Predicting axillary lymph node metastases in breast carcinoma patients View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1996-06

AUTHORS

Poh Lian Choong, Christopher J. S. deSilva, Hugh J. S. Dawkins, Gregory F. Sterrett, Peter Robbins, Jennet M. Harvey, John Papadimitriou, Yianni Attikiouzel

ABSTRACT

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 analysis 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. More... »

PAGES

135-149

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf01806495

DOI

http://dx.doi.org/10.1007/bf01806495

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1003772763

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/8750581


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