Human papillomavirus–related (p16-positive) oropharyngeal squamous cell carcinoma patients develop recurrent disease, mostly distant metastasis, in approximately 10% of cases, and the remaining patients, despite cure, can have major morbidity from treatment. Identifying patients with aggressive versus indolent tumors is critical. Hematoxylin and eosin-stained slides of a microarray cohort of p16-positive oropharyngeal squamous cell carcinoma cases were digitally scanned. A novel cluster cell graph was constructed using the nuclei as vertices to characterize and measure spatial distribution and cell clustering. A series of topological features defined on each node of the subgraph were analyzed, and a random forest decision tree classifier was developed. The classifier (QuHbIC) was validated over 25 runs of 3-fold cross-validation using case subsets for independent training and testing. Nineteen (11.9%) of the 160 patients on the array developed recurrence. QuHbIC correctly predicted outcomes in 140 patients (87.5% accuracy). There were 23 positive patients, of whom 11 developed recurrence (47.8% positive predictive value), and 137 negative patients, of whom only 8 developed recurrence (94.2% negative predictive value). The best other predictive features were stage T4 (18 patients; 83.1% accuracy) and N3 nodal disease (10 patients; 88.6% accuracy). QuHbIC-positive patients had poorer overall, disease-free, and disease-specific survival (P<0.001 for each). In multivariate analysis, QuHbIC-positive patients still showed significantly poorer disease-free and disease-specific survival, independent of all other variables. In summary, using just tiny hematoxylin and eosin punches, a computer-aided histomorphometric classifier (QuHbIC) can strongly predict recurrence risk. With prospective validation, this testing may be useful to stratify patients into different treatment groups.
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Departments of *Pathology and Immunology
†Otolaryngology Head and Neck Surgery
∥Radiation Oncology, Washington University, St Louis, MO
§Division of Biostatistics
‡Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH
Presented at the United States and Canadian Academy of Pathology, 101st Annual Meeting, Vancouver, BC, Canada 2012.
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Conflicts of Interest and Source of Funding: Supported by the National Cancer Institute of the National Institutes of Health under award numbers R01CA136535-01, R01CA140772-01, and R21CA167811-01; the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under award number R43EB015199-01; the National Science Foundation under award number IIP-1248316; the QED award from the University City Science Center and Rutgers University; Biostatistics Core; Siteman Comprehensive Cancer Center, and NCI Cancer Center Grant P30 CA091842 for support for statistical analysis. Co-author A.M. is a cofounder and majority stake holder in Ibris Inc. and vascuVis Inc. and could benefit by any future commercialization of the image classifier(s) studied in this work. For the remaining authors none were declared.
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Correspondence: James S. Lewis Jr., MD, Department of Pathology and Immunology, Washington University School of Medicine, 660S. Euclid Ave., Campus Box 8118, St Louis, MO, 63110 (e-mail: email@example.com).