Deep Semi Supervised Generative Learning for Automated Tumor Proportion Scoring on NSCLC Tissue Needle Biopsies

• Published in: Scientific reports Vol. 8; no. 1; pp. 17343 - 10
• Main Authors: Kapil, Ansh, Meier, Armin, Zuraw, Aleksandra, Steele, Keith E., Rebelatto, Marlon C., Schmidt, Günter, Brieu, Nicolas
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 26-11-2018; Nature Publishing Group
• Subjects: 631/114/1305; 631/114/1564; 631/250/2503; 692/4028/67/1612/1350; 692/53/2421; 82/51; Automation; B7-H1 Antigen - analysis; Biopsy; Biopsy, Needle - methods; Cancer; Carcinoma, Non-Small-Cell Lung - pathology; Humanities and Social Sciences; Humans; Image Processing, Computer-Assisted - methods; Immunohistochemistry; Immunohistochemistry - methods; Lung cancer; Lung Neoplasms - pathology; multidisciplinary; Non-small cell lung carcinoma; PD-1 protein; PD-L1 protein; Science; Science (multidisciplinary); Supervised Machine Learning; Training; Tumor cells; Tumor Proportion Score; Automatic Scoring; Programmed Death-ligand 1 (PD-L1); Concordance Measure; Visual Scoring
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-018-35501-5

The level of PD-L1 expression in immunohistochemistry (IHC) assays is a key biomarker for the identification of Non-Small-Cell-Lung-Cancer (NSCLC) patients that may respond to anti PD-1/PD-L1 treatments. The quantification of PD-L1 expression currently includes the visual estimation by a pathologist of the percentage (tumor proportional scoring or TPS) of tumor cells showing PD-L1 staining. Known challenges like differences in positivity estimation around clinically relevant cut-offs and sub-optimal quality of samples makes visual scoring tedious and subjective, yielding a scoring variability between pathologists. In this work, we propose a novel deep learning solution that enables the first automated and objective scoring of PD-L1 expression in late stage NSCLC needle biopsies. To account for the low amount of tissue available in biopsy images and to restrict the amount of manual annotations necessary for training, we explore the use of semi-supervised approaches against standard fully supervised methods. We consolidate the manual annotations used for training as well the visual TPS scores used for quantitative evaluation with multiple pathologists. Concordance measures computed on a set of slides unseen during training provide evidence that our automatic scoring method matches visual scoring on the considered dataset while ensuring repeatability and objectivity.