Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/216349
Title: Concordance in the estimation of tumor percentage in non-small cell lung cancer using digital pathology
Author: Carretero Barrio, Irene
Pijuan, Lara
Illarramendi, Adrián
Curto, Daniel
López Ríos, Fernando
Estébanez Gallo, Ángel
Castellvi, Josep
Granados Aparici, Sofía
Compañ Quilis, Desamparados
Noguera, Rosa
Esteban Rodríguez, Isabel
Sánchez Güerri, Ignacio
Ramos Guerra, Ana Delia
Ortuño, Juan Enrique
Garrido, Pilar
Ledesma Carbayo, María Jesús
Benito, Amparo
Palacios, José
Keywords: Càncer de pulmó
Histopatologia
Lung cancer
Pathological histology
Issue Date: 15-Oct-2024
Publisher: Springer Science and Business Media LLC
Abstract: 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.
Note: Reproducció del document publicat a: https://doi.org/10.1038/s41598-024-75175-w
It is part of: Scientific Reports, 2024, vol. 14, num. 1
URI: https://hdl.handle.net/2445/216349
Related resource: https://doi.org/10.1038/s41598-024-75175-w
ISSN: 2045-2322
Appears in Collections:Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))

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