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KMID : 0357920030370020115
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Korean Journal of Pathology
2003 Volume.37 No. 2 p.115 ~ p.120
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Quantitative Nuclear Characteristics of Lung Cancer Cells Using Image Analysis
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Kim Moon-Kyoung
Kim Chung-Yeul
Jeong Woon-Yong
Lee Ji-Hye
Lee Eung-Seok
Ha Seung-Yeon
Kim Young-Sik
Kim Han-Kyeom
Kim In-Sun
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Abstract
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Background: The usefulness of quantitative nuclear image analysis in the classification of lung carcinoma is widely investigated and published. In this study, we tried to measure the nuclear characteristics of primary lung carcinomas by image analysis and to find the possibility of differential diagnoses.
Methods: Seventeen cases of adenocarcinomas (not including bronchioloalveolar carcinoma), seven of bronchioloalveolar carcinomas, eight of large cell neuroendocrine carcinomas, five of small cell carcinamas, and 26 of squamous cell carcinomas were analysed. Three different images of each case were captured by digital camera, and we analyzed the nuclear area, perimeter, circularity, and density using the Optimas 6.5 Image Analyser software package. Statistical analyses were done using the statistical program STATISTICA kernel release 5.5.
Results: The mean nuclear area was 0.52+/-0.25 micro m2 in the adenocarcinomas, 0.50+/-1.82 micro m2 in the squamous cell carcinomas, 0.45+/-0.20 micro m2 in the large cell neuroendocrine carcinomas, 0.42+/-0.16 micro m2 in the bronchioloalveolar carcinomas, and 0.31+/-0.12 micro m2 in the small cell carcinamas. The nuclear area was significantly different between the small cell carcinomas and the non-small cell carcinomas (p < 0.01) and between the adenocarcinomas and the bronchioloalveolar carcinomas (p = 0.02). The mean nuclear perimeter was 3.36+/-0.92 micro m2 in the adenocarcinomas, 3.24+/-0.67 micro m2 the squamous cell carcinomas, 3.16+/-0.82 micro m2 in the large cell neuroendocrine carcinomas, 3.05+/- 0.80 micro m2 in the bronchioloalveolar carcinomas, and 2.54+/-0.62 micro m2 in the small cell carcinamas. The nuclear perimeter was significantly different between the small cell carcinomas and the non-small cell carcinomas (p < 0.04). The nuclear circularity showed no statistical difference. Nuclear density was the highest in the squamous cell carcinomas, and the lowest in the small cell carcinomas. The large cell neuroendocrine carcinomas showed the lowest standard deviation in nuclear density.
Conclusion: The analysis of nuclear characteristics using an image analyser can be used as an objective method in the classification of lung carcinomas.
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KEYWORD
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Lung carcinoma, Quantitative nuclear image features, Image analysis
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