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Automated PD-L1 Scoring for Non-Small Cell Lung Carcinoma Using Open-Source Software

PD-L1 expression in non-small cell lung cancer (NSCLC) is predictive of response to immunotherapy, but scoring of PD-L1 immunohistochemistry shows considerable interobserver variability. Automated methods may allow more consistent and expedient PD-L1 scoring. We aimed to assess the technical concord...

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Autores principales: Naso, Julia R., Povshedna, Tetiana, Wang, Gang, Banyi, Norbert, MacAulay, Calum, Ionescu, Diana N., Zhou, Chen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8262183/
https://www.ncbi.nlm.nih.gov/pubmed/34257575
http://dx.doi.org/10.3389/pore.2021.609717
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author Naso, Julia R.
Povshedna, Tetiana
Wang, Gang
Banyi, Norbert
MacAulay, Calum
Ionescu, Diana N.
Zhou, Chen
author_facet Naso, Julia R.
Povshedna, Tetiana
Wang, Gang
Banyi, Norbert
MacAulay, Calum
Ionescu, Diana N.
Zhou, Chen
author_sort Naso, Julia R.
collection PubMed
description PD-L1 expression in non-small cell lung cancer (NSCLC) is predictive of response to immunotherapy, but scoring of PD-L1 immunohistochemistry shows considerable interobserver variability. Automated methods may allow more consistent and expedient PD-L1 scoring. We aimed to assess the technical concordance of PD-L1 scores produced using free open source QuPath software with the manual scores of three pathologists. A classifier for PD-L1 scoring was trained using 30 NSCLC image patches. A separate test set of 207 image patches from 69 NSCLC resection cases was used for comparison of automated and manual scores. Automated and average manual scores showed excellent correlation (concordance correlation coeffecient = 0.925), though automated scoring resulted in significantly more 1–49% scores than manual scoring (p = 0.012). At both 1% and 50% thresholds, automated scores showed a level of concordance with our ‘gold standard’ (the average of three pathologists’ manual scores) similar to that of individual pathologists. Automated scoring showed high sensitivity (95%) but lower specificity (84%) at a 1% threshold, and excellent specificity (100%) but lower sensitivity (71%) at a 50% threshold. We conclude that our automated PD-L1 scoring system for NSCLC has an accuracy similar to that of individual pathologists. The detailed protocol we provide for free open source scoring software and our discussion of the limitations of this technology may facilitate more effective integration of automated scoring into clinical workflows.
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spelling pubmed-82621832021-07-12 Automated PD-L1 Scoring for Non-Small Cell Lung Carcinoma Using Open-Source Software Naso, Julia R. Povshedna, Tetiana Wang, Gang Banyi, Norbert MacAulay, Calum Ionescu, Diana N. Zhou, Chen Pathol Oncol Res Society Journal Archive PD-L1 expression in non-small cell lung cancer (NSCLC) is predictive of response to immunotherapy, but scoring of PD-L1 immunohistochemistry shows considerable interobserver variability. Automated methods may allow more consistent and expedient PD-L1 scoring. We aimed to assess the technical concordance of PD-L1 scores produced using free open source QuPath software with the manual scores of three pathologists. A classifier for PD-L1 scoring was trained using 30 NSCLC image patches. A separate test set of 207 image patches from 69 NSCLC resection cases was used for comparison of automated and manual scores. Automated and average manual scores showed excellent correlation (concordance correlation coeffecient = 0.925), though automated scoring resulted in significantly more 1–49% scores than manual scoring (p = 0.012). At both 1% and 50% thresholds, automated scores showed a level of concordance with our ‘gold standard’ (the average of three pathologists’ manual scores) similar to that of individual pathologists. Automated scoring showed high sensitivity (95%) but lower specificity (84%) at a 1% threshold, and excellent specificity (100%) but lower sensitivity (71%) at a 50% threshold. We conclude that our automated PD-L1 scoring system for NSCLC has an accuracy similar to that of individual pathologists. The detailed protocol we provide for free open source scoring software and our discussion of the limitations of this technology may facilitate more effective integration of automated scoring into clinical workflows. Frontiers Media S.A. 2021-03-26 /pmc/articles/PMC8262183/ /pubmed/34257575 http://dx.doi.org/10.3389/pore.2021.609717 Text en Copyright © 2021 Naso, Povshedna, Wang, Banyi, MacAulay, Ionescu and Zhou. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Society Journal Archive
Naso, Julia R.
Povshedna, Tetiana
Wang, Gang
Banyi, Norbert
MacAulay, Calum
Ionescu, Diana N.
Zhou, Chen
Automated PD-L1 Scoring for Non-Small Cell Lung Carcinoma Using Open-Source Software
title Automated PD-L1 Scoring for Non-Small Cell Lung Carcinoma Using Open-Source Software
title_full Automated PD-L1 Scoring for Non-Small Cell Lung Carcinoma Using Open-Source Software
title_fullStr Automated PD-L1 Scoring for Non-Small Cell Lung Carcinoma Using Open-Source Software
title_full_unstemmed Automated PD-L1 Scoring for Non-Small Cell Lung Carcinoma Using Open-Source Software
title_short Automated PD-L1 Scoring for Non-Small Cell Lung Carcinoma Using Open-Source Software
title_sort automated pd-l1 scoring for non-small cell lung carcinoma using open-source software
topic Society Journal Archive
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8262183/
https://www.ncbi.nlm.nih.gov/pubmed/34257575
http://dx.doi.org/10.3389/pore.2021.609717
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