Abstract 34: Predictors of discordance between image-based and manual scoring of immunohistochemical stains in ovarian cancer tissue microarrays
Notice bibliographique
Résumé
Abstract Digital image-based scoring of immunohistochemistry (IHC) staining allows faster, more objective and potentially more reproducible quantification of area stained or cell counts compared to manual scoring. However, predictors of discrepancies between these two assessment methods are not well understood. We examined clinical and pathological characteristics associated with discordance between image-based and manual scoring using ovarian cancer tissue microarrays (TMA) across a range of IHC stains. We evaluated 2,159 TMA cores from 681 ovarian cancer cases stained by four tissue cytoplasmic stains quantified as percentage of the tumor area stained (POSTN, CXCL14, ADH1B, COL11A1) and two immune cell markers quantified by cell count of infiltrating immune cells (CD68, CD163). The TMA slides were scored manually using semi-quantitative scoring by gynecologic pathologists as well as digitally using image-based Definiens automated platform. We used generalized linear mixed models with individual patients and TMAs included as random effects and stain as fixed effect to examine predictors of discordance, defined as absolute difference of ≥1SD in z-scores between image-based and manual scoring, overall and by each stain. The multi-level model included characteristics of the TMA, core, and tumor factors as predictors. Overall, TMA construction by automated robotic system (vs manual), smaller core size or area, borderline (vs invasive tumors), and having no geographic necrosis in the tumor were significantly associated with greater discordance between image-based and manual scoring. Interestingly, all these factors, except geographic necrosis, were differentially associated with discordance across the stains (p-interaction <0.05). Having multiple loci of geographic necrosis was associated with less discordance (OR=0.84, 95%CI=0.72-0.97). Invasive tumors compared to borderline tumors were less likely to be discordantly scored overall (OR=0.80, 95%CI=0.65-1.00) and for tissue cytoplasmic stains (OR range: 0.42-0.73), with associations in the opposite direction for immune cell markers (OR range: 1.21-2.37). Core areas in the lowest quartile vs all other cores were associated with greater discordance (OR=1.18, 95%CI=1.05-1.32), which was more notable for the tissue cytoplasmic stains (OR range: 1.18-2.00) compared to immune cell markers (OR range: 0.83-0.86). TMAs created by robot vs by hand had 64% higher odds of discordance (95%CI=1.20-2.25), which was more apparent when blank rows and columns were included on the TMA. Overall, TMA, core, and tumor level factors were related to discordance between image-based and manual scoring. Some stain types may be more susceptible to specific pre-analytic factors suggesting that reproducibility studies of manual vs image-based scoring should be conducted on a proportion of cases in large scale projects. Citation Format: Naoko Sasamoto, Mary Townsend, Farnoosh Abbas-Aghababazadeh, Kathryn L. Terry, Joseph O. Johnson, Jonathan L. Hecht, Brooke L. Fridley, Shelley S. Tworoger. Predictors of discordance between image-based and manual scoring of immunohistochemical stains in ovarian cancer tissue microarrays [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 34.
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 | 0,005 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 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 ».