Automated tumor proportion score analysis for PD-L1 (22C3) expression in lung squamous cell carcinoma
Abstract Programmed cell death ligend-1 (PD-L1) expression by immunohistochemistry (IHC) assays is a predictive marker of anti-PD-1/PD-L1 therapy response. With the popularity of anti-PD-1/PD-L1 inhibitor drugs, quantitative assessment of PD-L1 expression becomes a new labor for pathologists. Manual...
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Nature Portfolio
2021
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oai:doaj.org-article:b35f67f9350b49faac9dce59092b83022021-12-02T16:35:31ZAutomated tumor proportion score analysis for PD-L1 (22C3) expression in lung squamous cell carcinoma10.1038/s41598-021-95372-12045-2322https://doaj.org/article/b35f67f9350b49faac9dce59092b83022021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95372-1https://doaj.org/toc/2045-2322Abstract Programmed cell death ligend-1 (PD-L1) expression by immunohistochemistry (IHC) assays is a predictive marker of anti-PD-1/PD-L1 therapy response. With the popularity of anti-PD-1/PD-L1 inhibitor drugs, quantitative assessment of PD-L1 expression becomes a new labor for pathologists. Manually counting the PD-L1 positive stained tumor cells is an obviously subjective and time-consuming process. In this paper, we developed a new computer aided Automated Tumor Proportion Scoring System (ATPSS) to determine the comparability of image analysis with pathologist scores. A three-stage process was performed using both image processing and deep learning techniques to mimic the actual diagnostic flow of the pathologists. We conducted a multi-reader multi-case study to evaluate the agreement between pathologists and ATPSS. Fifty-one surgically resected lung squamous cell carcinoma were prepared and stained using the Dako PD-L1 (22C3) assay, and six pathologists with different experience levels were involved in this study. The TPS predicted by the proposed model had high and statistically significant correlation with sub-specialty pathologists’ scores with Mean Absolute Error (MAE) of 8.65 (95% confidence interval (CI): 6.42–10.90) and Pearson Correlation Coefficient (PCC) of 0.9436 ( $$p < 0.001$$ p < 0.001 ), and the performance on PD-L1 positive cases achieved by our method surpassed that of non-subspecialty and trainee pathologists. Those experimental results indicate that the proposed automated system can be a powerful tool to improve the PD-L1 TPS assessment of pathologists.Jingxin LiuQiang ZhengXiao MuYanfei ZuoBo XuYan JinYue WangHua TianYongguo YangQianqian XueZiling HuangLijun ChenBin GuXianxu HouLinlin ShenYan GuoYuan LiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021) |
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Medicine R Science Q |
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Medicine R Science Q Jingxin Liu Qiang Zheng Xiao Mu Yanfei Zuo Bo Xu Yan Jin Yue Wang Hua Tian Yongguo Yang Qianqian Xue Ziling Huang Lijun Chen Bin Gu Xianxu Hou Linlin Shen Yan Guo Yuan Li Automated tumor proportion score analysis for PD-L1 (22C3) expression in lung squamous cell carcinoma |
| description |
Abstract Programmed cell death ligend-1 (PD-L1) expression by immunohistochemistry (IHC) assays is a predictive marker of anti-PD-1/PD-L1 therapy response. With the popularity of anti-PD-1/PD-L1 inhibitor drugs, quantitative assessment of PD-L1 expression becomes a new labor for pathologists. Manually counting the PD-L1 positive stained tumor cells is an obviously subjective and time-consuming process. In this paper, we developed a new computer aided Automated Tumor Proportion Scoring System (ATPSS) to determine the comparability of image analysis with pathologist scores. A three-stage process was performed using both image processing and deep learning techniques to mimic the actual diagnostic flow of the pathologists. We conducted a multi-reader multi-case study to evaluate the agreement between pathologists and ATPSS. Fifty-one surgically resected lung squamous cell carcinoma were prepared and stained using the Dako PD-L1 (22C3) assay, and six pathologists with different experience levels were involved in this study. The TPS predicted by the proposed model had high and statistically significant correlation with sub-specialty pathologists’ scores with Mean Absolute Error (MAE) of 8.65 (95% confidence interval (CI): 6.42–10.90) and Pearson Correlation Coefficient (PCC) of 0.9436 ( $$p < 0.001$$ p < 0.001 ), and the performance on PD-L1 positive cases achieved by our method surpassed that of non-subspecialty and trainee pathologists. Those experimental results indicate that the proposed automated system can be a powerful tool to improve the PD-L1 TPS assessment of pathologists. |
| format |
article |
| author |
Jingxin Liu Qiang Zheng Xiao Mu Yanfei Zuo Bo Xu Yan Jin Yue Wang Hua Tian Yongguo Yang Qianqian Xue Ziling Huang Lijun Chen Bin Gu Xianxu Hou Linlin Shen Yan Guo Yuan Li |
| author_facet |
Jingxin Liu Qiang Zheng Xiao Mu Yanfei Zuo Bo Xu Yan Jin Yue Wang Hua Tian Yongguo Yang Qianqian Xue Ziling Huang Lijun Chen Bin Gu Xianxu Hou Linlin Shen Yan Guo Yuan Li |
| author_sort |
Jingxin Liu |
| title |
Automated tumor proportion score analysis for PD-L1 (22C3) expression in lung squamous cell carcinoma |
| title_short |
Automated tumor proportion score analysis for PD-L1 (22C3) expression in lung squamous cell carcinoma |
| title_full |
Automated tumor proportion score analysis for PD-L1 (22C3) expression in lung squamous cell carcinoma |
| title_fullStr |
Automated tumor proportion score analysis for PD-L1 (22C3) expression in lung squamous cell carcinoma |
| title_full_unstemmed |
Automated tumor proportion score analysis for PD-L1 (22C3) expression in lung squamous cell carcinoma |
| title_sort |
automated tumor proportion score analysis for pd-l1 (22c3) expression in lung squamous cell carcinoma |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/b35f67f9350b49faac9dce59092b8302 |
| work_keys_str_mv |
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