Albert R, Müller J G, Kristen P, Schindewolf T, Kneitz S, Harms H
Institute of Virology and Immunology, University of Würzburg, Germany.
Cytometry. 1996 Jun 1;24(2):140-50. doi: 10.1002/(SICI)1097-0320(19960601)24:2<140::AID-CYTO6>3.0.CO;2-N.
To optimize treatment of the individual patient with node-negative breast cancer, objective, reproducible, and standardized prognostic criteria are required. A number of factors have been studied in recent years, but until now it has been possible to obtain information about the risk of recurrence only for some patients belonging to subgroups with special characteristics. We report the establishment of an image analysis method for nuclear grading as an attempt to solve this problem. In a retrospective analysis, we used routine hematoxylin and eosinstained paraffin sections from 54 node-negative patients with surgery between 1980 and 1985. Cell scenes of primary tumors were scanned in a light microscope in successive focus positions to obtain three-dimensional information. After automatic image segmentation, nuclear features were calculated as input for a first binary classification tree to differentiate between tumor and nontumor cells. Tumor nuclei from patients with or without relapse were defined as high-risk or low-risk nuclei, respectively, and were separated with a second tree. Feature values of the measured tumor nuclei from each patient were examined with this second tree to analyze whether the majority of nuclei for each patient were classified as high-risk or low-risk nuclei. Correct classification rates in the two binary cell classification trees were 88.0% and 83.8%, respectively. In the learning sample of our study, all patients with a relapse had the majority of nuclei in the high-risk group, most with more than 80%. Therefore, it seems to be possible to develop an image analytical risk profile system for nuclear grading to provide information on individual prognosis.
为优化对淋巴结阴性乳腺癌个体患者的治疗，需要客观、可重复且标准化的预后标准。近年来已对多种因素进行了研究，但直到现在，仅能为某些具有特殊特征亚组的患者获取复发风险信息。我们报告建立一种用于核分级的图像分析方法，作为解决此问题的一种尝试。在一项回顾性分析中，我们使用了1980年至1985年间54例接受手术的淋巴结阴性患者的常规苏木精和伊红染色石蜡切片。在光学显微镜下，在连续的聚焦位置扫描原发性肿瘤的细胞场景以获取三维信息。自动图像分割后，计算核特征作为第一个二元分类树的输入，以区分肿瘤细胞和非肿瘤细胞。有复发或无复发患者的肿瘤细胞核分别定义为高风险或低风险细胞核，并通过第二个树进行分离。用第二个树检查每个患者测量的肿瘤细胞核的特征值，以分析每个患者的大多数细胞核是否被分类为高风险或低风险细胞核。两个二元细胞分类树中的正确分类率分别为88.0%和83.8%。在我们研究的学习样本中，所有复发患者的大多数细胞核都在高风险组，大多数超过80%。因此，似乎有可能开发一种用于核分级的图像分析风险概况系统，以提供个体预后信息。
