title: Characterization of squamous cell carcinomas of the head and neck using methods of spatial statistics.
abstract: In the present study, 53 cases of squamous cell carcinomas of the head and neck were characterized by a quantitative histological texture analysis based on principles of spatial statistics. A planar tessellation of the epithelial tumour component was generated by a skeletonization algorithm. The size distribution of the virtual cells of this planar tessellation, and the size distribution of the profiles of the tumour cell nuclei were estimated in terms of area and boundary length. The intensity, the reduced second moment function (K-function) and the pair correlation function of the point process of the centroids of the profiles of the tumour cell nuclei were also estimated. For both purposes, it is necessary to correct for edge effects, which we consider in this paper in some detail. Specifically, the point patterns of the tumour cell nuclei were considered as realizations of a point process, where the points exist only in the epithelial tumour component (the permitted phase) and not in the stroma (the forbidden phase). The methods allow to characterize each individual tumour by a series of summary statistics. The total set of cases was then partitioned into two groups: 19 cases without lymph node metastases (pN0), and 34 nodal positive cases (pN1 or pN2). Statistical analysis showed no significant differences between the intensities, the mean K-functions and the mean pair correlation functions of the tumour cell nucleus profiles of the two groups. However, there were some significant differences between the sizes of the virtual cells and of the nucleus profiles of the nodal negative cases as compared to the nodal positive cases. In a logistic regression analysis, one of the quantitative nuclear size variables (mean nuclear area) was found to be a significant predictor of lymph node metastasis, in addition to tumour stage. The study shows the potential of methods of spatial statistics for objective quantitative grading of squamous cell carcinomas of the head and neck, and provides an example for modelling histological point patterns as realizations of planar point processes occupying a reference phase which is only a partial component of the total tissue.
authors.affiliation: Institute of Pathology, University of Ulm, Um, Germany.
authors.forename: T
authors.initials: T
authors.lastname: Mattfeldt
authors.affiliation: Medical Data Services/Biostatistics, Boehringer-Ingelheim Pharma GmbH & Co. KG, Biberach, Germany.
authors.forename: F
authors.initials: F
authors.lastname: Fleischer
mesh_terms: D002294:Carcinoma, Squamous Cell / Q000175:diagnosis* / Q000473:pathology*; D006258:Head and Neck Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D006651:Histocytochemistry / Q000379:methods*; D006801:Humans; D008853:Microscopy / Q000379:methods*; D062206:Spatial Analysis
keywords: Pattern recognition; point process; spatial statistics; squamous cell carcinoma; stereology; tessellation