Concordance in the estimation of tumor percentage in non-small cell lung cancer using digital pathology

Abstract

Non-small cell lung cancer; Digital pathology

Cáncer de pulmón de células no pequeñas; Patología digital

Càncer de pulmó de cèl·lules no petites; Patologia digital

The incorporation of digital pathology in clinical practice will require the training of pathologists in digital skills. Our study aimed to assess the reliability among pathologists in determining tumor percentage in whole slide images (WSI) of non-small cell lung cancer (NSCLC) using digital image analysis, and study how the results correlate with the molecular findings. Pathologists from nine centers were trained to quantify epithelial tumor cells, tumor-associated stromal cells, and non-neoplastic cells from NSCLC WSI using QuPath. Then, we conducted two consecutive ring trials. In the first trial, analyzing four WSI, reliability between pathologists in the assessment of tumor cell percentage was poor (intraclass correlation coefficient (ICC) 0.09). After performing the first ring trial pathologists received feedback. The second trial, comprising 10 WSI with paired next-generation sequencing results, also showed poor reliability (ICC 0.24). Cases near the recommended 20% visual threshold for molecular techniques exhibited higher values with digital analysis. In the second ring trial reliability slightly improved and human errors were reduced from 5.6% to 1.25%. Most discrepancies arose from subjective tasks, such as the annotation process, suggesting potential improvement with future artificial intelligence solutions.

This work has been supported by CIBERONC (grant CB16/12/00316), by Instituto de Salud Carlos III (ISCIII) through the project “PI22/01892”, by the European Union. Developed with the financial support of Immune4ALL Project (PMP22/00054), with European funds of the Recovery, Transformation and Resilence Plan; and by the Spanish Ministry of Science, Innovation and Universities under grants PID2022-141493OB-I00 and PDC2022-133865-I00 (https://doi.org/10.13039/501100011033/MCIN/AEI/ERDF, UE) - NextGenerationEU. FLR also acknowledges the support of Fundacion Mutua Madrileña (AP18051-2022), Instituto de Salud Carlos III (ISCIII) (PI22-01700, co-funded by the European Union) and the Comunidad de Madrid iLUNG Program (P2022/BMD-7437). CÓDIGO DE PROYECTO: PMP21/00107 - TÍTULO: “INtegrative GENomic, digital Imaging and clinical information towards Precision Oncology Optimization – INGENIO” - IP: LUIS PAZ-ARES RODRIGUEZ “ENTIDAD FINANCIADORA: INSTITUTO DE SALUD CARLOS III (ISCIII). PROYECTO “FINANCIADO CON CARGO A FONDOS NEXTGENERATIONEU, QUE FINANCIAN LAS ACTUACIONES DEL MRR”.