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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doaj-b35f67f9350b49faac9dce59092b83022021-08-08T11:23:45ZengNature Publishing GroupScientific Reports2045-23222021-08-011111910.1038/s41598-021-95372-1Automated tumor proportion score analysis for PD-L1 (22C3) expression in lung squamous cell carcinomaJingxin Liu0Qiang Zheng1Xiao Mu2Yanfei Zuo3Bo Xu4Yan Jin5Yue Wang6Hua Tian7Yongguo Yang8Qianqian Xue9Ziling Huang10Lijun Chen11Bin Gu12Xianxu Hou13Linlin Shen14Yan Guo15Yuan Li16Department of Pathology, Fudan University Shanghai Cancer CenterDepartment of Pathology, Fudan University Shanghai Cancer CenterHisto Pathology Diagnostic CenterHisto Pathology Diagnostic CenterHisto Pathology Diagnostic CenterDepartment of Pathology, Fudan University Shanghai Cancer CenterDepartment of Pathology, Fudan University Shanghai Cancer CenterDepartment of Pathology, Yangzhou Jiangdu People’s HospitalDepartment of Pathology, Yangzhou Jiangdu People’s HospitalDepartment of Pathology, Fudan University Shanghai Cancer CenterDepartment of Pathology, Fudan University Shanghai Cancer CenterDepartment of Pathology, Fudan University Shanghai Cancer CenterHisto Pathology Diagnostic CenterComputer Vision Institute, School of Computer Science and Software Engineering, Shenzhen UniversityComputer Vision Institute, School of Computer Science and Software Engineering, Shenzhen UniversityHisto Pathology Diagnostic CenterDepartment of Pathology, Fudan University Shanghai Cancer CenterAbstract 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.https://doi.org/10.1038/s41598-021-95372-1 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
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 |
spellingShingle |
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 Scientific Reports |
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 Publishing Group |
series |
Scientific Reports |
issn |
2045-2322 |
publishDate |
2021-08-01 |
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. |
url |
https://doi.org/10.1038/s41598-021-95372-1 |
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