Artificial Intelligence for Predicting HER2 Status of Gastric Cancer Based on Whole‐Slide Histopathology Images: A Retrospective Multicenter Study
Abstract Human epidermal growth factor receptor 2 (HER2) positive gastric cancer (GC) shows a robust response to the combined therapy based HER2‐targeted therapy. The application of these therapies is highly dependent on the evaluation of tumor HER2 status. However, there are many risks and challeng...
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| Format: | Article |
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Wiley
2025-03-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202408451 |
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| author | Yuhan Liao Xinhua Chen Shupeng Hu Bing Chen Xinghua Zhuo Hao Xu Xiaojin Wu Xiaofeng Zeng Huimin Zeng Donghui Zhang Yunfei Zhi Liang Zhao |
| author_facet | Yuhan Liao Xinhua Chen Shupeng Hu Bing Chen Xinghua Zhuo Hao Xu Xiaojin Wu Xiaofeng Zeng Huimin Zeng Donghui Zhang Yunfei Zhi Liang Zhao |
| author_sort | Yuhan Liao |
| collection | DOAJ |
| description | Abstract Human epidermal growth factor receptor 2 (HER2) positive gastric cancer (GC) shows a robust response to the combined therapy based HER2‐targeted therapy. The application of these therapies is highly dependent on the evaluation of tumor HER2 status. However, there are many risks and challenges in HER2 assessment in GC. Therefore, an economically viable and readily available instrument is requisite for distinguishing HER2 status among patients diagnosed with GC. The study has innovatively developed a deep learning model, HER2Net, which can predict the HER2 status by quantitatively calculating the proportion of HER2 high‐expression regions. The HER2Net is trained on an internal training set derived from 531 hematoxylin & eosin (H&E) whole slide images (WSI) of 520 patients. Subsequently, the performance of HER2Net is validated on an internal test set from 115 H&E WSI of 111 patients and an external multi‐center test set from 102 H&E WSI of 101 patients. The HER2Net achieves an accuracy of 0.9043 on the internal test set, and an accuracy of 0.8922 on an external test set from multiple institutes. This discovery indicates that the HER2Net can potentially offer a novel methodology for the identification of HER2‐positive GC. |
| format | Article |
| id | doaj-art-29a0c1c9f11c4790950a91c7746b60e3 |
| institution | OA Journals |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
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| series | Advanced Science |
| spelling | doaj-art-29a0c1c9f11c4790950a91c7746b60e32025-08-20T02:35:40ZengWileyAdvanced Science2198-38442025-03-011210n/an/a10.1002/advs.202408451Artificial Intelligence for Predicting HER2 Status of Gastric Cancer Based on Whole‐Slide Histopathology Images: A Retrospective Multicenter StudyYuhan Liao0Xinhua Chen1Shupeng Hu2Bing Chen3Xinghua Zhuo4Hao Xu5Xiaojin Wu6Xiaofeng Zeng7Huimin Zeng8Donghui Zhang9Yunfei Zhi10Liang Zhao11Department of Pathology Nanfang Hospital Southern Medical University Guangzhou 510515 ChinaDepartment of General Surgery Nanfang Hospital Southern Medical University Guangzhou 510515 ChinaSchool of Computer Science University of Manchester Manchester M13 9PL UKDepartment of Pathology Nanfang Hospital Southern Medical University Guangzhou 510515 ChinaDepartment of Pathology Nanfang Hospital Southern Medical University Guangzhou 510515 ChinaDepartment of Pathology Nanfang Hospital Southern Medical University Guangzhou 510515 ChinaDepartment of Pathology Shunde Hospital of Southern Medical University (The First People's Hospital of Shunde) Foshan Guangdong 528399 ChinaDepartment of Pathology Shunde Hospital of Southern Medical University (The First People's Hospital of Shunde) Foshan Guangdong 528399 ChinaDepartment of Pathology Shunde Hospital of Southern Medical University (The First People's Hospital of Shunde) Foshan Guangdong 528399 ChinaDepartment of Pathology Affiliated Cancer Hospital and Institute of Guangzhou Medical University Guangzhou 510095 ChinaDepartment of Gastroenterology Chinese Academy of Medical Sciences Peking Union Medical College Hospital Beijing 100730 ChinaDepartment of Pathology Nanfang Hospital Southern Medical University Guangzhou 510515 ChinaAbstract Human epidermal growth factor receptor 2 (HER2) positive gastric cancer (GC) shows a robust response to the combined therapy based HER2‐targeted therapy. The application of these therapies is highly dependent on the evaluation of tumor HER2 status. However, there are many risks and challenges in HER2 assessment in GC. Therefore, an economically viable and readily available instrument is requisite for distinguishing HER2 status among patients diagnosed with GC. The study has innovatively developed a deep learning model, HER2Net, which can predict the HER2 status by quantitatively calculating the proportion of HER2 high‐expression regions. The HER2Net is trained on an internal training set derived from 531 hematoxylin & eosin (H&E) whole slide images (WSI) of 520 patients. Subsequently, the performance of HER2Net is validated on an internal test set from 115 H&E WSI of 111 patients and an external multi‐center test set from 102 H&E WSI of 101 patients. The HER2Net achieves an accuracy of 0.9043 on the internal test set, and an accuracy of 0.8922 on an external test set from multiple institutes. This discovery indicates that the HER2Net can potentially offer a novel methodology for the identification of HER2‐positive GC.https://doi.org/10.1002/advs.202408451deep learning modelgastric cancerhuman epidermal growth factor receptor 2whole slide images |
| spellingShingle | Yuhan Liao Xinhua Chen Shupeng Hu Bing Chen Xinghua Zhuo Hao Xu Xiaojin Wu Xiaofeng Zeng Huimin Zeng Donghui Zhang Yunfei Zhi Liang Zhao Artificial Intelligence for Predicting HER2 Status of Gastric Cancer Based on Whole‐Slide Histopathology Images: A Retrospective Multicenter Study Advanced Science deep learning model gastric cancer human epidermal growth factor receptor 2 whole slide images |
| title | Artificial Intelligence for Predicting HER2 Status of Gastric Cancer Based on Whole‐Slide Histopathology Images: A Retrospective Multicenter Study |
| title_full | Artificial Intelligence for Predicting HER2 Status of Gastric Cancer Based on Whole‐Slide Histopathology Images: A Retrospective Multicenter Study |
| title_fullStr | Artificial Intelligence for Predicting HER2 Status of Gastric Cancer Based on Whole‐Slide Histopathology Images: A Retrospective Multicenter Study |
| title_full_unstemmed | Artificial Intelligence for Predicting HER2 Status of Gastric Cancer Based on Whole‐Slide Histopathology Images: A Retrospective Multicenter Study |
| title_short | Artificial Intelligence for Predicting HER2 Status of Gastric Cancer Based on Whole‐Slide Histopathology Images: A Retrospective Multicenter Study |
| title_sort | artificial intelligence for predicting her2 status of gastric cancer based on whole slide histopathology images a retrospective multicenter study |
| topic | deep learning model gastric cancer human epidermal growth factor receptor 2 whole slide images |
| url | https://doi.org/10.1002/advs.202408451 |
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