Potential use of quantitative tissue phenotype to predict malignant risk for oral premalignant lesions

Abstract

The importance of early diagnosis in improving mortality and morbidity rates of oral squamous cell carcinoma (SCC) has long been recognized. However, a major challenge for early diagnosis is our limited ability to differentiate oral premalignant lesions (OPL) at high risk of progressing into invasive SCC from those at low risk. We investigated the potential of quantitative tissue phenotype (QTP), measured by high-resolution image analysis, to identify severe dysplasia/carcinoma in situ (CIS; known to have an increased risk of progression) and to predict progression to cancer within hyperplasia or mild/moderate dysplasia. We generated a nuclear phenotype score (NPS), a combination of five nuclear morphometric features that best discriminate 4,027 "normal" nuclei (selected from 29 normal oral biopsies) from 4,298 "abnormal" nuclei (selected from 30 SCC biopsies). This NPS was then determined for a set of 69 OPLs. Severe dysplasia/CIS showed a significant increase in NPS compared with hyperplasia or mild/moderate dysplasia. However, within the latter group, elevated NPS was strongly associated with the presence of high-risk loss of heterozygosity (LOH) patterns. There was a statistical difference between NPS of hyperplasia or mild/moderate dysplasia that progressed to cancer and those that did not. Individuals with a high NPS had a 10-fold increase in relative risk of progression. In the multivariate Cox model, LOH and NPS together were the strongest predictors for cancer development. These data suggest that QTP could be used to identify lesions that require molecular evaluation and should be integrated with such approaches to facilitate the identification of hyperplasia or mild/moderate dysplasia OPLs at high risk of progression.

Fig. 1

Quantitative Tissue Phenotype (QTP) of an oral dysplastic epithelium. A: A Region of Interest (ROI) is manually delineated according to pathologist diagnosis. Nuclei selected by the technician within the ROI are automatically segmented (in blue); B: Graphic representations of 3 of the 110 nuclear features assessed in this study, all of which measure DNA distribution in the nucleus. Top row: OD-Skewness_measures whether the nucleus is dark with light areas or light with dark areas; middle row: Fractal_area1 measures heterochromatin vs. euchromatin organization, i.e. large intensity contrast between highly condensed chromatin and non-condensed chromatin, bottom row: Long90_Run measures the fraction of nuclear diameter one can travel before an intensity change is encountered. C: Nuclei images and corresponding features are displayed for graphical and statistical representation and analysis.

Fig. 2

Images of the Region of Interest (ROI) of four oral mucosa specimens with the corresponding histogram distribution of texture feature Fractal_area1, used in the calculation of the NPS. Top row: normal epithelium; second row: non-progressing mild dysplasia; third row: progressing mild dysplasia; fourth row: SCC.

Fig. 3

Correlation of Nuclear Phenotype Score (NPS) with histopathology grade. Error bar represents 5^th and 95^th percentiles, box represents central 50^th percentile and black square represents the NPS Median. N: Normal; HMD: Hyperplasia, Mild, Moderate Dysplasia; Sev-CIS: Severe dysplasia/CIS; SCC: Squamous Cell Carcinoma.

Fig. 4

Correlation of NPS with progression to cancer for hyperplasia, mild and moderate dysplasia (HMD). (A) Box plots of NPS for progressing and non–progressing hyperplasia, and progressing and non-progressing mild/moderate dysplasia. Error bar represents 5th and 95th percentiles, box represents central 50th percentile and black square represents median NPS values. (B) Receiver Operating Characteristic Curve of NPS to identify progressing lesions with a cut-off value of 4.2 (1), 4.5 (2) and 4.7 (3). (C) Probability of having no progression to cancer, for specimens with a low NPS (NPS <4.5) and specimens with a High NPS (NPS > 4.5).