A simple index using video image analysis to predict disease outcome in primary breast cancer

Open Access
Publisher Website
Google Scholar
Abstract
Image analysis was used to investigate the prognostic significance of immunostaining for oestrogen receptor (ER), p53 tumour‐suppressor protein and tumour cell proliferation (MIB‐1) in a random cohort of 200 primary breast cancer patients with between 4 and 7 years of clinical follow‐up. Image measurements of the percentage of immunopositive cancer cell nuclei (% positive nuclear area) were recorded for the above tumour features for each patient. Assessment of relative risk using Cox's univariate analysis indicated that tumour size, number of cancer‐involved nodes, MIB‐1 and ER % positive nuclear area were significantly associated with breast cancer disease outcome, i.e., relapse‐free survival and overall survival. In multivariate analysis, tumour size, number of involved nodes, ER and MIB‐1 % positive nuclear area were retained as independent predictors of prognosis, depending on the image measurement cut‐point used. A prognostic model, which can be used without reference to nodal involvement, was constructed for tumour size, ER cut‐point of 30% positive nuclear area and MIB‐1 cut‐point of 10% positive nuclear area. Kaplan‐Meier analysis of this image‐based prognostic index identified 4 risk groups with predicted 5‐year overall survival rates of 93%, 83%, 76.7% and 61.5%. We conclude that image measurements of ER and proliferative rate can be combined with tumour size to construct a prognostic index which reliably predicts disease outcome in primary breast cancer without knowledge of the nodal status of the patient. Int. J. Cancer (Pred. Oncol.) 84:203–208, 1999.