Performance of a HER2 testing algorithm tailored for urothelial bladder cancer: A Bi-centre study
Aims: This study aimed to develop an AI algorithm for automated HER2 scoring in urothelial bladder cancer (UBCa) and assess the interobserver agreement using both manual and AI-assisted methods based on breast cancer criteria. Methods and Results: We utilized 330 slides from two institutions for ini...
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Elsevier
2024-12-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037024003271 |
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| author | Aoling Huang Yizhi Zhao Feng Guan Hongfeng Zhang Bin Luo Ting Xie Shuaijun Chen Xinyue Chen Shuying Ai Xianli Ju Honglin Yan Lin Yang Jingping Yuan |
| author_facet | Aoling Huang Yizhi Zhao Feng Guan Hongfeng Zhang Bin Luo Ting Xie Shuaijun Chen Xinyue Chen Shuying Ai Xianli Ju Honglin Yan Lin Yang Jingping Yuan |
| author_sort | Aoling Huang |
| collection | DOAJ |
| description | Aims: This study aimed to develop an AI algorithm for automated HER2 scoring in urothelial bladder cancer (UBCa) and assess the interobserver agreement using both manual and AI-assisted methods based on breast cancer criteria. Methods and Results: We utilized 330 slides from two institutions for initial AI development and selected 200 slides for the ring study, involving six pathologists (3 senior, 3 junior). Our AI algorithm achieved high accuracy in two independent tests, with accuracies of 0.94 and 0.92. In the ring study, the AI-assisted method improved both accuracy (0.66 vs 0.94) and consistency (kappa=0.48; 95 % CI, 0.443–0.526 vs kappa=0.87; 95 % CI, 0.852–0.885) compared to manual scoring, especially in HER2-low cases (F1-scores: 0.63 vs 0.92). Additionally, in 62.3 % of heterogeneous HER2-positive cases, the interpretation accuracy significantly improved (0.49 vs 0.93). Pathologists, particularly junior ones, experienced enhanced accuracy and consistency with AI assistance. Conclusions: This is the first study to provide a quantification algorithm for HER2 scoring in UBCa to assist pathologists in diagnosis. The ring study demonstrated that HER2 scoring based on breast cancer criteria can be effectively applied to UBCa. Furthermore, AI assistance significantly improves the accuracy and consistency of interpretations among pathologists with varying levels of experience, even in heterogeneous cases. |
| format | Article |
| id | doaj-art-af9ec4b1370342b5a6af974f714fbbb7 |
| institution | Kabale University |
| issn | 2001-0370 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-af9ec4b1370342b5a6af974f714fbbb72024-12-19T10:53:29ZengElsevierComputational and Structural Biotechnology Journal2001-03702024-12-01264050Performance of a HER2 testing algorithm tailored for urothelial bladder cancer: A Bi-centre studyAoling Huang0Yizhi Zhao1Feng Guan2Hongfeng Zhang3Bin Luo4Ting Xie5Shuaijun Chen6Xinyue Chen7Shuying Ai8Xianli Ju9Honglin Yan10Lin Yang11Jingping Yuan12Department of Pathology, Renmin Hospital of Wuhan University, 238 Jiefang-Road, Wuchang District, Wuhan 430060, PR ChinaSchool of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou 310030, PR ChinaDepartment of Pathology, Renmin Hospital of Wuhan University, 238 Jiefang-Road, Wuchang District, Wuhan 430060, PR ChinaDepartment of Pathology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, PR ChinaDepartment of Pathology, Renmin Hospital of Wuhan University, 238 Jiefang-Road, Wuchang District, Wuhan 430060, PR ChinaDepartment of Pathology, Renmin Hospital of Wuhan University, 238 Jiefang-Road, Wuchang District, Wuhan 430060, PR ChinaDepartment of Pathology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, PR ChinaDepartment of Pathology, Renmin Hospital of Wuhan University, 238 Jiefang-Road, Wuchang District, Wuhan 430060, PR ChinaDepartment of Pathology, Renmin Hospital of Wuhan University, 238 Jiefang-Road, Wuchang District, Wuhan 430060, PR ChinaDepartment of Pathology, Renmin Hospital of Wuhan University, 238 Jiefang-Road, Wuchang District, Wuhan 430060, PR ChinaDepartment of Pathology, Renmin Hospital of Wuhan University, 238 Jiefang-Road, Wuchang District, Wuhan 430060, PR ChinaSchool of Engineering, Westlake University, 600 Dunyu Road, Xihu District, Hangzhou 310030, PR China; Corresponding authors.Department of Pathology, Renmin Hospital of Wuhan University, 238 Jiefang-Road, Wuchang District, Wuhan 430060, PR China; Corresponding authors.Aims: This study aimed to develop an AI algorithm for automated HER2 scoring in urothelial bladder cancer (UBCa) and assess the interobserver agreement using both manual and AI-assisted methods based on breast cancer criteria. Methods and Results: We utilized 330 slides from two institutions for initial AI development and selected 200 slides for the ring study, involving six pathologists (3 senior, 3 junior). Our AI algorithm achieved high accuracy in two independent tests, with accuracies of 0.94 and 0.92. In the ring study, the AI-assisted method improved both accuracy (0.66 vs 0.94) and consistency (kappa=0.48; 95 % CI, 0.443–0.526 vs kappa=0.87; 95 % CI, 0.852–0.885) compared to manual scoring, especially in HER2-low cases (F1-scores: 0.63 vs 0.92). Additionally, in 62.3 % of heterogeneous HER2-positive cases, the interpretation accuracy significantly improved (0.49 vs 0.93). Pathologists, particularly junior ones, experienced enhanced accuracy and consistency with AI assistance. Conclusions: This is the first study to provide a quantification algorithm for HER2 scoring in UBCa to assist pathologists in diagnosis. The ring study demonstrated that HER2 scoring based on breast cancer criteria can be effectively applied to UBCa. Furthermore, AI assistance significantly improves the accuracy and consistency of interpretations among pathologists with varying levels of experience, even in heterogeneous cases.http://www.sciencedirect.com/science/article/pii/S2001037024003271Artificial intelligenceBladder cancerHER2-lowHeterogeneity |
| spellingShingle | Aoling Huang Yizhi Zhao Feng Guan Hongfeng Zhang Bin Luo Ting Xie Shuaijun Chen Xinyue Chen Shuying Ai Xianli Ju Honglin Yan Lin Yang Jingping Yuan Performance of a HER2 testing algorithm tailored for urothelial bladder cancer: A Bi-centre study Computational and Structural Biotechnology Journal Artificial intelligence Bladder cancer HER2-low Heterogeneity |
| title | Performance of a HER2 testing algorithm tailored for urothelial bladder cancer: A Bi-centre study |
| title_full | Performance of a HER2 testing algorithm tailored for urothelial bladder cancer: A Bi-centre study |
| title_fullStr | Performance of a HER2 testing algorithm tailored for urothelial bladder cancer: A Bi-centre study |
| title_full_unstemmed | Performance of a HER2 testing algorithm tailored for urothelial bladder cancer: A Bi-centre study |
| title_short | Performance of a HER2 testing algorithm tailored for urothelial bladder cancer: A Bi-centre study |
| title_sort | performance of a her2 testing algorithm tailored for urothelial bladder cancer a bi centre study |
| topic | Artificial intelligence Bladder cancer HER2-low Heterogeneity |
| url | http://www.sciencedirect.com/science/article/pii/S2001037024003271 |
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