52 Digital pathology training effectiveness for the evaluation of PD-L1 expression in multiple tumor indications
Notice bibliographique
Résumé
<h3>Background</h3> In-person pathologist trainings during the COVID-19 pandemic became impossible, necessitating a shift to remote-digital whole slide image (WSI) training. High concordance between WSI and glass slide scores from the same specimens stained with PD-L1 IHC 22C3 pharmDx (SK006) across multiple tumor indications supported the validity of digital training.<sup>1</sup> However, in-person microscope (glass-slide) training versus remote-digital (WSI) training effectiveness must be assessed. Collated testing data on specimens (SK006 stained) spanning multiple indications scored by external pathologists during Agilent led training and testing (T&T) sessions via glass slides were compared to sessions utilizing WSIs. <h3>Methods</h3> Stained slides (30 unique specimens per tumor indication) were scanned on an Aperio AT2 scanner to generate WSIs for digital T&T. Remote T&T sessions used WebEx and PathcoreScholar’s online platform to discuss scoring guidelines and WSI training cases. Subsequently, external pathologists evaluated WSIs in PathcoreScholar for PD-L1 expression using either Tumor Proportion Score (TPS) or Combined Positive Score (CPS) scoring algorithms and interpreted these scores at predefined cutoffs (figure 1). In both glass and WSI scoring test modalities, passing is defined as inter and intra-observer overall agreement (OA) ≥85%. Training effectiveness pass rates from glass slide data (2018–2020) and WSI data (2021–2022) spanning multiple indications and scoring algorithms were calculated and then compared using the Fisher-Freeman-Halton test, with a significance threshold of 0.05. Only data from initial pathologist tests were included in the pass rate calculation; data from re-tests executed after initial test failure were excluded. <h3>Results</h3> The differences between pass rates for microscope (glass slide) and digital (WSI) testing were not statistically significant (p-value > 0.05) (tables 1 and 2). Testing pass rates for indications scored with TPS or CPS using microscope glass slide vs digital WSI T&T was not statistically significant (p-value > 0.05) (table 3). <h3>Conclusions</h3> No statistically significant differences in pathologist training effectiveness for PD-L1 were observed between remote and in-person trainings across multiple tumor indications, scoring algorithms, and cutoffs. These results demonstrate the effectiveness and equivalency of remote-digital pathologist trainings for evaluation of PD-L1 expression as detected by PD-L1 IHC 22C3 pharmDx in multiple tumor indications when compared to in-person-microscope glass slide T&T. Use of digital training and scoring proficiency testing can provide pathologists around the world with access to high-quality, interactive training from leading experts in PD-L1 expression evaluation. <h3>Acknowledgements</h3> We would like to thank our colleagues at Agilent Technologies, Inc. and all the pathologists who completed Agilent scoring certification training and testing for their valuable contributions to this study. 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. 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), US Biolab (Gaithersburg, MD, USA), Contract Research Ltd (Charlestown, Nevis), Centre Hospitalier Universitaire (CHU) de Nice (Nice, France), IOM Ricera (Viagrande, Italy), National BioService LLC (Saint Petersburg, Russia), SageBio LLC (Sharon, MA, USA, Tumorothèque Régionale de Franche-Comté (Besançon, France), Centre Antoine Lacassagne (CAL; Nice, France, GLAS (Winston-Salem, NC, USA), Maine Medical, Hospices Civils de Lyon (Lyon, France), Sofia Bio LLC (New York, NY, USA), SELARL DIAG (Nice, France), and Clin-Path Diagnostics (Tempe, AZ, USA) with appropriate ethics approval and through Azenta Life Sciences. 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. <h3>Reference</h3> Adams M, Moquin D, Littrell J, et al 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. <i>Journal for ImmunoTherapy of Cancer</i> 2021;<b>9</b>: doi: 10.1136/jitc-2021-SITC2021.036.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».