Elsevier

Modern Pathology

Volume 30, Issue 12, December 2017, Pages 1655-1665
Modern Pathology

Article
An oral cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for disease-specific survival

https://doi.org/10.1038/modpathol.2017.98Get rights and content
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Abstract

Oral cavity squamous cell carcinoma is the most common type of head and neck carcinoma. Its incidence is increasing worldwide, and it is associated with major morbidity and mortality. It is often unclear which patients have aggressive, treatment refractory tumors vs those whose tumors will be more responsive to treatment. Better identification of patients with high- vs low-risk cancers could help provide more tailored treatment approaches and could improve survival rates while decreasing treatment-related morbidity. This study investigates computer-extracted image features of nuclear shape and texture on digitized images of H&E-stained tissue sections for risk stratification of oral cavity squamous cell carcinoma patients compared with standard clinical and pathologic parameters. With a tissue microarray cohort of 115 retrospectively identified oral cavity squamous cell carcinoma patients, 50 were randomly chosen as the modeling set, and the remaining 65 constituted the test set. Following nuclear segmentation and feature extraction, the Wilcoxon rank sum test was used to identify the five most prognostic quantitative histomorphometric features from the modeling set. These top ranked features were then combined via a machine learning classifier to construct the oral cavity histomorphometric-based image classifier (OHbIC). The classifier was then validated for its ability to risk stratify patients for disease-specific outcomes on the test set. On the test set, the classifier yielded an area under the receiver operating characteristic curve of 0.72 in distinguishing disease-specific outcomes. In univariate survival analysis, high-risk patients predicted by the classifier had significantly poorer disease-specific survival (P=0.0335). In multivariate analysis controlling for T/N-stage, resection margins, and smoking status, positive classifier results were independently predictive of poorer disease-specific survival: hazard ratio (95% confidence interval)=11.023 (2.62–46.38) and P=0.001. Our results suggest that quantitative histomorphometric features of local nuclear architecture derived from digitized H&E slides of oral cavity squamous cell carcinomas are independently predictive of patient survival.

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Supplementary Information accompanies the paper on Modern Pathology website

James S Lewis and Anant Madabhushi: These authors are co-senior authors of this work.

Supplementary information

The online version of this article (doi:10.1038/modpathol.2017.98) contains supplementary material, which is available to authorized users.