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Comparison of continuous measures across diagnostic PD-L1 assays in non-small cell lung cancer using automated image analysis

Tumor programmed cell death ligand-1 (PD-L1) expression is a key biomarker to identify patients with non-small cell lung cancer who may have an enhanced response to anti-programmed cell death-1 (PD-1)/PD-L1 treatment. Such treatments are used in conjunction with PD-L1 diagnostic immunohistochemistry...

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Autores principales: Widmaier, Moritz, Wiestler, Tobias, Walker, Jill, Barker, Craig, Scott, Marietta L., Sekhavati, Farzad, Budco, Alexei, Schneider, Katrin, Segerer, Felix J., Steele, Keith, Rebelatto, Marlon C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group US 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7051919/
https://www.ncbi.nlm.nih.gov/pubmed/31527709
http://dx.doi.org/10.1038/s41379-019-0349-y
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author Widmaier, Moritz
Wiestler, Tobias
Walker, Jill
Barker, Craig
Scott, Marietta L.
Sekhavati, Farzad
Budco, Alexei
Schneider, Katrin
Segerer, Felix J.
Steele, Keith
Rebelatto, Marlon C.
author_facet Widmaier, Moritz
Wiestler, Tobias
Walker, Jill
Barker, Craig
Scott, Marietta L.
Sekhavati, Farzad
Budco, Alexei
Schneider, Katrin
Segerer, Felix J.
Steele, Keith
Rebelatto, Marlon C.
author_sort Widmaier, Moritz
collection PubMed
description Tumor programmed cell death ligand-1 (PD-L1) expression is a key biomarker to identify patients with non-small cell lung cancer who may have an enhanced response to anti-programmed cell death-1 (PD-1)/PD-L1 treatment. Such treatments are used in conjunction with PD-L1 diagnostic immunohistochemistry assays. We developed a computer-aided automated image analysis with customized PD-L1 scoring algorithm that was evaluated via correlation with manual pathologist scores and used to determine comparability across PD-L1 immunohistochemistry assays. The image analysis scoring algorithm was developed to quantify the percentage of PD-L1 positive tumor cells on scans of whole-slide images of archival tumor samples from commercially available non-small cell lung cancer cases, stained with four immunohistochemistry PD-L1 assays (Ventana SP263 and SP142 and Dako 22C3 and 28-8). The scans were co-registered and tumor and exclusion annotations aligned to ensure that analysis of each case was restricted to comparable tissue areas. Reference pathologist scores were available from previous studies. F1, a statistical measure of precision and recall, and overall percentage agreement scores were used to assess concordance between pathologist and image analysis scores and between immunohistochemistry assays. In total, 471 PD-L1-evalulable samples were amenable to image analysis scoring. Image analysis and pathologist scores were highly concordant, with F1 scores ranging from 0.8 to 0.9 across varying matched PD-L1 cutoffs. Based on F1 and overall percentage agreement scores (both manual and image analysis scoring), the Ventana SP263 and Dako 28-8 and 22C3 assays were concordant across a broad range of cutoffs; however, the Ventana SP142 assay showed very different characteristics. In summary, a novel automated image analysis scoring algorithm was developed that was highly correlated with pathologist scores. The algorithm permitted quantitative comparison of existing PD-L1 diagnostic assays, confirming previous findings that indicate a high concordance between the Ventana SP263 and Dako 22C3 and 28-8 PD-L1 immunohistochemistry assays.
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spelling pubmed-70519192020-03-05 Comparison of continuous measures across diagnostic PD-L1 assays in non-small cell lung cancer using automated image analysis Widmaier, Moritz Wiestler, Tobias Walker, Jill Barker, Craig Scott, Marietta L. Sekhavati, Farzad Budco, Alexei Schneider, Katrin Segerer, Felix J. Steele, Keith Rebelatto, Marlon C. Mod Pathol Article Tumor programmed cell death ligand-1 (PD-L1) expression is a key biomarker to identify patients with non-small cell lung cancer who may have an enhanced response to anti-programmed cell death-1 (PD-1)/PD-L1 treatment. Such treatments are used in conjunction with PD-L1 diagnostic immunohistochemistry assays. We developed a computer-aided automated image analysis with customized PD-L1 scoring algorithm that was evaluated via correlation with manual pathologist scores and used to determine comparability across PD-L1 immunohistochemistry assays. The image analysis scoring algorithm was developed to quantify the percentage of PD-L1 positive tumor cells on scans of whole-slide images of archival tumor samples from commercially available non-small cell lung cancer cases, stained with four immunohistochemistry PD-L1 assays (Ventana SP263 and SP142 and Dako 22C3 and 28-8). The scans were co-registered and tumor and exclusion annotations aligned to ensure that analysis of each case was restricted to comparable tissue areas. Reference pathologist scores were available from previous studies. F1, a statistical measure of precision and recall, and overall percentage agreement scores were used to assess concordance between pathologist and image analysis scores and between immunohistochemistry assays. In total, 471 PD-L1-evalulable samples were amenable to image analysis scoring. Image analysis and pathologist scores were highly concordant, with F1 scores ranging from 0.8 to 0.9 across varying matched PD-L1 cutoffs. Based on F1 and overall percentage agreement scores (both manual and image analysis scoring), the Ventana SP263 and Dako 28-8 and 22C3 assays were concordant across a broad range of cutoffs; however, the Ventana SP142 assay showed very different characteristics. In summary, a novel automated image analysis scoring algorithm was developed that was highly correlated with pathologist scores. The algorithm permitted quantitative comparison of existing PD-L1 diagnostic assays, confirming previous findings that indicate a high concordance between the Ventana SP263 and Dako 22C3 and 28-8 PD-L1 immunohistochemistry assays. Nature Publishing Group US 2019-09-16 2020 /pmc/articles/PMC7051919/ /pubmed/31527709 http://dx.doi.org/10.1038/s41379-019-0349-y Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Widmaier, Moritz
Wiestler, Tobias
Walker, Jill
Barker, Craig
Scott, Marietta L.
Sekhavati, Farzad
Budco, Alexei
Schneider, Katrin
Segerer, Felix J.
Steele, Keith
Rebelatto, Marlon C.
Comparison of continuous measures across diagnostic PD-L1 assays in non-small cell lung cancer using automated image analysis
title Comparison of continuous measures across diagnostic PD-L1 assays in non-small cell lung cancer using automated image analysis
title_full Comparison of continuous measures across diagnostic PD-L1 assays in non-small cell lung cancer using automated image analysis
title_fullStr Comparison of continuous measures across diagnostic PD-L1 assays in non-small cell lung cancer using automated image analysis
title_full_unstemmed Comparison of continuous measures across diagnostic PD-L1 assays in non-small cell lung cancer using automated image analysis
title_short Comparison of continuous measures across diagnostic PD-L1 assays in non-small cell lung cancer using automated image analysis
title_sort comparison of continuous measures across diagnostic pd-l1 assays in non-small cell lung cancer using automated image analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7051919/
https://www.ncbi.nlm.nih.gov/pubmed/31527709
http://dx.doi.org/10.1038/s41379-019-0349-y
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