Machine-learning-based image analysis algorithms improve interpathologist concordance when scoring PD-L1 expression in non-small-cell lung cancer
Programmed death ligand 1 (PD-L1) expression on tumour cells is the only predictive biomarker of response to immuno-modulatory therapy for patients with non-small-cell lung cancer (NSCLC). Accuracy of this biomarker is hampered by its challenging interpretation. Here we explore if the use of machine-learning derived image analysis tools can improve interpathologist concordance of assessing PD-L1 expression in NSCLC.
Five pathologists who routinely score PD-L1 at a major regional referral hospital for thoracic surgery participated. 13 NSCLC small diagnostic biopsies were stained for PD-L1 (SP263 clone) and digitally scanned. Each pathologist independently scored each case with and without the Roche uPath PD-L1 (SP263) image analysis NSCLC algorithm with a wash-out interim period of 6 weeks.
A consistent improvement in interpathologist concordance was seen when using the image analysis tool compared with scoring without: (Fleiss’ kappa 0.886 vs 0.613 (p<0.0001) and intraclass coefficient correlation 0.954 vs 0.837 (p<0.001)). Five cases (38%) were classified into clinically relevant different categories (negative/weak/strong) by multiple pathologists when not using the image analysis algorithm, whereas only two cases (15%) were classified differently when using the image analysis algorithm.
The use of the image analysis algorithm improved the concordance of assessing PD-L1 expression between pathologists. Critically, there was a marked improvement in the placement o...
Source: Journal of Clinical Pathology - Category: Pathology Authors: Haragan, A., Parashar, P., Bury, D., Cross, G., Gosney, J. R. Tags: Open access Short report Source Type: research
More News: Cancer | Cancer & Oncology | Cardiovascular & Thoracic Surgery | Hospitals | Learning | Lung Cancer | Non-Small Cell Lung Cancer | Pathology | PET Scan | Study | Universities & Medical Training