Predicting axillary lymph node metastases in breast carcinoma patients. 1996

P L Choong, and C J deSilva, and H J Dawkins, and G F Sterrett, and P Robbins, and J M Harvey, and J Papadimitriou, and Y Attikiouzel
Department of Electrical & Electronic Engineering, University of Western Australia, Nedlands.

Routine axillary dissection is primarily used as a means of assessing prognosis to establish appropriate treatment plans for patients with primary breast carcinoma. However, axillary dissection offers no therapeutic benefit to node negative patients and patients may incur unnecessary morbidity, including mild to severe impairment of arm motion and lymphedema, as a result. This paper outlines a method of evaluating the probability of harbouring lymph node metastases at the time of initial surgery by assessment of tumour based parameters, in order to provide an objective basis for further selection of patients for treatment or investigation. The novel aspect of this study is the use of Maximum Entropy Estimation (MEE) to construct probabilistic models of the relationship between the risk factors and the outcome. Two hundred and seventeen patients with invasive breast carcinoma were studied. Surgical treatment included axillary clearance in all cases, so that the pathologic status of the nodes was known. Tumour size was found to be significantly correlated (P < 0.001) to the axillary lymph node status in the multivariate anlaysis with age (P = 0.089) and vascular invasion (P = 0.08) marginally correlated. Using the multivariate model constructed, 38 patients were predicted to have risk of nodal metastases lower than 20%, of these only 4 (10%) patients had lymph node metastases. A comparison with the Multivariate Logistic Regression (MLR) was carried out. It was found that the predictive quality of the MEE model was better than that of the MLR model. In view of the small sample size, further verification of this model is required in assessing its practical application to a larger population.

UI MeSH Term Description Entries
D008207 Lymphatic Metastasis Transfer of a neoplasm from its primary site to lymph nodes or to distant parts of the body by way of the lymphatic system. Lymph Node Metastasis,Lymph Node Metastases,Lymphatic Metastases,Metastasis, Lymph Node
D008875 Middle Aged An adult aged 45 - 64 years. Middle Age
D001943 Breast Neoplasms Tumors or cancer of the human BREAST. Breast Cancer,Breast Tumors,Cancer of Breast,Breast Carcinoma,Cancer of the Breast,Human Mammary Carcinoma,Malignant Neoplasm of Breast,Malignant Tumor of Breast,Mammary Cancer,Mammary Carcinoma, Human,Mammary Neoplasm, Human,Mammary Neoplasms, Human,Neoplasms, Breast,Tumors, Breast,Breast Carcinomas,Breast Malignant Neoplasm,Breast Malignant Neoplasms,Breast Malignant Tumor,Breast Malignant Tumors,Breast Neoplasm,Breast Tumor,Cancer, Breast,Cancer, Mammary,Cancers, Mammary,Carcinoma, Breast,Carcinoma, Human Mammary,Carcinomas, Breast,Carcinomas, Human Mammary,Human Mammary Carcinomas,Human Mammary Neoplasm,Human Mammary Neoplasms,Mammary Cancers,Mammary Carcinomas, Human,Neoplasm, Breast,Neoplasm, Human Mammary,Neoplasms, Human Mammary,Tumor, Breast
D002277 Carcinoma A malignant neoplasm made up of epithelial cells tending to infiltrate the surrounding tissues and give rise to metastases. It is a histological type of neoplasm and not a synonym for "cancer." Carcinoma, Anaplastic,Carcinoma, Spindle-Cell,Carcinoma, Undifferentiated,Carcinomatosis,Epithelial Neoplasms, Malignant,Epithelioma,Epithelial Tumors, Malignant,Malignant Epithelial Neoplasms,Neoplasms, Malignant Epithelial,Anaplastic Carcinoma,Anaplastic Carcinomas,Carcinoma, Spindle Cell,Carcinomas,Carcinomatoses,Epithelial Neoplasm, Malignant,Epithelial Tumor, Malignant,Epitheliomas,Malignant Epithelial Neoplasm,Malignant Epithelial Tumor,Malignant Epithelial Tumors,Neoplasm, Malignant Epithelial,Spindle-Cell Carcinoma,Spindle-Cell Carcinomas,Tumor, Malignant Epithelial,Undifferentiated Carcinoma,Undifferentiated Carcinomas
D005260 Female Females
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D001365 Axilla Area of the human body underneath the SHOULDER JOINT, also known as the armpit or underarm. Armpit,Underarm
D012307 Risk Factors An aspect of personal behavior or lifestyle, environmental exposure, inborn or inherited characteristic, which, based on epidemiological evidence, is known to be associated with a health-related condition considered important to prevent. Health Correlates,Risk Factor Scores,Risk Scores,Social Risk Factors,Population at Risk,Populations at Risk,Correlates, Health,Factor, Risk,Factor, Social Risk,Factors, Social Risk,Risk Factor,Risk Factor Score,Risk Factor, Social,Risk Factors, Social,Risk Score,Score, Risk,Score, Risk Factor,Social Risk Factor
D015233 Models, Statistical Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc. Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D016015 Logistic Models Statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable. A common application is in epidemiology for estimating an individual's risk (probability of a disease) as a function of a given risk factor. Logistic Regression,Logit Models,Models, Logistic,Logistic Model,Logistic Regressions,Logit Model,Model, Logistic,Model, Logit,Models, Logit,Regression, Logistic,Regressions, Logistic

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