Prediction of the axillary lymph node status in mammary cancer on the basis of clinicopathological data and flow cytometry.
Axillary lymph node status is a major prognostic factor in mammary carcinoma. It is clinically desirable to predict the axillary lymph node status from data from the mammary cancer specimen. In the study, the axillary lymph node status, routine histological parameters and flow-cytometric data were retrospectively obtained from 1139 specimens of invasive mammary cancer. The ten variables: age, tumour type, tumour grade, tumour size, skin infiltration, lymphangiosis carcinomatosa, pT4 category, percentage of tumour cells in G2/M- and S-phases of the cell cycle, and ploidy index were considered as predictor variables, and the single variable lymph node metastasis pN (0 for pN0, or 1 for pN1 or pN2) was used as an output variable. A stepwise logistic regression analysis, with the axillary lymph node as a dependent variable, was used for feature selection. Only lymphangiosis carcinomatosa and tumour size proved to be significant as independent predictor variables; the other variables were non-contributory. Three paradigms with supervised learning rules (multilayer perceptron, learning vector quantisation and support vector machines) were used for the purpose of prediction. If any of these paradigms was used with the information from all ten input variables, 73% of cases could be correctly predicted, with specificity ranging from 82 to 84% and sensitivity ranging from 60 to 63%. If only the two significant input variables were used, lymphangiosis carcinomatosa and tumour diameter, the prediction accuracy was no worse. Nearly identical results were obtained by two different techniques of cross-validation (leave-one-out against ten-fold cross validation). It was concluded that: artificial neural networks can be used for risk stratification on the basis of routine data in individual cases of mammary cancer; and lymphangiosis carcinomatosa and tumour size are independent predictors of axillary lymph node metastasis in mammary cancer.
Mattfeldt
T
T
Department of Pathology, University of Ulm, Germany. torsten.mattfeldt@medizin.uni-ulm-de
Kestler
H A
HA
Sinn
H P
HP
D000465:Algorithms; D001365:Axilla; D001943:Breast Neoplasms / Q000473:pathology*; D005260:Female; D005434:Flow Cytometry / Q000379:methods*; D006801:Humans; D008207:Lymphatic Metastasis / Q000473:pathology; D009361:Neoplasm Invasiveness; D016571:Neural Networks, Computer; D011003:Ploidies; D011237:Predictive Value of Tests; D011379:Prognosis; D012044:Regression Analysis; D012189:Retrospective Studies