven J Dietl; Roger Markham; John T McDevitt Journal: Cancer Cytopathol Date: 2020-02-07 Impact factor: 5.284
Authors: Tanya Walsh; Richard Macey; Alexander R Kerr; Mark W Lingen; Graham R Ogden; Saman Warnakulasuriya Journal: Cochrane Database Syst Rev Date: 2021-07-20
Authors: Timothy J Abram; Curtis R Pickering; Alexander K Lang; Nancy E Bass; Rameez Raja; Cynthia Meena; Amin M Alousi; Jeffrey N Myers; John T McDevitt; Ann M Gillenwater; Nadarajah Vigneswaran Journal: Transl Oncol Date: 2018-02-24 Impact factor: 4.243
Authors: Michael P McRae; Glennon W Simmons; Nicolaos J Christodoulides; Zhibing Lu; Stella K Kang; David Fenyo; Timothy Alcorn; Isaac P Dapkins; Iman Sharif; Deniz Vurmaz; Sayli S Modak; Kritika Srinivasan; Shruti Warhadpande; Ravi Shrivastav; John T McDevitt Journal: medRxiv Date: 2020-04-22