Abstract PO-040: Comparing digital image analysis with a manual scoring approach for quantification of p63 and BRCA1 protein expression in oropharyngeal squamous cell carcinoma
Notice bibliographique
Résumé
Abstract In oropharyngeal squamous cell carcinomas (OPSCC) there is overexpression of p63. We previously identified a link between p63 and BRCA1 in OPSCC, both at a gene and protein level. However, interpretation of biomarkers based on protein expression identified using immunohistochemistry can be subject to interobserver variability. We aimed to explore the option of digital image analysis as an alternative approach to immunohistochemical protein quantification. Herein, we compare manual histological interpretation of p63 and BRCA1 expression in the context of HPV status with digital image analysis. Representative samples of a clinically annotated cohort of OPSCC (n=191) were arranged in triplicate tissue microarrays (TMA) and stained immunohistochemically for p63 (p63NCL and p63DN) and BRCA1 (D-9 and Ab1). RNA in situ hybridization for a cocktail of 18 high-risk HPV genotypes (HR-18 HPV) was used to determine HPV status. Cases were scored manually using at least two independent scorers to generate consensus Q scores. Each TMA slide was digitally scanned. These images were imported into QuPath version 0.2.3, which was used to quantify the expression of each biomarker and generate digital H scores. Statistical analysis was conducted using R version 4.2.3 and GraphPad Prism5. Spearman’s Rank correlation was used to compare the digital and manual scores for each biomarker. Kaplan-Meier curves and log rank tests were used for survival analysis. Preliminary analysis showed a strong correlation between manual scoring methods and digital scores for each of the four biomarkers: p63NCL (rs=0.77, p<0.0001); p63DN (rs=0.72, p<0.0001); BRCA D9 (rs=0.75, p<0.0001) and BRCA AB1 (rs=0.84, p<0.0001). We identified a subgroup of HPV negative patients with low p63NCL expression and high BRCA D9 expression that had poor 5-year overall survival (p<0.0001). The process of digital quantification is more labour intensive than one might anticipate. It requires significant pre-processing to remove artefacts prior to obtaining a computer-generated H score. In contrast manual Q scoring allows immediate assessment. In addition, the BRCA D-9 antibody had non-specific staining, highlighting darkly staining “dendritic-like” cells which complicated digital assessment. The results show the effectiveness and reproducibility of digital pathology scoring methods for antibody quantification when compared with traditional manual scoring methods. However manual scoring methods presently used by pathologists are significantly more time efficient when compared to newer digital techniques. There is a need for improved or automated pre-processing techniques to remove some of the pitfalls associated with this approach for future studies validating use of combined low p63NCL expression and high BRCA D9 expression to predict outcome in patients with OPSCC. Citation Format: Laura Graham, Stephanie Craig, Kris McCombe, Stephen McQuaid, Simon McDade, Jacqueline James. Comparing digital image analysis with a manual scoring approach for quantification of p63 and BRCA1 protein expression in oropharyngeal squamous cell carcinoma [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-040.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».