Evaluation of the invasion front pattern of squamous cell cervical carcinoma by measuring classical and discrete compactness. - Einenkel, J.; Braumann, U. D.; Horn, L. C.; Pannicke, N.; Kuska, J. P.; Schutz, A.; Hentschel, B.; Hockel, M. (Comput Med Imaging Graph, 2007)
The invasion front pattern of squamous cell carcinoma (SCC) is a conspicuous histological phenomenon, which is assessed without precise criteria. The current study was performed to introduce the classical (C(C)) and discrete compactness (C(D)) as new morphometric parameters for quantification of this pattern. A retrospective analysis of 76 surgically treated patients with cervical carcinoma was conducted and the pattern of invasion was qualitatively classified as closed, finger-like or diffuse, respectively, by two pathologists. After digitization of the histological slides with a field of view of 10.4 mm x 8.3mm, tumor areas were labeled and C(C) and C(D) were computed based on the drawings (binary images). Additionally, intraindividual variation of compactness was evaluated for 12 selected tumors. The qualitative pattern assessment by the pathologists was moderately reproducible with an interobserver agreement of 72% and a kappa coefficient of 0.44. The values of C(C) and C(D) referring to the invasion front patterns assigned by both pathologists were significantly different between the three classified groups (p< or =0.01 and p< or =0.0001), so that, both theoretically and in practice, compactness regards the same morphological feature. In due consideration of the analysis of the area under the ROC (receiver operating characteristic) curves and the variation coefficient of different tumor regions, C(D) is more suitable for practical use than C(C). Tumors with a microscopic invasion into the parametria and with lymph-vascular space invasion were found to have a lower value of C(D), which indicates a more diffuse pattern of invasion (p=0.028 and p=0.033). We conclude that the discrete compactness C(D) is a new and reproducible parameter for a computer assisted quantification of the invasion front pattern and, thus, defines a further phenotypic feature of SCC of the uterine cervix.
Keywords: *Algorithms; *Artificial Intelligence; Carcinoma, Squamous Cell/*pathology; Female; Humans; Image Enhancement/methods; Image Interpretation, Computer-Assisted/*methods; Imaging, Three-Dimensional/methods; Neoplasm Invasiveness/*pathology; Pattern Recognition, Automated/*methods; Reproducibility of Results; Sensitivity and Specificity; Uterine Cervical Neoplasms/*pathology