Abstract P1-03-01: An international multicenter study to evaluate reproducibility of automated scoring methods for assessment of Ki67 in breast cancer
Notice bibliographique
Résumé
Abstract Background: The nuclear proliferation biomarker Ki67 has multiple potential roles in breast cancer, including prognosis-based decisions, but unacceptable between-laboratory variability has limited its clinical value. The International Ki67 Working Group (IKWG) has undertaken a systematic program to determine whether Ki67 immunohistochemistry can be analytically validated and standardized across laboratories. Technological advances and broader availability of devices for automated assessment of stained slides raise the possibility that these machines may improve on reproducibility of traditional pathologist-based visual Ki67 assessment. Aims: To characterize reproducibility of automated machine-measured Ki67 expression using slides previously analyzed in the IKWG phase 3 study that evaluated reproducibility of visual Ki67 assessment. Methods: Two sets of 30 previously stained slides containing core-cut biopsy sections of breast tumors were circulated to 14 laboratories for scanning and automated assessment of Ki67 expression. Sites were instructed to return average and maximum percentage of tumor cells positive for Ki67 for each slide, where maximum is designed to reflect “hot spot” analysis. Two laboratories returned scores from 2 operators; not all laboratories reported values for maximum Ki67 scores. Different operators were treated as distinct laboratories in analyses. Sixteen and 10 score sets were available for average and maximum Ki67 analyses, respectively, encompassing 7 unique scanner and 10 software platforms. Pre-specified analyses included evaluation of reproducibility across all laboratories as well as within a subgroup limited to those using Aperio scanners. The primary reproducibility metric was intraclass correlation coefficient between laboratories (ICC), regardless of device platform or software. Results: Geometric means across 30 cases for 16 operators ranged from 11.06% to 38.11% with overall mean 16.75% (95% CI:14.45-19.42) for average scores. Geometric means for 10 operators ranged from 16.44% to 68.73% with overall mean 25.16% (95% CI: 18.71-33.84) for maximum scores. ICC for automated average scores across 16 operators was 0.83 (95% CI: 0.73-0.91) and ICC for maximum scores across 10 operators was 0.63 (95% CI: 0.44-0.80) although one outlier lab dramatically affected results. For the laboratories using the Aperio platform (8 score sets), ICC for automated average scores was 0.89 (95% CI; 0.81-0.96). These results are similar to ICC of 0.87 (95%CI; 0.81-0.93) reported using these same slides in the Phase 3 visual assessment reproducibility study in which observers counted 500 cells per slide (Leung et al, NPJBrCancer, in press). Conclusions: Between-laboratory reproducibility for automated machine assessment of average Ki67 is similar to that for pathologist-based visual assessment of Ki67. However, the observed ICC was markedly numerically lower for the maximum score method compared to the average method, suggesting that the maximum score may not be useful as a reproducible measure of proliferation. Automated average scoring methods show promise for standardization of Ki67 scoring, supporting future studies to clinically validate Ki67. Citation Format: Rimm DL, McShane LM, Leung SCY, Bai Y, Bane AL, Bartlett JMS, Bayani J, Chang MC, Dean M, Denkert C, Enwere E, Galderisi C, Gholap A, Hugh JC, Jadhav A, Kornaga E, Laurinavicius A, Levenson R, Lima J, Miller K, Pantanowitz L, Piper T, Ruan J, Srinivasan M, Virk S, Wu Y, Yang H, Hayes DF, Nielsen TO, Dowsett M. An international multicenter study to evaluate reproducibility of automated scoring methods for assessment of Ki67 in breast cancer [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P1-03-01.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,016 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».