A Whole Slide Ki-67 Proliferation Analysis System for Breast Carcinoma

C. Ko, Chun-Hung Lin, Chih-Hung Chuang, Chuan-Yu Chang, Shih-Hao Chang, Ji-Han Jiang
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引用次数: 2

Abstract

The expression of Ki-67 with IHC stain has been utilized to assess the prognosis of breast cancer, and the degree of cellular differentiation and proliferation rate. Recently, some researchers utilize the index to predict metastasis of breast carcinoma. In traditional pathological screening, manual assessment of Ki-67 proliferative index may be limited by manual evaluation from different pathologists. Especially, inconsistent biopsy staining would affect the quantitation of Ki-67 proliferation so that developing an automatic system to assess Ki-67 proliferation index poses a big challenge. The goal of this paper is to propose an automatic analysis system to evaluate the degrees of Ki-67 proliferation on IHC stained cells of breast tissue using image processing and machine intelligence techniques. The proposed system not only can assist physicians diagnose, but also provides important information of treatment and prognosis. In order to validate the evaluation performance, we compared with visual assessments by a pathologist and the ImmnuoRatio (i.e., a web-based evaluation system in Ki-67 expression) developed by Vilppu J Tuominen et al.[1] via a number of Ki-67 stained samples for patients with breast carcinoma. Experimental results also demonstrate that the proposed system can automatically, accurately and reliably assess the Ki-67 proliferation index on the breast tissue images with a precision of around 87.37%. However, the accuracy evaluating with ImmunoRatio only can reach 75.82% with the same samples. Moreover, our proposed system also provides various interaction functions including browsing, navigation, and quantitative analyses for pathologists who evaluate the expression of the Ki-67 proliferation.
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全幻灯片Ki-67乳腺癌增殖分析系统
通过免疫组化染色检测Ki-67的表达可用于评价乳腺癌的预后、细胞分化程度和增殖率。近年来,一些研究者利用该指数预测乳腺癌的转移。在传统的病理筛查中,Ki-67增殖指数的人工评估可能会受到不同病理医师人工评估的限制。特别是活检染色不一致会影响Ki-67增殖的定量,因此开发一种自动评估Ki-67增殖指数的系统是一个很大的挑战。本文的目的是提出一种基于图像处理和机器智能技术的乳腺组织免疫组化染色细胞Ki-67增殖程度的自动分析系统。该系统不仅能辅助医生诊断,还能提供重要的治疗和预后信息。为了验证评估效果,我们将病理学家的视觉评估与Vilppu J Tuominen等人[1]开发的immunoratio(即Ki-67表达的基于网络的评估系统)进行了比较,通过对乳腺癌患者的一些Ki-67染色样本进行了分析。实验结果还表明,该系统能够自动、准确、可靠地评估乳腺组织图像上的Ki-67增殖指数,准确率约为87.37%。然而,在相同的样品下,用ImmunoRatio评价的准确率只能达到75.82%。此外,我们提出的系统还为病理学家评估Ki-67增殖表达提供了多种交互功能,包括浏览、导航和定量分析。
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