Tuczek H V, Fritz P, Schwarzmann P, Wu X, Mähner G
Department of Pathology, Marienhospital and Robert Bosch Hospital, Stuttgart, Germany.
Anal Quant Cytol Histol. 1996 Dec;18(6):481-93.
To investigate the relevance of image analysis for grading breast carcinomas.
The results of histologic grading were correlated with 18 features of image analysis, including SD. "Simple" characteristics, like area and perimeter, shape indices, optical density and textural features of nuclei from cancer cells, were analyzed. Hematoxylin-eosin-stained tissue sections of 67 cancer specimens were routinely used for the study.
We found statistically significant correlations between overall histologic grading and the sum of its subscores and features of image analysis, especially nuclear area, nuclear perimeter and the diameter of the circumscribing circle (diametercirc), including their SDs. The visually and therefore subjectively assessed subscore of the nuclear pleomorphism of histologic grading significantly correlated with the features of image analysis, like nuclear area, nuclear perimeter, diametercirc, integrated optical density and correlation (and their SDs). There were significant relationships between the absolute numbers of mitoses per 10 high-power fields and nuclear area, nuclear perimeter and diametercirc (and their SDs). We did not observe a significant correlation between the subscore of tubule formation of histologic grading and any of the features of the image analysis studied. Furthermore, the correlations between the features of image analysis and the subscores of the visual histologic grading system were analyzed with respect to each other. The subscore of nuclear pleomorphism of histologic grading correlated best with overall grading (r = .72), whereas no significant correlation could be found between the subscores of nuclear pleomorphism and mitotic activity.
Image analysis provides objectivity and reproducibility to the grading of breast carcinomas and thus could contribute to more individualized prognostication of the disease.
探讨图像分析在乳腺癌分级中的相关性。
组织学分级结果与图像分析的18项特征相关,包括标准差。分析了“简单”特征,如面积、周长、形状指数、癌细胞核的光密度和纹理特征。67例癌标本的苏木精-伊红染色组织切片常规用于本研究。
我们发现组织学总分级与其子评分之和与图像分析特征之间存在统计学显著相关性,尤其是核面积、核周长和外接圆直径(直径circ),包括它们的标准差。组织学分级中视觉上且因此主观评估的核多形性子评分与图像分析特征显著相关,如核面积、核周长、直径circ、积分光密度和相关性(及其标准差)。每10个高倍视野中的有丝分裂绝对数与核面积、核周长和直径circ(及其标准差)之间存在显著关系。我们未观察到组织学分级中管状结构形成的子评分与所研究的任何图像分析特征之间存在显著相关性。此外,还分析了图像分析特征与视觉组织学分级系统子评分之间的相互关系。组织学分级中核多形性子评分与总分级的相关性最佳(r = 0.72),而核多形性子评分与有丝分裂活性之间未发现显著相关性。
图像分析为乳腺癌分级提供了客观性和可重复性,因此有助于对该疾病进行更个体化的预后评估。