Assessment of nodal involvement and survival analysis in breast cancer patients using image cytometric data: Statistical, neural network and fuzzy approaches

Anticancer Research

Volumn 22, Issue 1 A, 2002, Pages 433-438

  Seker, Huseyin ^a   Odetayo, Michael O ^a   Petrovic, Dobrila ^a   Naguib, Raouf N G ^a   Bartoli, C ^a   Alasio, L ^a   Lakshmi, M S ^a   Sherbet, G V ^a  

^a Institute for Clean Growth and Future Mobility   (United Kingdom)

Author keywords

Artificial neural networks; Fuzzy k nearest neighbour; Histological assessment; Logistic regression; Nodal involvement; Oncology; Prognosis; Survival analysis

Indexed keywords

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; BREAST CANCER; CANCER GRADING; CANCER GROWTH; CANCER SURVIVAL; CELL CYCLE G0 PHASE; CELL CYCLE G1 PHASE; CELL CYCLE G2 PHASE; CELL CYCLE S PHASE; FEMALE; HISTOLOGY; HUMAN; HUMAN TISSUE; IMAGE CYTOMETRY; LOGISTIC REGRESSION ANALYSIS; LYMPH NODE METASTASIS; MAJOR CLINICAL STUDY; METAPHASE; MODEL; PLOIDY; PREDICTION; PRIORITY JOURNAL; PROGNOSIS; RELIABILITY; SURVIVAL RATE;

BREAST NEOPLASMS; CELL CYCLE; FEMALE; FUZZY LOGIC; HUMANS; LYMPH NODES; LYMPHATIC METASTASIS; NEURAL NETWORKS (COMPUTER); PLOIDIES; PROGNOSIS; SURVIVAL ANALYSIS;

EID: 0036254780     PISSN: 02507005     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

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