Objective: Because of the increased precision of ultrasound breast cancer screening, early cancer cases with no clear mass or extraction of microcysts on imaging have recently increased, and improvement of the accuracy of breast fine-needle aspiration biopsy (FNAB) cytology is needed. The objective of this study was to investigate the usefulness of cluster gray image-fractal analysis evaluating the darkness of clusters, cluster unevenness, and complexity of hyperchromicity (cluster density) of deep-stained cell clusters, known as hyperchromatic crowded cell groups (HCG), on FNAB as a cytology assistance system for breast FNAB. Study Design: One hundred clusters collected from 10 patients with fibroadenoma (FA), 90 clusters from 9 patients with ductal carcinoma in situ (DCIS), and 122 clusters from 11 patients with invasive breast carcinoma of no special type (IBC-NST) were used. (1) Cluster size classification: clusters were classified into small, middle, and large clusters (small cluster: smaller than 40 × 10² μm²; large cluster: 100 × 10² μm² or larger; middle cluster: intermediate), and their frequency was calculated. (2) Cluster gray image-fractal analysis: (a) the darkness of clusters (luminance), (b) cluster unevenness (complexity), and (c) complexity of cluster density (roundness-corrected fractal value) were assessed. For statistical analysis, the multiple comparison Steel-Dwass test was used, with a significance level of p #x3c; 0.05. Results: (1) Cluster size classification: in FA, small, middle, and large clusters appeared at a similar frequency, and the frequency (30%) of large clusters was significantly higher than that in other diseases. In IBC-NST, many small clusters (61%) appeared and their frequency was significantly higher than that in other diseases, whereas the frequency of large clusters was significantly lower. (2) Cluster gray image-fractal analysis: in IBC-NST, the luminance of small clusters was low (dark), the cluster unevenness was high, and the complexity of cluster density was high, whereas the luminance of large clusters was high (bright), the cluster unevenness was high, and complexity of cluster density was high compared with those in FA. Conclusion: Cluster gray image-fractal analysis evaluating the darkness of clusters, cluster unevenness, and complexity of cluster density in breast FNAB HCG is a useful cytology assistance system for breast FNA.
Acta Cytologica

中文翻译：

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分形分析方法对乳腺癌FNAB细胞簇染色的复杂性。

目的：由于超声乳癌筛查的准确性提高，因此近期影像不清的肿块或微囊肿摘除的早期癌症病例有所增加，因此需要提高乳腺细针穿刺活检（FNAB）细胞学准确性。这项研究的目的是研究聚类灰度图像分形分析在评估聚类暗度，聚类不均匀性以及深染色细胞聚类（称为高色拥挤细胞群（HCG））的增色性（聚类密度）的复杂性方面的有效性。 ，作为乳房FNAB的细胞学辅助系统。学习规划：使用从10例纤维腺癌（FA）患者中收集的一百个簇，从9例原位导管癌（DCIS）患者中收集的90个簇以及从11例非特殊类型浸润性乳腺癌（IBC-NST）中收集的122个簇。（1）簇的大小分类：簇分为小，中，大型集群（小簇：小于40×10 ²微米² ;大簇：100×10 ²微米²或更大；中间簇：中间），并计算其频率。（2）聚类灰度图像分形分析：（a）聚类的暗度（亮度），（b）聚类不均匀性（复杂度），以及（c）聚类密度的复杂度（经圆度校正的分形值）。为了进行统计分析，使用了多重比较Steel-Dwass检验，显着性水平为p＃x3c；0.05。结果：（1）集群大小分类：在FA中，小集群，中集群和大集群以相似的频率出现，大集群的频率（30％）明显高于其他疾病。在IBC-NST中，出现了许多小簇（61％），它们的发生率明显高于其他疾病，而大簇的发生率则明显更低。（2）聚类灰度图像的分形分析：在IBC-NST中，小聚类的亮度低（暗），聚类不均匀度高，聚类密度的复杂度高，而大聚类的亮度高（与FA相比，簇状不均匀度高，簇密度的复杂度高。结论：评估乳房FNAB HCG中的簇的暗度，簇的不均匀性和簇密度的复杂性的簇灰度图像分形分析是一种有用的乳腺FNA细胞学辅助系统。
细胞学学报