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

Abstract

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.

Keywords: Cytology; Model ensembles; Multi-class classification; Oral cancer; Risk assessment.

Figure 1 -. Risk stratification diagram and “Cytology-on-chip” workflow.

Panel I: A) Suspicious lesion is sampled via “brush sample” technique, B/C) single cells are captured on a nano-porous membrane embedded within a microfluidic channel, D) multispectral fluorescence images are recorded across a raster-scan of the membrane, E) algorithms identify cellular boundaries based on signal contrast, and F) regions of interest (ROIs) are extracted for quantification (scale bar = 100 μm). Panel II: The 7-stage diagnostic spectrum proposed by the 2005 WHO guidelines [25] displayed as a continuous number line. A) Binary risk assessment in the primary clinical setting scenario, where the main goal is to refer suspicious lesions for biopsy [10], B) Continuous score of the MARIO.

Figure 2 -. MARIO Performance. Barplots scaled to class densities (y-axis) across the MARIO (x-axis) for (A) Training and (B) Test datasets.

Color-coding represents the true patient class according to histopathology. Vertical dashed lines represent optimal cut-points for discretizing the continuous score into disease class domains based on minimizing class entropy in the training dataset. C) Box-plot of MARIO values (y-axis) across the 6 different diagnostic categories (center line = median value, top/bottom box = inter-quartile range).

Figure 3 -. Results from single-cell calibration exercise and phenotype query.

Panel I) Representative images of cell phenotypes for frequency tables in Panel II: A) Medium-sized rounded cell with enlarged nuclei, B) Large, normal-appearing squamous cell, C) Small, highly-circular cell, D) Leukocyte, E) lone nuclei. Panel II) Distribution of phenotype frequencies for patients with the identified range of risk scores (Blue: 0–25, Orange: 30–60, Red: 75–100). Left axis = A, B; Right axis = C, D, E. Error bars = standard deviation of phenotype frequency per patient. Panel III: Results from cell reassignment simulation where solid lines represent median MARIO values for each of 10 randomly selected healthy volunteer samples across the increasing percentage of their cells exchanged for cells from a corpus of OSCC patient cells (x-axis). Gray boundaries surrounding each line represents +/− standard error across 10 replicates.