Abstract PR-06: Analysis of pathologists’ intraobserver, interobserver and AI agreement in breast cancer HER2 scoring: AI-assessed intra-sample tumor heterogeneity relates to lower agreement among pathologists and with AI
Notice bibliographique
Résumé
Abstract Objective: This study aimed to evaluate the intra- and interobserver variability among pathologists in the assessment of HER2 immunohistochemistry (IHC) in breast cancer samples and to compare their performance with that of an artificial intelligence (AI) algorithm. We sought to measure the extent of the disagreements between observers and AI and explore potential underlying causes for variability. Methods: Thirty-four pathologists, including generalists and breast specialists, from the pathology department of a multicenter Brazilian hospital network were asked to assess 176 digital HER2 IHC samples in three testing sessions. The study design included an initial 10-image survey, a standardization workshop, and two subsequent 100-image surveys separated by one month. Repeated samples were included to allow for intraobserver variability analysis. A subset of 100 cases was also analyzed by an AI model that provided an overall HER2 score (0, 1+, 2+, or 3+) and a spatial breakdown of HER2 categories across the tumor area. Results: Four pathologists did not complete the final survey. Their data were excluded from intraobserver analysis but included in interobserver and AI comparisons. Median intraobserver concordance among pathologists was 67.68%, and median concordance with AI was 60.80%. Median interobserver agreement, using one response per pathologist, was 67.65% per sample. Median agreement with AI, using all responses, was 59.38% per sample. Out of 100 samples assessed by AI, 22 were scored as 0, 45 as 1+, 25 as 2+, and 8 as 3+. Significant positive correlations were found between interobserver and AI agreement (r = 0.86, p < 2.2×10-16), intra- and interobserver agreement (r = 0.74, p = 1.19×10-15), and intraobserver and AI agreement (r = 0.68, p = 1.47×10-11), indicating that more consistently scored samples had higher concordance across all metrics. Samples in which AI identified a higher proportion of the tumor area as matching the overall HER2 score (e.g., 90% of the tumor was 1+ and the final score was 1+) showed significantly higher interobserver (r = 0.65, p = 4.11x10-13) and AI agreement (r = 0.58, p = 2.57×10-10). In contrast, neither total tumor area nor absolute area matching the overall score showed correlation with variability metrics. This suggests that lower tumor heterogeneity within a sample, regardless of the tumor area assessed, is associated with more reproducible scoring. Conclusions: HER2 IHC interpretation remains subject to significant variability, even among experienced pathologists. Our findings highlight that tumor heterogeneity within a sample can challenge assessment reproducibility and suggest that AI can assist in identifying and evaluating challenging cases. While AI has the potential to improve reliability, overreliance on these tools may distance pathologists from critical reflection. Our results support the notion that proper integration of AI models into diagnostic workflows can enhance consistency in IHC scoring while helping pathologists recognize and address sources of variability in their assessments. Citation Format: Pedro S. S. M. Ferrari, Mariana P. Macedo, Isabela W. Cunha, Fernando A. Soares. Analysis of pathologists’ intraobserver, interobserver and AI agreement in breast cancer HER2 scoring: AI-assessed intra-sample tumor heterogeneity relates to lower agreement among pathologists and with AI [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr PR-06.
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 | 0,029 | 0,077 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 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 | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».