Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma
Abstract Diffuse large B cell lymphoma (DLBCL) is an aggressive blood cancer known for its rapid progression and high incidence. The growing use of immunohistochemistry (IHC) has significantly contributed to the detailed cell characterization, thereby playing a crucial role in guiding treatment stra...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-03-01
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| Series: | npj Precision Oncology |
| Online Access: | https://doi.org/10.1038/s41698-024-00577-y |
| _version_ | 1797233809007050752 |
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| author | Fang Yan Qian Da Hongmei Yi Shijie Deng Lifeng Zhu Mu Zhou Yingting Liu Ming Feng Jing Wang Xuan Wang Yuxiu Zhang Wenjing Zhang Xiaofan Zhang Jingsheng Lin Shaoting Zhang Chaofu Wang |
| author_facet | Fang Yan Qian Da Hongmei Yi Shijie Deng Lifeng Zhu Mu Zhou Yingting Liu Ming Feng Jing Wang Xuan Wang Yuxiu Zhang Wenjing Zhang Xiaofan Zhang Jingsheng Lin Shaoting Zhang Chaofu Wang |
| author_sort | Fang Yan |
| collection | DOAJ |
| description | Abstract Diffuse large B cell lymphoma (DLBCL) is an aggressive blood cancer known for its rapid progression and high incidence. The growing use of immunohistochemistry (IHC) has significantly contributed to the detailed cell characterization, thereby playing a crucial role in guiding treatment strategies for DLBCL. In this study, we developed an AI-based image analysis approach for assessing PD-L1 expression in DLBCL patients. PD-L1 expression represents as a major biomarker for screening patients who can benefit from targeted immunotherapy interventions. In particular, we performed large-scale cell annotations in IHC slides, encompassing over 5101 tissue regions and 146,439 live cells. Extensive experiments in primary and validation cohorts demonstrated the defined quantitative rule helped overcome the difficulty of identifying specific cell types. In assessing data obtained from fine needle biopsies, experiments revealed that there was a higher level of agreement in the quantitative results between Artificial Intelligence (AI) algorithms and pathologists, as well as among pathologists themselves, in comparison to the data obtained from surgical specimens. We highlight that the AI-enabled analytics enhance the objectivity and interpretability of PD-L1 quantification to improve the targeted immunotherapy development in DLBCL patients. |
| first_indexed | 2024-04-24T16:22:04Z |
| format | Article |
| id | doaj.art-1ce1530f669a4a3ba193648ec48199e2 |
| institution | Directory Open Access Journal |
| issn | 2397-768X |
| language | English |
| last_indexed | 2024-04-24T16:22:04Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Precision Oncology |
| spelling | doaj.art-1ce1530f669a4a3ba193648ec48199e22024-03-31T11:09:40ZengNature Portfolionpj Precision Oncology2397-768X2024-03-018111210.1038/s41698-024-00577-yArtificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphomaFang Yan0Qian Da1Hongmei Yi2Shijie Deng3Lifeng Zhu4Mu Zhou5Yingting Liu6Ming Feng7Jing Wang8Xuan Wang9Yuxiu Zhang10Wenjing Zhang11Xiaofan Zhang12Jingsheng Lin13Shaoting Zhang14Chaofu Wang15Shanghai Artificial Intelligence LaboratoryRuijin Hospital, Shanghai Jiao Tong University School of MedicineRuijin Hospital, Shanghai Jiao Tong University School of MedicineRuijin Hospital, Shanghai Jiao Tong University School of MedicineRuijin Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Computer Science, Rutgers UniversityRuijin Hospital, Shanghai Jiao Tong University School of MedicineCollege of Electronic and Information Engineering, Tongji UniversityRuijin Hospital, Shanghai Jiao Tong University School of MedicineRuijin Hospital, Shanghai Jiao Tong University School of MedicineRuijin Hospital, Shanghai Jiao Tong University School of MedicineRuijin Hospital, Shanghai Jiao Tong University School of MedicineShanghai Artificial Intelligence LaboratoryRuijin Hospital, Shanghai Jiao Tong University School of MedicineShanghai Artificial Intelligence LaboratoryRuijin Hospital, Shanghai Jiao Tong University School of MedicineAbstract Diffuse large B cell lymphoma (DLBCL) is an aggressive blood cancer known for its rapid progression and high incidence. The growing use of immunohistochemistry (IHC) has significantly contributed to the detailed cell characterization, thereby playing a crucial role in guiding treatment strategies for DLBCL. In this study, we developed an AI-based image analysis approach for assessing PD-L1 expression in DLBCL patients. PD-L1 expression represents as a major biomarker for screening patients who can benefit from targeted immunotherapy interventions. In particular, we performed large-scale cell annotations in IHC slides, encompassing over 5101 tissue regions and 146,439 live cells. Extensive experiments in primary and validation cohorts demonstrated the defined quantitative rule helped overcome the difficulty of identifying specific cell types. In assessing data obtained from fine needle biopsies, experiments revealed that there was a higher level of agreement in the quantitative results between Artificial Intelligence (AI) algorithms and pathologists, as well as among pathologists themselves, in comparison to the data obtained from surgical specimens. We highlight that the AI-enabled analytics enhance the objectivity and interpretability of PD-L1 quantification to improve the targeted immunotherapy development in DLBCL patients.https://doi.org/10.1038/s41698-024-00577-y |
| spellingShingle | Fang Yan Qian Da Hongmei Yi Shijie Deng Lifeng Zhu Mu Zhou Yingting Liu Ming Feng Jing Wang Xuan Wang Yuxiu Zhang Wenjing Zhang Xiaofan Zhang Jingsheng Lin Shaoting Zhang Chaofu Wang Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma npj Precision Oncology |
| title | Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma |
| title_full | Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma |
| title_fullStr | Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma |
| title_full_unstemmed | Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma |
| title_short | Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma |
| title_sort | artificial intelligence based assessment of pd l1 expression in diffuse large b cell lymphoma |
| url | https://doi.org/10.1038/s41698-024-00577-y |
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