Abstract S4-6: An international Ki67 reproducibility study
Notice bibliographique
Résumé
Abstract Introduction: In breast cancer, immunohistochemical assessment of the cell proliferation marker Ki67 is of interest for potential use in clinical management (e.g. prognosis, prediction of treatment response, monitoring the effect of neoadjuvant therapy). However, lack of consistency across labs has limited the value of Ki67. A working group was assembled to devise a strategy to harmonize Ki67 analysis and scoring and to identify procedures to improve concordance (Dowsett et al. JNCI,'11). As a result, we have conducted an international Ki67 reproducibility study. Methods: Step 1: 100 breast cancer cases arranged into 1 mm core tissue microarrays (TMAs) were centrally stained using MIB-1 antibody. Eight labs scored Ki67 as the percentage of positively stained invasive tumor cells using their own local method. Six labs repeated scoring of 50 cases on the same TMA 3 times. Ki67 data were log2-transformed to approximate a normal distribution. Pairwise intra-lab and inter-lab concordance was displayed using Bland-Altman plots. Sources of variation (e.g. patient and lab) were analyzed using two-way crossed random effects models with quantification of reproducibility via intraclass correlation coefficient (ICC; range of 0–1, 1 = highest agreement). Step 2: A web-based scoring calibration interface was created to mitigate systematic differences in Ki67 interpretation observed in Step 1. Digital images of TMA cores from 9 “training” and 9 “test” cases representing a range of Ki67 values were assembled onto the web-based interface. An initial set of observers yielding consistent scores using a standardized scoring method served as reference labs. Detailed instructions for the standardized method were provided to 8 other labs, who were asked to score the cases and learn from discrepancies observed via the web tool. Statistical criteria for passing were pre-specified. Results: In Step 1, intra-lab reproducibility was high (ICC = 0.94). Inter-lab reproducibility was only moderate (ICC = 0.71). Absolute mean Ki67 values across the series ranged from 7% to 24%. Contributing to inter-lab discordance were tumor region selection, formal counting of nuclei versus visual estimation, and subjective assessment of staining positivity. Labs using formal counting methods gave more consistent results than those using visual estimation. The calibration exercise (Step 2) helped to improve consistency: all 8 labs passed the training exercise, 4 at first attempt and 4 after 1–2 rounds of retraining. When the same 8 labs scored the 9 test calibration cases, 3 passed on first attempt. Although overall concordance improved, discrepancies for cases with low Ki67 were largely responsible for the failure of the remaining 5 labs to pass the testing. Conclusions: Absolute values and cutpoints for Ki67, and associated clinical decisions, cannot be transferred between laboratories without careful standardization of scoring methodology. Our calibration training tool offers an initial process to improve inter-lab concordance, but further studies are required to establish what concordance can be achieved in practice. A large subsequent study will assess whether a pre-specified target of success (ICC = 0.9) can be achieved on glass slides after training with the web-based calibration tools developed as part of this study Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr S4-6.
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 | 0,049 | 0,047 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
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 ».