Quantitative immunohistochemistry analysis of breast Ki67 based on artificial intelligence
Breast cancer is a common malignant tumor of women. Ki67 is an important biomarker of cell proliferation. With the quantitative analysis, it is an important indicator of malignancy for breast cancer diagnosis. However, it is difficult to accurately and quantitatively evaluate the count of positive n...
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| Format: | Article |
| Language: | English |
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De Gruyter
2024-12-01
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| Series: | Open Life Sciences |
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| Online Access: | https://doi.org/10.1515/biol-2022-1013 |
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| author | Wang Wenhui Gong Yitang Chen Bingxian Guo Hualei Wang Qiang Li Jing Jin Cheng Gui Kun Chen Hao |
| author_facet | Wang Wenhui Gong Yitang Chen Bingxian Guo Hualei Wang Qiang Li Jing Jin Cheng Gui Kun Chen Hao |
| author_sort | Wang Wenhui |
| collection | DOAJ |
| description | Breast cancer is a common malignant tumor of women. Ki67 is an important biomarker of cell proliferation. With the quantitative analysis, it is an important indicator of malignancy for breast cancer diagnosis. However, it is difficult to accurately and quantitatively evaluate the count of positive nucleus during the diagnosis process of pathologists, and the process is time-consuming and labor-intensive. In this work, we employed a quantitative analysis method of Ki67 in breast cancer based on deep learning approach. For the diagnosis of breast cancer, according to breast cancer diagnosis guideline, we first identified the tumor region of Ki67 pathological image, neglecting the non-tumor region in the image. Then, we detect the nucleus in the tumor region to determine the nucleus location information. After that, we classify the detected nucleuses as positive and negative according to the expression level of Ki67. According to the results of quantitative analysis, the proportion of positive cells is counted. Combining the above process, we design a breast Ki67 quantitative analysis pipeline. The Ki67 quantitative analysis system was assessed on the validation set. The Dice coefficient of the tumor region segmentation model was 0.848, the Average Precision index of the nucleus detection model was 0.817, and the accuracy of the nucleus classification model was 96.66%. Besides, in clinical independent sample experiment, the results show that the proposed breast Ki67 quantitative analysis system achieve excellent correlation with the diagnosis efficiency of doctors improved more than ten times and the overall consistency of diagnosis is intra-group correlation coefficient: 0.964. The research indicates that our quantitative analysis method of Ki67 in breast cancer has high clinical application value. |
| format | Article |
| id | doaj-art-8d293a898f9b45c09ab6cc5dfa80ec87 |
| institution | Kabale University |
| issn | 2391-5412 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Life Sciences |
| spelling | doaj-art-8d293a898f9b45c09ab6cc5dfa80ec872025-02-02T15:44:51ZengDe GruyterOpen Life Sciences2391-54122024-12-01191450910.1515/biol-2022-1013Quantitative immunohistochemistry analysis of breast Ki67 based on artificial intelligenceWang Wenhui0Gong Yitang1Chen Bingxian2Guo Hualei3Wang Qiang4Li Jing5Jin Cheng6Gui Kun7Chen Hao8Department of Pathology, Hangzhou Women’s Hospital, Hangzhou, 310008, Zhejiang, ChinaDepartment of Pathology, Hangzhou Women’s Hospital, Hangzhou, 310008, Zhejiang, ChinaNingbo Konfoong Bioinformation Tech Co., Ltd, Ningbo, ChinaDepartment of Pathology, Hangzhou Women’s Hospital, Hangzhou, 310008, Zhejiang, ChinaNingbo Konfoong Bioinformation Tech Co., Ltd, Ningbo, ChinaDepartment of Pathology, Hangzhou Women’s Hospital, Hangzhou, 310008, Zhejiang, ChinaDepartment of Pathology, Hangzhou Women’s Hospital, Hangzhou, 310008, Zhejiang, ChinaNingbo Konfoong Bioinformation Tech Co., Ltd, Ningbo, ChinaDepartment of Pathology, Hangzhou Women’s Hospital, 369 Kunpeng Road, Shangcheng District, Hangzhou, 310008, Zhejiang, ChinaBreast cancer is a common malignant tumor of women. Ki67 is an important biomarker of cell proliferation. With the quantitative analysis, it is an important indicator of malignancy for breast cancer diagnosis. However, it is difficult to accurately and quantitatively evaluate the count of positive nucleus during the diagnosis process of pathologists, and the process is time-consuming and labor-intensive. In this work, we employed a quantitative analysis method of Ki67 in breast cancer based on deep learning approach. For the diagnosis of breast cancer, according to breast cancer diagnosis guideline, we first identified the tumor region of Ki67 pathological image, neglecting the non-tumor region in the image. Then, we detect the nucleus in the tumor region to determine the nucleus location information. After that, we classify the detected nucleuses as positive and negative according to the expression level of Ki67. According to the results of quantitative analysis, the proportion of positive cells is counted. Combining the above process, we design a breast Ki67 quantitative analysis pipeline. The Ki67 quantitative analysis system was assessed on the validation set. The Dice coefficient of the tumor region segmentation model was 0.848, the Average Precision index of the nucleus detection model was 0.817, and the accuracy of the nucleus classification model was 96.66%. Besides, in clinical independent sample experiment, the results show that the proposed breast Ki67 quantitative analysis system achieve excellent correlation with the diagnosis efficiency of doctors improved more than ten times and the overall consistency of diagnosis is intra-group correlation coefficient: 0.964. The research indicates that our quantitative analysis method of Ki67 in breast cancer has high clinical application value.https://doi.org/10.1515/biol-2022-1013artificial intelligencebreast canceralgorithmsdeep learningimage detectionki-67 |
| spellingShingle | Wang Wenhui Gong Yitang Chen Bingxian Guo Hualei Wang Qiang Li Jing Jin Cheng Gui Kun Chen Hao Quantitative immunohistochemistry analysis of breast Ki67 based on artificial intelligence Open Life Sciences artificial intelligence breast cancer algorithms deep learning image detection ki-67 |
| title | Quantitative immunohistochemistry analysis of breast Ki67 based on artificial intelligence |
| title_full | Quantitative immunohistochemistry analysis of breast Ki67 based on artificial intelligence |
| title_fullStr | Quantitative immunohistochemistry analysis of breast Ki67 based on artificial intelligence |
| title_full_unstemmed | Quantitative immunohistochemistry analysis of breast Ki67 based on artificial intelligence |
| title_short | Quantitative immunohistochemistry analysis of breast Ki67 based on artificial intelligence |
| title_sort | quantitative immunohistochemistry analysis of breast ki67 based on artificial intelligence |
| topic | artificial intelligence breast cancer algorithms deep learning image detection ki-67 |
| url | https://doi.org/10.1515/biol-2022-1013 |
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