{"title":"A Whole Slide Ki-67 Proliferation Analysis System for Breast Carcinoma","authors":"C. Ko, Chun-Hung Lin, Chih-Hung Chuang, Chuan-Yu Chang, Shih-Hao Chang, Ji-Han Jiang","doi":"10.1109/Ubi-Media.2019.00048","DOIUrl":null,"url":null,"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.","PeriodicalId":259542,"journal":{"name":"2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Twelfth International Conference on Ubi-Media Computing (Ubi-Media)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/Ubi-Media.2019.00048","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.