Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review
With the advance of digital pathology, image analysis has begun to show its advantages in information analysis of hematoxylin and eosin histopathology images. Generally, histological features in hematoxylin and eosin images are measured to evaluate tumor grade and prognosis for breast cancer. This r...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2017-03-01
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| Series: | Tumor Biology |
| Online Access: | https://doi.org/10.1177/1010428317694550 |
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| _version_ | 1850054804092485632 |
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| author | Jia-Mei Chen Yan Li Jun Xu Lei Gong Lin-Wei Wang Wen-Lou Liu Juan Liu |
| author_facet | Jia-Mei Chen Yan Li Jun Xu Lei Gong Lin-Wei Wang Wen-Lou Liu Juan Liu |
| author_sort | Jia-Mei Chen |
| collection | DOAJ |
| description | With the advance of digital pathology, image analysis has begun to show its advantages in information analysis of hematoxylin and eosin histopathology images. Generally, histological features in hematoxylin and eosin images are measured to evaluate tumor grade and prognosis for breast cancer. This review summarized recent works in image analysis of hematoxylin and eosin histopathology images for breast cancer prognosis. First, prognostic factors for breast cancer based on hematoxylin and eosin histopathology images were summarized. Then, usual procedures of image analysis for breast cancer prognosis were systematically reviewed, including image acquisition, image preprocessing, image detection and segmentation, and feature extraction. Finally, the prognostic value of image features and image feature–based prognostic models was evaluated. Moreover, we discussed the issues of current analysis, and some directions for future research. |
| format | Article |
| id | doaj-art-757b03fe8a99472783bfd27c8bfd524c |
| institution | DOAJ |
| issn | 1423-0380 |
| language | English |
| publishDate | 2017-03-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Tumor Biology |
| spelling | doaj-art-757b03fe8a99472783bfd27c8bfd524c2025-08-20T02:52:08ZengSAGE PublishingTumor Biology1423-03802017-03-013910.1177/1010428317694550Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A reviewJia-Mei Chen0Yan Li1Jun Xu2Lei Gong3Lin-Wei Wang4Wen-Lou Liu5Juan Liu6Department of Oncology, Zhongnan Hospital of Wuhan University, Hubei Key Laboratory of Tumor Biological Behaviors & Hubei Cancer Clinical Study Center, Wuhan, ChinaDepartment of Peritoneal Cancer Surgery, Beijing Shijitan Hospital of Capital Medical University, Beijing, ChinaJiangsu Key Laboratory of Big Data Analysis Technique, Nanjing University of Information Science and Technology, Nanjing, ChinaJiangsu Key Laboratory of Big Data Analysis Technique, Nanjing University of Information Science and Technology, Nanjing, ChinaDepartment of Oncology, Zhongnan Hospital of Wuhan University, Hubei Key Laboratory of Tumor Biological Behaviors & Hubei Cancer Clinical Study Center, Wuhan, ChinaDepartment of Oncology, Zhongnan Hospital of Wuhan University, Hubei Key Laboratory of Tumor Biological Behaviors & Hubei Cancer Clinical Study Center, Wuhan, ChinaState Key Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan, ChinaWith the advance of digital pathology, image analysis has begun to show its advantages in information analysis of hematoxylin and eosin histopathology images. Generally, histological features in hematoxylin and eosin images are measured to evaluate tumor grade and prognosis for breast cancer. This review summarized recent works in image analysis of hematoxylin and eosin histopathology images for breast cancer prognosis. First, prognostic factors for breast cancer based on hematoxylin and eosin histopathology images were summarized. Then, usual procedures of image analysis for breast cancer prognosis were systematically reviewed, including image acquisition, image preprocessing, image detection and segmentation, and feature extraction. Finally, the prognostic value of image features and image feature–based prognostic models was evaluated. Moreover, we discussed the issues of current analysis, and some directions for future research.https://doi.org/10.1177/1010428317694550 |
| spellingShingle | Jia-Mei Chen Yan Li Jun Xu Lei Gong Lin-Wei Wang Wen-Lou Liu Juan Liu Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review Tumor Biology |
| title | Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review |
| title_full | Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review |
| title_fullStr | Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review |
| title_full_unstemmed | Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review |
| title_short | Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review |
| title_sort | computer aided prognosis on breast cancer with hematoxylin and eosin histopathology images a review |
| url | https://doi.org/10.1177/1010428317694550 |
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