How can artificial intelligence models assist PD-L1 expression scoring in breast cancer: results of multi-institutional ring studies
Abstract Programmed death ligand-1 (PD-L1) expression is a key biomarker to screen patients for PD-1/PD-L1-targeted immunotherapy. However, a subjective assessment guide on PD-L1 expression of tumor-infiltrating immune cells (IC) scoring is currently adopted in clinical practice with low concordance...
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2021
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oai:doaj.org-article:dcd2ce87231243738367b9b9bb95ee4c2021-12-02T16:52:56ZHow can artificial intelligence models assist PD-L1 expression scoring in breast cancer: results of multi-institutional ring studies10.1038/s41523-021-00268-y2374-4677https://doaj.org/article/dcd2ce87231243738367b9b9bb95ee4c2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41523-021-00268-yhttps://doaj.org/toc/2374-4677Abstract Programmed death ligand-1 (PD-L1) expression is a key biomarker to screen patients for PD-1/PD-L1-targeted immunotherapy. However, a subjective assessment guide on PD-L1 expression of tumor-infiltrating immune cells (IC) scoring is currently adopted in clinical practice with low concordance. Therefore, a repeatable and quantifiable PD-L1 IC scoring method of breast cancer is desirable. In this study, we propose a deep learning-based artificial intelligence-assisted (AI-assisted) model for PD-L1 IC scoring. Three rounds of ring studies (RSs) involving 31 pathologists from 10 hospitals were carried out, using the current guideline in the first two rounds (RS1, RS2) and our AI scoring model in the last round (RS3). A total of 109 PD-L1 (Ventana SP142) immunohistochemistry (IHC) stained images were assessed and the role of the AI-assisted model was evaluated. With the assistance of AI, the scoring concordance across pathologists was boosted to excellent in RS3 (0.950, 95% confidence interval (CI): 0.936–0.962) from moderate in RS1 (0.674, 95% CI: 0.614–0.735) and RS2 (0.736, 95% CI: 0.683–0.789). The 2- and 4-category scoring accuracy were improved by 4.2% (0.959, 95% CI: 0.953–0.964) and 13% (0.815, 95% CI: 0.803–0.827) (p < 0.001). The AI results were generally accepted by pathologists with 61% “fully accepted” and 91% “almost accepted”. The proposed AI-assisted method can help pathologists at all levels to improve the PD-L1 assay (SP-142) IC assessment in breast cancer in terms of both accuracy and concordance. The AI tool provides a scheme to standardize the PD-L1 IC scoring in clinical practice.Xinran WangLiang WangHong BuNingning ZhangMeng YueZhanli JiaLijing CaiJiankun HeYanan WangXin XuShengshui LiKaiwen XiaoKezhou YanKuan TianXiao HanJunzhou HuangJianhua YaoYueping LiuNature PortfolioarticleNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENnpj Breast Cancer, Vol 7, Iss 1, Pp 1-10 (2021) |
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Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 |
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Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Xinran Wang Liang Wang Hong Bu Ningning Zhang Meng Yue Zhanli Jia Lijing Cai Jiankun He Yanan Wang Xin Xu Shengshui Li Kaiwen Xiao Kezhou Yan Kuan Tian Xiao Han Junzhou Huang Jianhua Yao Yueping Liu How can artificial intelligence models assist PD-L1 expression scoring in breast cancer: results of multi-institutional ring studies |
| description |
Abstract Programmed death ligand-1 (PD-L1) expression is a key biomarker to screen patients for PD-1/PD-L1-targeted immunotherapy. However, a subjective assessment guide on PD-L1 expression of tumor-infiltrating immune cells (IC) scoring is currently adopted in clinical practice with low concordance. Therefore, a repeatable and quantifiable PD-L1 IC scoring method of breast cancer is desirable. In this study, we propose a deep learning-based artificial intelligence-assisted (AI-assisted) model for PD-L1 IC scoring. Three rounds of ring studies (RSs) involving 31 pathologists from 10 hospitals were carried out, using the current guideline in the first two rounds (RS1, RS2) and our AI scoring model in the last round (RS3). A total of 109 PD-L1 (Ventana SP142) immunohistochemistry (IHC) stained images were assessed and the role of the AI-assisted model was evaluated. With the assistance of AI, the scoring concordance across pathologists was boosted to excellent in RS3 (0.950, 95% confidence interval (CI): 0.936–0.962) from moderate in RS1 (0.674, 95% CI: 0.614–0.735) and RS2 (0.736, 95% CI: 0.683–0.789). The 2- and 4-category scoring accuracy were improved by 4.2% (0.959, 95% CI: 0.953–0.964) and 13% (0.815, 95% CI: 0.803–0.827) (p < 0.001). The AI results were generally accepted by pathologists with 61% “fully accepted” and 91% “almost accepted”. The proposed AI-assisted method can help pathologists at all levels to improve the PD-L1 assay (SP-142) IC assessment in breast cancer in terms of both accuracy and concordance. The AI tool provides a scheme to standardize the PD-L1 IC scoring in clinical practice. |
| format |
article |
| author |
Xinran Wang Liang Wang Hong Bu Ningning Zhang Meng Yue Zhanli Jia Lijing Cai Jiankun He Yanan Wang Xin Xu Shengshui Li Kaiwen Xiao Kezhou Yan Kuan Tian Xiao Han Junzhou Huang Jianhua Yao Yueping Liu |
| author_facet |
Xinran Wang Liang Wang Hong Bu Ningning Zhang Meng Yue Zhanli Jia Lijing Cai Jiankun He Yanan Wang Xin Xu Shengshui Li Kaiwen Xiao Kezhou Yan Kuan Tian Xiao Han Junzhou Huang Jianhua Yao Yueping Liu |
| author_sort |
Xinran Wang |
| title |
How can artificial intelligence models assist PD-L1 expression scoring in breast cancer: results of multi-institutional ring studies |
| title_short |
How can artificial intelligence models assist PD-L1 expression scoring in breast cancer: results of multi-institutional ring studies |
| title_full |
How can artificial intelligence models assist PD-L1 expression scoring in breast cancer: results of multi-institutional ring studies |
| title_fullStr |
How can artificial intelligence models assist PD-L1 expression scoring in breast cancer: results of multi-institutional ring studies |
| title_full_unstemmed |
How can artificial intelligence models assist PD-L1 expression scoring in breast cancer: results of multi-institutional ring studies |
| title_sort |
how can artificial intelligence models assist pd-l1 expression scoring in breast cancer: results of multi-institutional ring studies |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/dcd2ce87231243738367b9b9bb95ee4c |
| work_keys_str_mv |
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