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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Qu, Aiping Chen, Jiamei Wang, Linwei Yuan, Jingping Yang, Fang Xiang, Qingming Maskey, Ninu Yang, Guifang Liu, Juan Li, Yan |
| Copyright Year | 2014 |
| Description | Author affiliation: Department of pathology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China (Maskey, Ninu; Yang, Guifang) || Department of Oncology, Zhongnan Hospital of Wuhan University, Hubei Key Laboratory of Tumor Biological Behaviors & Hubei Cancer Clinical Study Center, Wuhan, 430071, China (Chen, Jiamei; Wang, Linwei; Yuan, Jingping; Yang, Fang; Xiang, Qingming; Li, Yan) || Key State Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan 430072, China (Qu, Aiping; Liu, Juan) |
| Abstract | Accurately predicting the risk of cancer recurrence and metastasis is critical to cancer individualized treatment. Currently, physicians commonly use histological grade, which is determined by pathologists via performing a semi-quantitative analysis of three histological and cytological features on Hematoxylin-Eosin (HE) stained histopathological images, to assess the prognosis of a breast cancer patient and the treatment option. In order to efficiently and objectively make full use of the underlying invaluable information in HE stained histopathological images, this work proposes a computational method to extract the potential morphological information as features to establish an classification model for the prognosis of cancer. Firstly, we propose a method based on the pixel-wise support vector machine (SVM) classifier for segmenting tumor nests-stroma and a method based on the marker-controlled watershed for segmenting cell nucleus, then we subclassify all image objects and extract a rich set of predefined quantitative morphological features. Secondly, a classification model based on these measurements is used to predict the binary patients' outcome of 8-year disease free survival (8-DFS). Finally, the predict model is tested on two independent cohorts of breast cancer patients. Experimental results demonstrate the efficiency and effectiveness of the proposed method, providing valuable and reasonable prognosis information for breast cancer. |
| Starting Page | 218 |
| Ending Page | 223 |
| File Size | 12487757 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479956692 |
| DOI | 10.1109/BIBM.2014.6999158 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-11-02 |
| Publisher Place | United Kingdom |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Image segmentation Image color analysis Predictive models Feature extraction Breast cancer classification Prognostics and health management prognosis histopathological image analysis Cancer |
| Content Type | Text |
| Resource Type | Article |
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