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36 Digital Whole Slide Image (WSI) scoring is equivalent to microscope glass slide scoring for evaluation of programmed death-ligand 1 (PD-L1) expression across multiple tumor indications

2021· article· en· W3212494582 on OpenAlexaboutno aff
Micki Adams, Deanna Moquin, Joshua Littrell, Jay Milo, Stephanie Hund, Angéliki Apostolaki

Bibliographic record

VenueRegular and Young Investigator Award Abstracts · 2021
Typearticle
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsnot available
Fundersnot available
KeywordsConcordanceMedicineDigital pathologyDigital image analysisNuclear medicineCutoffPathologyInternal medicineComputer scienceComputer visionPhysics

Abstract

fetched live from OpenAlex

<h3>Background</h3> The COVID-19 pandemic brought a host of new challenges, including the immediate need for digital solutions addressing the lack of remote options available to pathologists in the field of immunohistochemistry (IHC)-based companion diagnostics for Programmed Death-Ligand 1 (PD-L1) expression evaluation in tumor tissues. Agilent Technologies, Inc. investigated concordance of PD-L1 expression results recorded by trained pathologists between stained glass slides and digital whole slide images (WSIs). Formalin-fixed, paraffin-embedded (FFPE) specimens of eleven tumor indications (table 1) were evaluated in this study. Specimens were stained using the qualitative IHC assay PD-L1 IHC 22C3 pharmDx on Autostainer Link 48 and scored using TPS (Tumor Proportion Score) or CPS (Combined Positive Score) algorithms at six validated cutoffs.<sup>1</sup> The objective was to demonstrate equivalency between digital WSI and microscope glass slide scoring. <h3>Methods</h3> Three Agilent-certified pathologists evaluated specimen PD-L1 expression level (positive/negative) using CPS and/or TPS at relevant cutoff(s) for each indication (table 1) using two scoring modalities for the same specimen sets: 1) light microscope, and, 2) digital monitor (WSI) with a minimum 14-day washout period between glass slide and WSI reads. WSIs were generated using Leica’s Aperio AT2 scanner and evaluated using Aperio ImageScope software (figure 1) on appropriate monitors (table 2). Concordance between specimen glass slide (reference condition) and WSI PD-L1 expression results was assessed per cutoff on pooled data from all applicable indications using negative percent agreement (NPA), positive percent agreement (PPA) and overall agreement (OA) with 95% two-sided percentile bootstrap confidence intervals (CI); the acceptance criteria for equivalency at each cutoff were set at CI lower-bounds (CILBs) ≥85%. Discordant comparisons with respect to specimen screening data generated prior to inclusion in the study were also analyzed where applicable. <h3>Results</h3> NPA/PPA/OA CILBs for the CPS ≥1, CPS ≥10, TPS ≥1%, and TPS ≥50% cutoffs were ≥85% (table 3). NPA and OA CILBs at CPS ≥20 and CPS ≥50 were ≥85%; PPA CILBs were 83.2% and 84.2%, respectively. Discordant comparisons analysis for CPS ≥20 and CPS ≥50 suggested that WSI is not more prone to discordances in PD-L1 expression level than glass slide scoring when compared to specimen screening data (tables 4 and 5). <h3>Conclusions</h3> Glass slide and WSI scoring are equivalent across multiple validated cutoffs and tumor indications tested for PD-L1 expression using PD-L1 IHC 22C3 pharmDx with CPS and/or TPS algorithms and are, thus, considered interchangeable scoring modalities. <h3>Acknowledgements</h3> &lt; i &gt;We would like to thank our colleagues at Agilent Technologies, Inc. and all of the pathologists involved in study specimen scoring for their valuable contributions to this study. Samples/tissue supplied by Conversant Biologics.Tissue samples supplied by BioIVT (Hicksville, NY, USA)The data and biospecimens used in this project were provided by Centre Hospitalier Universitaire (CHU) de Nice (Nice, France), Contract Research Ltd (Charlestown, Nevis), National BioService LLC (Saint Petersburg, Russia), Sofia Bio LLC (New York, NY, USA), US Biolab (Gaithersburg, MD, USA), Nottingham University Hospitals NHS Trust (Nottingham, UK), Gundersen Medical Foundation Center Biobank (La Crosse, WI, USA), LLC Biomedica CRO (Kyiv, Ukraine), Clinfound Clinical Research Services Pvt Ltd (Idukki, Kerala, India), SageBio LLC (Sharon, MA, USA), GLAS (Winston-Salem, NC, USA), Hospices Civils de Lyon (Lyon, France), IOM Ricera (Viagrande, Italy), Clin-Path Diagnostics (Tempe, AZ, USA), Centre Antoine Lacassagne (CAL; Nice, France), CHU de Bordeaux (Biobank ID: BB-0033–00036; Bordeaux, France) and contributions by clinical personnel from Centre de ressources biologiques, and SELARL DIAG (Nice, France) with appropriate ethics approval and through Trans-Hit Biomarkers Inc. Biological materials were provided by the Ontario Tumour Bank, which is supported by the Ontario Institute for Cancer Research (Toronto, Ontario, Canada) through funding provided by the Government of Ontario.Tissue samples were provided by the Cooperative Human Tissue Network which is funded by the National Cancer Institute. Other investigators may have received specimens from the same subjects.&lt;/i &gt; <h3>Trial Registration</h3> N/A <h3>Reference</h3> P02893/13 Instructions for Use (IFU) for PD-L1 IHC 22C3 pharmDx Human Cancer (SK00621-4) Package Insert <h3>Ethics Approval</h3> N/A <h3>Consent</h3> N/A

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.033
GPT teacher head0.341
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2021
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