Automated PD-L1 Scoring Using Artificial Intelligence in Head and Neck Squamous Cell Carcinoma

Cancers

Khosrow Siamak Houschyar¹  Behrus Puladi²  Mark Ooms²  Ali Modabber²  Frank Hölzle²  Till Braunschweig³  Ruth Knüchel-Clarke³  Florian Steib³  Svetlana Kintsler³ 

[1] Department of Dermatology and Allergology, University Hospital RWTH Aachen, 52074 Aachen, Germany;Department of Oral and Maxillofacial Surgery, University Hospital RWTH Aachen, 52074 Aachen, Germany;Institute of Pathology, University Hospital RWTH Aachen, 52074 Aachen, Germany;

关键词: PD-L1 scoring;    head and neck squamous cell carcinoma;    deep learning;    tumor detection;    medical image analysis;    open-source;   

DOI  :  10.3390/cancers13174409

来源: DOAJ

【 摘 要 】

Immune checkpoint inhibitors (ICI) represent a new therapeutic approach in recurrent and metastatic head and neck squamous cell carcinoma (HNSCC). The patient selection for the PD-1/PD-L1 inhibitor therapy is based on the degree of PD-L1 expression in immunohistochemistry reflected by manually determined PD-L1 scores. However, manual scoring shows variability between different investigators and is influenced by cognitive and visual traps and could therefore negatively influence treatment decisions. Automated PD-L1 scoring could facilitate reliable and reproducible results. Our novel approach uses three neural networks sequentially applied for fully automated PD-L1 scoring of all three established PD-L1 scores: tumor proportion score (TPS), combined positive score (CPS) and tumor-infiltrating immune cell score (ICS). Our approach was validated using WSIs of HNSCC cases and compared with manual PD-L1 scoring by human investigators. The inter-rater correlation (ICC) between human and machine was very similar to the human-human correlation. The ICC was slightly higher between human-machine compared to human-human for the CPS and ICS, but a slightly lower for the TPS. Our study provides deeper insights into automated PD-L1 scoring by neural networks and its limitations. This may serve as a basis to improve ICI patient selection in the future.

【 授权许可】

Unknown