Development of a cytology-based multivariate analytical risk index for oral cancer

Objectives The diagnosis and management of oral cavity cancers are often complicated by the uncertainty of which patients will undergo malignant transformation, obligating close surveillance over time. However, serial biopsies are undesirable, highly invasive, and subject to inherent issues with poor inter-pathologist agreement and unpredictability as a surrogate for malignant transformation and clinical outcomes. The goal of this study was to develop and evaluate a Multivariate Analytical Risk Index for Oral Cancer (MARIO) with potential to provide non-invasive, sensitive, and quantitative risk assessments for monitoring lesion progression. Materials and methods A series of predictive models were developed and validated using previously recorded single-cell data from oral cytology samples resulting in a “continuous risk score”. Model development consisted of: (1) training base classification models for each diagnostic class pair, (2) pairwise coupling to obtain diagnostic class probabilities, and (3) a weighted aggregation resulting in a continuous MARIO. Results and conclusions Diagnostic accuracy based on optimized cut-points for the test dataset ranged from 76.0% for Benign, to 82.4% for Dysplastic, 89.6% for Malignant, and 97.6% for Normal controls for an overall MARIO accuracy of 72.8%. Furthermore, a strong positive relationship with diagnostic severity was demonstrated (Pearson’s coefficient = 0.805 for test dataset) as well as the ability of the MARIO to respond to subtle changes in cell composition. The development of a continuous MARIO for PMOL is presented, resulting in a sensitive, accurate, and non-invasive method with potential for enabling monitoring disease progression, recurrence, and the need for therapeutic intervention of these lesions

Interobserver agreement in dysplasia grading: toward an enhanced gold standard for clinical pathology trials

Objective: Interobserver agreement in the context of oral epithelial dysplasia (OED) grading has been notoriously unreliable and can impose barriers for developing new molecular markers and diagnostic technologies. This paper aimed to report the details of a 3-stage histopathology review and adjudication process with the goal of achieving a consensus histopathologic diagnosis of each biopsy. Study Design: Two adjacent serial histologic sections of oral lesions from 846 patients were independently scored by 2 different pathologists from a pool of 4. In instances where the original 2 pathologists disagreed, a third, independent adjudicating pathologist conducted a review of both sections. If a majority agreement was not achieved, the third stage involved a face-to-face consensus review. Results: Individual pathologist pair κ values ranged from 0.251 to 0.706 (fair-good) before the 3-stage review process. During the initial review phase, the 2 pathologists agreed on a diagnosis for 69.9% of the cases. After the adjudication review by a third pathologist, an additional 22.8% of cases were given a consensus diagnosis (agreement of 2 out of 3 pathologists). After the face-to-face review, the remaining 7.3% of cases had a consensus diagnosis. Conclusions: The use of the defined protocol resulted in a substantial increase (30%) in diagnostic agreement and has the potential to improve the level of agreement for establishing gold standards for studies based on histopathologic diagnosis

‘Cytology-on-a-chip’ based sensors for monitoring of potentially malignant oral lesions

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