Mat-Sakim H A, Naguib R N, Lakshmi M S, Wadehra V, Lennard T W, Bhatavdekar J, Sherbet G V
Department of Electrical and Electronic Engineering, University of Newcastle, Newcastle upon Tyne, U.K.
Anticancer Res. 1998 Jul-Aug;18(4A):2723-6.
Image flow cytometry data of aspirated tumour cells from 102 patients with breast cancer were analysed and used as prognostic markers in an attempt to predict involvement of axillary lymph nodes and histological grade using logistic regression. Prediction was 70% for both nodal status and histological analyses. The outcome of this study is compared to an earlier study using the same cytological information to obtain prediction using a neural approach. Using artificial neural networks, prediction accuracy was 87% and 82% for nodal status and histological assessment, respectively. This study also attempts to identify the impact of individual prognostic factors. The statistical approach identified S-phase fraction and DNA-ploidy as the most important prediction markers for nodal status and histological assessment analyses. A comparison was made between these two quantitative techniques.
分析了102例乳腺癌患者穿刺肿瘤细胞的图像流式细胞术数据，并将其用作预后标志物，试图通过逻辑回归预测腋窝淋巴结受累情况和组织学分级。淋巴结状态和组织学分析的预测准确率均为70%。将本研究的结果与早期一项使用相同细胞学信息通过神经方法进行预测的研究进行了比较。使用人工神经网络，淋巴结状态和组织学评估的预测准确率分别为87%和82%。本研究还试图确定个体预后因素的影响。统计方法确定S期分数和DNA倍性是淋巴结状态和组织学评估分析中最重要的预测标志物。对这两种定量技术进行了比较。
