Abstract 3381: Standardizing the analysis of Ki-67 immunohistochemical assays
Notice bibliographique
Résumé
Abstract Intro Immunohistochemical assays of the proliferation marker, Ki-67, have been associated with poorer clinical prognosis in breast cancer. However, a high degree of inconsistency in scores has been demonstrated in inter-laboratory and intra-laboratory Ki-67 positivity scorings, this has limited its potential in clinical practice. In this study, we aim to find a more consistent method for scoring Ki-67 positivity among malignant breast tumours. Methods Six Tissue Microarray (TMA) blocks were sectioned and immunohistochemistry was performed with Anti-Ki-67 antibody. Slides were then evaluated and Ki-67 positive cells in invasive breast carcinoma were scored as a percent positivity manually by a trained analyst with random sample quality assurance (QA) by trained pathologists. This was used as the standard benchmark for the experiment as it has been correlated successfully with clinical outcome. Successively, the same six slides were then annotated on Aperio ePathology software by two observers with different levels of pathology training and experience. The annotated regions were analyzed for Ki-67 positivity with Aperio ePathology software on UHN BioBank servers. The computer analyzed scores were compared to the manual benchmark scores. Results The difference between computer-analyzed and manual-scores were relatively large, Observer-A-annotated-computer-analyzed vs. analyst-manual-scores had a difference of 4.23% to 16.96%, while Observer-B-annotated-computer-analyzed vs. analyst-manual-scores had a difference of 7.13% to 15.03%. Interestingly, Observer-A-annotated-computer-analyzed vs. Observer-B-annotated-computer-analyzed scores only had a difference of 0.49% to 2.91%. Pearson Correlation was calculated for all samples on a case-by-case basis and we found there to be a linear correlation of 0.564, with a P-value of 3.6082×10-8, between the computer scores and the manual scores; suggesting significant correlation between the computer scores and the manual score. Conclusion A significant linear correlation has been observed between the computer score and the manual score. However, while the data does not seem to support the idea that a semi-automatic method of computer scoring will replace analyst manual scoring, most of the large contributing variables have been identified. We plan in the next steps of the project to continue to decrease the effects of such variables. It is interesting that the inter-observer computer score displayed a minimal amount of difference, again with the variables identified. This could signify a more consistent method of Ki-67 scoring. Further experiments will be conducted to continue to reduce the variables and optimize the system to gain similar performance as manual scoring. Hopefully in the near future, computerized immunohistochemical analysis can replace the tedious task of manual scoring. Citation Format: Tian Yu Liu, Trillium Chang, Adewunmi Adeoye, Willa Shi, Sheng-Ben Liang, Dianne Chadwick, Michael H.A. Roehrl, Naomi Miller, Fei-Fei Liu, Susan J. Done. Standardizing the analysis of Ki-67 immunohistochemical assays. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3381. doi:10.1158/1538-7445.AM2015-3381
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 | 0,039 | 0,059 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| 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 ».