Quantitative nucleic features are effective for discrimination of intraductal proliferative lesions of the breast.
Quantitative nucleic features are effective for discrimination of intraductal proliferative lesions of the breast.
复制标题
DOI：
10.4103/2153-3539.175380
复制
发表时间：
2016
影响因子：
--
通讯作者：
Kuroda M
中科院分区：
文献类型：
--
作者：
Yamada M;Saito A;Yamamoto Y;Cosatto E;Kurata A;Nagao T;Tateishi A;Kuroda M
乳腺导管内增生性病变（IDPL）被认为是随后发生浸润性癌的危险因素。虽然IDPL诊断的机会增加了，但这些病变很难正确诊断，特别是不典型导管增生（ADH）和低度导管原位癌（LG-DCIS）。为了确定这些病变之间的差异，已经进行了许多分子病理学方法。然而，我们仍然没有一个分子标记物和客观的组织学指标的乳腺IDPL。我们从175名女性IDPL患者中生成了完整的数字病理学档案，包括普通型导管增生（UDH）、ADH、LG-DCIS、中度（IM）-DCIS和高度（HG）-DCIS。在提取了总共2，035，807个核片段后，我们使用逐步线性判别分析（LDA）和支持向量机评估了核特征。在病理学家的诊断和来自用于IDPL区分的核特征的两组LDA预测之间实现了高诊断准确性（81.8-99.3%）。核的大小和形状相关的或核内纹理相关的特征的分析表明，后一组在区分正常导管，UDH，ADH和LG-DCIS时更重要。然而，当区分LG-DCIS和HG-DCIS时，这两组同样重要。各组之间的马氏距离显示，LG-DCIS与IM-DCIS之间以及ADH与正常之间的距离值最小。而ADH与LG-DCIS之间的距离值大于该距离。在这项研究中，我们提出了一个实用和有用的数字病理学方法，结合核形态和纹理特征的IDPL预测。我们期望这种新的算法用于乳腺癌的自动诊断辅助系统。
Intraductal proliferative lesions (IDPLs) of the breast are recognized as a risk factor for subsequent invasive carcinoma development. Although opportunities for IDPL diagnosis have increased, these lesions are difficult to diagnose correctly, especially atypical ductal hyperplasia (ADH) and low-grade ductal carcinoma in situ (LG-DCIS). In order to define the difference between these lesions, many molecular pathological approaches have been performed. However, still we do not have a molecular marker and objective histological index about IDPLs of the breast. We generated full digital pathology archives from 175 female IDPL patients, including usual ductal hyperplasia (UDH), ADH, LG-DCIS, intermediate-grade (IM)-DCIS, and high-grade (HG)-DCIS. After total 2,035,807 nucleic segmentations were extracted, we evaluated nuclear features using step-wise linear discriminant analysis (LDA) and a support vector machine. High diagnostic accuracy (81.8–99.3%) was achieved between pathologists’ diagnoses and two-group LDA predictions from nucleic features for IDPL discrimination. Grouping of nuclear features as size and shape-related or intranuclear texture-related revealed that the latter group was more important when distinguishing between normal duct, UDH, ADH, and LG-DCIS. However, these two groups were equally important when discriminating between LG-DCIS and HG-DCIS. The Mahalanobis distances between each group showed that the smallest distance values occurred between LG-DCIS and IM-DCIS and between ADH and Normal. On the other hand, the distance value between ADH and LG-DCIS was larger than this distance. In this study, we have presented a practical and useful digital pathological method that incorporates nuclear morphological and textural features for IDPL prediction. We expect that this novel algorithm is used for the automated diagnosis assisting system for breast cancer.