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Improved prediction of metastasis in tongue carcinomas, combining vascular and nuclear tumor parameters
Author(s) -
Hannen Egied J. M.,
van der Laak Jeroen A. W. M.,
Manni Johannes J.,
Pahlplatz Martin M. M.,
Freihofer Hans Peter M.,
Slootweg Piet J.,
Koole Ronald,
de Wilde Peter C. M.
Publication year - 2001
Publication title -
cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.052
H-Index - 304
eISSN - 1097-0142
pISSN - 0008-543X
DOI - 10.1002/1097-0142(20011001)92:7<1881::aid-cncr1705>3.0.co;2-m
Subject(s) - metastasis , medicine , tongue , pathology , stain , cancer , staining
BACKGROUND Predicting the presence of metastasis, based on tumor or tumor‐related characteristics is of utmost importance. The authors studied the significance of tumor DNA features and tumor‐related angiogenesis to predict the occurrence of metastasis in squamous cell carcinomas (SCCs) of the tongue. METHODS Paraplast blocks from resection specimens of 20 metastasized and 20 nonmetastasized SCCs of the tongue with a minimum follow‐up of 24 months were used. Tissue sections were stained with anti‐CD34 monoclonal antibodies for vessel visualization, and according to Feulgen to stain DNA. Using image analysis, data from both stainings were computed for each of the 40 carcinomas. A logistic regression model to predict the presence of metastasis, based on vascular and nuclear morphology features, was developed. RESULTS The intratumor variation of chromatin condensation and the percentage vessels smaller than 5 μm in diameter were selected for the model. The model correctly predicted metastasis in 90% of patients and excluded metastasis correctly in 75% of nonmetastasized tumors. Taking into account the prevalence of metastasis in SCC of the tongue of between 30% and 60%, this means a predictive value for a negative outcome of between 95% and 83%. CONCLUSIONS The proposed model shows an improvement of predictive values compared with previous models with single parameters. Therefore, a multiparameter model appears to predict the multiparameter process of metastasis better. Cancer 2001;92:1881–7. © 2001 American Cancer Society.