Karakitsos P, Pouliakis A, Koutroumbas K, Stergiou E B, Tzivras M, Archimandritis A, Liossi A I
Department of Cytology, St. Olga Hospital, Greece.
Anal Quant Cytol Histol. 2000 Feb;22(1):63-9.
To investigate the potential value of morphometry and neural networks for the discrimination of benign from malignant gastric lesions.
One thousand cells from 19 cases of cancer, 19 cases of gastritis and 56 cases of ulcer were selected as a training set, and an additional 4,000 cells from the same cases of cancer, gastritis and ulcer were used as a test set. Images of routinely processed gastric smears stained by the Papanicolaou technique were analyzed by a custom-made image analysis system.
Application of the neural network gave correct classification in 96% of benign cells and 89% of malignant cells.
The results indicate that the use of neural networks and image morphometry may offer useful information concerning the potential of malignancy in gastric cells.
探讨形态计量学和神经网络在鉴别胃良性病变与恶性病变方面的潜在价值。
选取19例癌症、19例胃炎和56例溃疡患者的1000个细胞作为训练集，另外从相同的癌症、胃炎和溃疡病例中选取4000个细胞作为测试集。采用巴氏染色法对常规处理的胃涂片进行图像分析，由定制的图像分析系统完成。
神经网络应用于良性细胞时分类正确率为96%，应用于恶性细胞时分类正确率为89%。
结果表明，神经网络和图像形态计量学的应用可能为胃细胞恶性潜能提供有用信息。
