Prediction of the axillary lymph node status in mammary cancer on the basis of clinicopathological data and flow cytometry

Medical and Biological Engineering and Computing

Volumn 42, Issue 6, 2004, Pages 733-739

  Mattfeldt, Torsten ^a   Kestler, H A ^a   Sinn, H P ^b  

^a UNIVERSITY OF ULM   (Germany)

^b UNIVERSITY OF HEIDELBERG   (Germany)

Author keywords

Bio informatics; Breast cancer; Flow cytometry; Learning vector quantisation; Mammary cancer; Support vector machine

Indexed keywords

CELLS; DATA REDUCTION; HISTOLOGY; MEDICAL IMAGING; SKIN; VECTORS;

AXILLARY LYMPH NODES; FEATURE SELECTION; PROGNOSTIC FACTORS; SKIN INFILTRATION;

TUMORS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; AXILLARY LYMPH NODE; BREAST CANCER; CANCER GRADING; CELL CYCLE G2 PHASE; CELL CYCLE S PHASE; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; DIAGNOSTIC VALUE; FLOW CYTOMETRY; HISTOPATHOLOGY; HUMAN; HUMAN TISSUE; LOGISTIC REGRESSION ANALYSIS; LYMPH NODE METASTASIS; MAJOR CLINICAL STUDY; MASTECTOMY; MATHEMATICAL COMPUTING; PREDICTION; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; STATISTICAL SIGNIFICANCE; TUMOR CELL; TUMOR CLASSIFICATION; TUMOR VOLUME; VALIDATION PROCESS;

ALGORITHMS; AXILLA; BREAST NEOPLASMS; FEMALE; FLOW CYTOMETRY; HUMANS; LYMPHATIC METASTASIS; NEOPLASM INVASIVENESS; NEURAL NETWORKS (COMPUTER); PLOIDIES; PREDICTIVE VALUE OF TESTS; PROGNOSIS; REGRESSION ANALYSIS; RETROSPECTIVE STUDIES;

EID: 10244235420     PISSN: 01400118     EISSN: None     Source Type: Journal    
DOI: 10.1007/BF02345205     Document Type: Article

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