BACKGROUND: Automated image capture and analysis (AICA) has not previously been used to measure the size of nucleoli in uveal melanoma. In this study, the measurements were tested for a possible association with patient outcome. METHODS: Sections from 63 uveal melanomas were stained using the silver stain for nucleolar organizing regions (AgNOR). AICA was used to measure the following five nucleolar features in ten microscopic fields of the tumor: area, circularity, maximum diameter, width, and length of the perimeter. For each tumor, the mean and standard deviation of each of the features were calculated based on all the nucleoli and on subsets of nucleoli with larger areas. For the five nucleolar features the mean of the largest value (MLV) in each of the ten fields was calculated. For comparison, a related visually measured nucleolar feature (MLN) was obtained from hematoxylin and eosin stained sections using a filar micrometer. RESULTS: Thirty-four patients died with metastatic disease and 29 patients survived at least 5 years without metastasis. A greater proportion of nucleoli larger than 3 mm2 in greatest dimension were from patients who died of their disease. The means of the nucleolar features were less significant outcome discriminators than the standard deviations. Means and standard deviations based on subgroups of nucleoli larger than 3 or 3.5 mm2 in greatest dimension were better discriminators. The MLVs were as effective discriminators as the corresponding standard deviations of the larger nucleoli and were better discriminators than MLN. CONCLUSIONS: AICA of AgNOR stained sections of uveal melanoma provides an excellent method for predicting the outcome of patients with uveal melanoma.
BACKGROUND: Automated image capture and analysis (AICA) has not previously been used to measure the size of nucleoli in uveal melanoma. In this study, the measurements were tested for a possible association with patient outcome. METHODS: Sections from 63 uveal melanomas were stained using the silver stain for nucleolar organizing regions (AgNOR). AICA was used to measure the following five nucleolar features in ten microscopic fields of the tumor: area, circularity, maximum diameter, width, and length of the perimeter. For each tumor, the mean and standard deviation of each of the features were calculated based on all the nucleoli and on subsets of nucleoli with larger areas. For the five nucleolar features the mean of the largest value (MLV) in each of the ten fields was calculated. For comparison, a related visually measured nucleolar feature (MLN) was obtained from hematoxylin and eosin stained sections using a filar micrometer. RESULTS: Thirty-four patients died with metastatic disease and 29 patients survived at least 5 years without metastasis. A greater proportion of nucleoli larger than 3 mm2 in greatest dimension were from patients who died of their disease. The means of the nucleolar features were less significant outcome discriminators than the standard deviations. Means and standard deviations based on subgroups of nucleoli larger than 3 or 3.5 mm2 in greatest dimension were better discriminators. The MLVs were as effective discriminators as the corresponding standard deviations of the larger nucleoli and were better discriminators than MLN. CONCLUSIONS: AICA of AgNOR stained sections of uveal melanoma provides an excellent method for predicting the outcome of patients with uveal melanoma.
Authors: K Nakamoto; A Ito; K Watabe; Y Koma; H Asada; K Yoshikawa; Y Shinomura; Y Matsuzawa; H Nojima; Y Kitamura Journal: Am J Pathol Date: 2001-10 Impact factor: 4.307