Automated interpretation of PD-L1 CPS based on multi-AI models integration strategy in gastric cancer
IntroductionProgrammed cell death ligand-1 (PD-L1) combined positive score (CPS) evaluation plays a pivotal role in predicting immunotherapy efficacy for gastric cancer. However, manual CPS assessment suffers from significant inter-observer variability among pathologists, leading to clinical inconsi...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1614099/full |
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| author | Ting Han Meng Zhuo Ziyu Song Peilin Chen Shiting Chen Wei Zhang Yuanyuan Zhou Hong Li Dadong Zhang Xiaolin Lin Zebing Liu Xiuying Xiao |
| author_facet | Ting Han Meng Zhuo Ziyu Song Peilin Chen Shiting Chen Wei Zhang Yuanyuan Zhou Hong Li Dadong Zhang Xiaolin Lin Zebing Liu Xiuying Xiao |
| author_sort | Ting Han |
| collection | DOAJ |
| description | IntroductionProgrammed cell death ligand-1 (PD-L1) combined positive score (CPS) evaluation plays a pivotal role in predicting immunotherapy efficacy for gastric cancer. However, manual CPS assessment suffers from significant inter-observer variability among pathologists, leading to clinical inconsistencies. To address this limitation, we developed a deep learning-based artificial intelligence (AI) system that automates PD-L1 CPS quantification for patients with gastric cancer (GC) using whole slide images (WSIs).MethodsWe developed a deep learning-based artificial intelligence (AI) system that automates PD-L1 CPS quantification for patients with gastric cancer (GC) using whole slide images (WSIs). Our pipeline firstly employs a dual-network architecture for tumor region detection: MobileNet for patch-level classification and U-Net for pixel-level segmentation. Followed by a YOLO-based cell detection model to compute PD-L1 expression on different cells for CPS calculation. A total of 308 GC WSIs were included, including 210 in the internal cohort and 98 in the external cohort. Within the internal cohort, 100 WSIs were utilized for the model development, while the remaining 110 WSIs served as an internal testing set for comparative analysis between AI-derived CPS values and pathologist-derived reference standards.ResultsThe AI-derived CPS demonstrated strong concordance with expert pathologists’ consensus in internal cohort (Cohen’s kappa = 0.782). Furthermore, the AI-based CPS prediction pipeline was evaluated for its performance in the external cohort, and showed robust performance (Cohen’s kappa = 0.737).DiscussionOur system provides a standardized decision-support tool for immunotherapy stratification in GC management, demonstrating potential to improve CPS assessment reproducibility. |
| format | Article |
| id | doaj-art-9d63ed820c7c409aa43b94ffa9b33bc7 |
| institution | Kabale University |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-9d63ed820c7c409aa43b94ffa9b33bc72025-08-20T03:39:32ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-08-011610.3389/fimmu.2025.16140991614099Automated interpretation of PD-L1 CPS based on multi-AI models integration strategy in gastric cancerTing Han0Meng Zhuo1Ziyu Song2Peilin Chen3Shiting Chen4Wei Zhang5Yuanyuan Zhou6Hong Li7Dadong Zhang8Xiaolin Lin9Zebing Liu10Xiuying Xiao11Department of Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Pathology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaProduct Development Department, SODA Data Technology Inc., Shanghai, ChinaProduct Development Department, SODA Data Technology Inc., Shanghai, ChinaDepartment of Clinical and Translational Research, 3D Medicines Inc., Shanghai, ChinaSchool of Pharmacy, East China University of Science and Technology, Shanghai, ChinaState Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, ChinaDepartment of Clinical and Translational Research, 3D Medicines Inc., Shanghai, ChinaDepartment of Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Pathology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaIntroductionProgrammed cell death ligand-1 (PD-L1) combined positive score (CPS) evaluation plays a pivotal role in predicting immunotherapy efficacy for gastric cancer. However, manual CPS assessment suffers from significant inter-observer variability among pathologists, leading to clinical inconsistencies. To address this limitation, we developed a deep learning-based artificial intelligence (AI) system that automates PD-L1 CPS quantification for patients with gastric cancer (GC) using whole slide images (WSIs).MethodsWe developed a deep learning-based artificial intelligence (AI) system that automates PD-L1 CPS quantification for patients with gastric cancer (GC) using whole slide images (WSIs). Our pipeline firstly employs a dual-network architecture for tumor region detection: MobileNet for patch-level classification and U-Net for pixel-level segmentation. Followed by a YOLO-based cell detection model to compute PD-L1 expression on different cells for CPS calculation. A total of 308 GC WSIs were included, including 210 in the internal cohort and 98 in the external cohort. Within the internal cohort, 100 WSIs were utilized for the model development, while the remaining 110 WSIs served as an internal testing set for comparative analysis between AI-derived CPS values and pathologist-derived reference standards.ResultsThe AI-derived CPS demonstrated strong concordance with expert pathologists’ consensus in internal cohort (Cohen’s kappa = 0.782). Furthermore, the AI-based CPS prediction pipeline was evaluated for its performance in the external cohort, and showed robust performance (Cohen’s kappa = 0.737).DiscussionOur system provides a standardized decision-support tool for immunotherapy stratification in GC management, demonstrating potential to improve CPS assessment reproducibility.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1614099/fullPD-L1CPSgastric cancerautomated scoringartificial intelligence |
| spellingShingle | Ting Han Meng Zhuo Ziyu Song Peilin Chen Shiting Chen Wei Zhang Yuanyuan Zhou Hong Li Dadong Zhang Xiaolin Lin Zebing Liu Xiuying Xiao Automated interpretation of PD-L1 CPS based on multi-AI models integration strategy in gastric cancer Frontiers in Immunology PD-L1 CPS gastric cancer automated scoring artificial intelligence |
| title | Automated interpretation of PD-L1 CPS based on multi-AI models integration strategy in gastric cancer |
| title_full | Automated interpretation of PD-L1 CPS based on multi-AI models integration strategy in gastric cancer |
| title_fullStr | Automated interpretation of PD-L1 CPS based on multi-AI models integration strategy in gastric cancer |
| title_full_unstemmed | Automated interpretation of PD-L1 CPS based on multi-AI models integration strategy in gastric cancer |
| title_short | Automated interpretation of PD-L1 CPS based on multi-AI models integration strategy in gastric cancer |
| title_sort | automated interpretation of pd l1 cps based on multi ai models integration strategy in gastric cancer |
| topic | PD-L1 CPS gastric cancer automated scoring artificial intelligence |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1614099/full |
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