Pathology Quality Control for Multiplex Immunofluorescence and Image Analysis Assessment in Longitudinal Studies
Immune profiling of formalin-fixed, paraffin-embedded tissues using multiplex immunofluorescence (mIF) staining and image analysis methodology allows for the study of several biomarkers on a single slide. The pathology quality control (PQC) for tumor tissue immune profiling using digital image analy...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-07-01
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| Series: | Frontiers in Molecular Biosciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2021.661222/full |
| _version_ | 1818607545123602432 |
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| author | Rossana Lazcano Frank Rojas Caddie Laberiano Sharia Hernandez Edwin Roger Parra |
| author_facet | Rossana Lazcano Frank Rojas Caddie Laberiano Sharia Hernandez Edwin Roger Parra |
| author_sort | Rossana Lazcano |
| collection | DOAJ |
| description | Immune profiling of formalin-fixed, paraffin-embedded tissues using multiplex immunofluorescence (mIF) staining and image analysis methodology allows for the study of several biomarkers on a single slide. The pathology quality control (PQC) for tumor tissue immune profiling using digital image analysis of core needle biopsies is an important step in any laboratory to avoid wasting time and materials. Although there are currently no established inclusion and exclusion criteria for samples used in this type of assay, a PQC is necessary to achieve accurate and reproducible data. We retrospectively reviewed PQC data from hematoxylin and eosin (H&E) slides and from mIF image analysis samples obtained during 2019. We reviewed a total of 931 reports from core needle biopsy samples; 123 (13.21%) were excluded during the mIF PQC. The most common causes of exclusion were the absence of malignant cells or fewer than 100 malignant cells in the entire section (n = 42, 34.15%), tissue size smaller than 4 × 1 mm (n = 16, 13.01%), fibrotic tissue without inflammatory cells (n = 12, 9.76%), and necrotic tissue (n = 11, 8.94%). Baseline excluded samples had more fibrosis (90 vs 10%) and less necrosis (5 vs 90%) compared with post-treatment excluded samples. The most common excluded organ site of the biopsy was the liver (n = 19, 15.45%), followed by soft tissue (n = 17, 13.82%) and the abdominal region (n = 15, 12.20%). We showed that the PQC is an important step for image analysis and that the absence of malignant cells is the most limiting sample characteristic for mIF image analysis. We also discuss other challenges that pathologists need to consider to report reliable and reproducible image analysis data. |
| first_indexed | 2024-12-16T14:28:27Z |
| format | Article |
| id | doaj.art-69e99ed80bab43d8a9dcde0c4a6b4b3c |
| institution | Directory Open Access Journal |
| issn | 2296-889X |
| language | English |
| last_indexed | 2024-12-16T14:28:27Z |
| publishDate | 2021-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Molecular Biosciences |
| spelling | doaj.art-69e99ed80bab43d8a9dcde0c4a6b4b3c2022-12-21T22:28:18ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2021-07-01810.3389/fmolb.2021.661222661222Pathology Quality Control for Multiplex Immunofluorescence and Image Analysis Assessment in Longitudinal StudiesRossana LazcanoFrank RojasCaddie LaberianoSharia HernandezEdwin Roger ParraImmune profiling of formalin-fixed, paraffin-embedded tissues using multiplex immunofluorescence (mIF) staining and image analysis methodology allows for the study of several biomarkers on a single slide. The pathology quality control (PQC) for tumor tissue immune profiling using digital image analysis of core needle biopsies is an important step in any laboratory to avoid wasting time and materials. Although there are currently no established inclusion and exclusion criteria for samples used in this type of assay, a PQC is necessary to achieve accurate and reproducible data. We retrospectively reviewed PQC data from hematoxylin and eosin (H&E) slides and from mIF image analysis samples obtained during 2019. We reviewed a total of 931 reports from core needle biopsy samples; 123 (13.21%) were excluded during the mIF PQC. The most common causes of exclusion were the absence of malignant cells or fewer than 100 malignant cells in the entire section (n = 42, 34.15%), tissue size smaller than 4 × 1 mm (n = 16, 13.01%), fibrotic tissue without inflammatory cells (n = 12, 9.76%), and necrotic tissue (n = 11, 8.94%). Baseline excluded samples had more fibrosis (90 vs 10%) and less necrosis (5 vs 90%) compared with post-treatment excluded samples. The most common excluded organ site of the biopsy was the liver (n = 19, 15.45%), followed by soft tissue (n = 17, 13.82%) and the abdominal region (n = 15, 12.20%). We showed that the PQC is an important step for image analysis and that the absence of malignant cells is the most limiting sample characteristic for mIF image analysis. We also discuss other challenges that pathologists need to consider to report reliable and reproducible image analysis data.https://www.frontiersin.org/articles/10.3389/fmolb.2021.661222/fulldigital image analysisbiopsyquality controlpathologymultiplex immunofluorescence |
| spellingShingle | Rossana Lazcano Frank Rojas Caddie Laberiano Sharia Hernandez Edwin Roger Parra Pathology Quality Control for Multiplex Immunofluorescence and Image Analysis Assessment in Longitudinal Studies Frontiers in Molecular Biosciences digital image analysis biopsy quality control pathology multiplex immunofluorescence |
| title | Pathology Quality Control for Multiplex Immunofluorescence and Image Analysis Assessment in Longitudinal Studies |
| title_full | Pathology Quality Control for Multiplex Immunofluorescence and Image Analysis Assessment in Longitudinal Studies |
| title_fullStr | Pathology Quality Control for Multiplex Immunofluorescence and Image Analysis Assessment in Longitudinal Studies |
| title_full_unstemmed | Pathology Quality Control for Multiplex Immunofluorescence and Image Analysis Assessment in Longitudinal Studies |
| title_short | Pathology Quality Control for Multiplex Immunofluorescence and Image Analysis Assessment in Longitudinal Studies |
| title_sort | pathology quality control for multiplex immunofluorescence and image analysis assessment in longitudinal studies |
| topic | digital image analysis biopsy quality control pathology multiplex immunofluorescence |
| url | https://www.frontiersin.org/articles/10.3389/fmolb.2021.661222/full |
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