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Oral Oncol. 2016 Sep;60:103-11. doi: 10.1016/j.oraloncology.2016.07.002. Epub 2016 Jul 20.

'Cytology-on-a-chip' based sensors for monitoring of potentially malignant oral lesions.

Author information

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

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.

KEYWORDS:

Cytology; High content analysis; LASSO; Machine learning; Microfluidic; Oral cancer; Oral epithelial dysplasia; Random forest

[Indexed for MEDLINE]
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