PubMed · 5636138
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L F Grapski, H D Sanders. 1968-01-16. An automated materials processing and distribution system.. https://pubmed.ncbi.nlm.nih.gov/5636138/
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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.
DNA "ploidy" histogram interpretation is one of the most important sources of variation in DNA image cytometry and is influenced by multiple technical factors such as scaling, selection of peaks, and variable classification criteria. A rule-based expert system was developed to automate and eliminate subjectivity from this interpretative process. Ninety-eight Feulgen stained histologic sections from patients with breast, colon, and lung cancer were measured with the CAS 200 image analysis system (Becton Dickinson, Santa Clara, CA); they included diploid (n = 42), aneuploid (n = 46), tetraploid (n = 7), and multiploid (n = 3) examples. The data was converted from listmode format into ASCII with the aid of CELLSHEET software (JVC Imaging, Elmhurst, IL). Individual microphotometric nuclear measurements were sorted to one of 64 bins based on DNA index. The 64 bins were then divided into 5 semi-arbitrarily defined ranges: hypodiploid, diploid, aneuploid, tetraploid, and hypertetraploid. The nuclear percentages in each range were calculated with EXCEL 4.0 (Microsoft, Redmond, WA). The histograms were divided into 2 equal sets: training and testing. The data from the training set were used to develop 16 IF-THEN rules to classify the histograms into diploid, aneuploid, or tetraploid. A macro was programmed in EXCEL to automate all these operations. The rule-based expert system classified correctly 45/50 histograms of the training set. Two tetraploid histograms were classified as aneuploid. Three multiploid histograms were classified as tetraploid. All histograms in the testing set were correctly classified by the expert system. The potential role of rule-based expert system technology for the objective classification of DNA "ploidy" histograms measured by image cytometry is discussed.