Advancing Breast Cancer HER2 FISH Quality by Image Analysis.
Notice bibliographique
Résumé
Abstract Background: A critically important factor in the correlation of Her2 amplification and clinical outcome in breast cancer is the quality of histologic Her2 amplification detection. FISH is the “gold standard” for HER2 amplification detection but presently most labs use manual methods which are labour intensive and restricted by analysis time to small samples (60 cells or less if amplification status appeared clear to the observer).An ideal FISH detection system should be rapid (less than 30 minutes per sample), identify HER2/neu and CEP17 copy number objectively in each cell, count sufficient cells to be representative statistically, count Her2/neu in cell clusters objectively, and have a permanent record of each cell counted. Our objective is to develop an image analysis system for Her2/neu FISH that is superior to current manual and automated methods in all the above criteria.Materials and Methods: The Visiopharm Integrator System software and a Leica DM6000B fluorescence microscope equipped with a Prior 8-slide capacity motorized stage (Mac 5000 ps system stage control) and Hamamatsu camera (Model CA4742-80-12AG) were used for image analysis. Forty breast cancer biopsies, 15 core biopsies included, were examined for Her2 FISH previously assessed manually on the same slide. For manual detection, at least 3 representative fields were selected by the observer. For image analysis, unbiased tumour sampling, typically 16 fields, was assessed within a region of interest previously identified. Methods were compared by ASCO/CAP amplification criteria and by assessment of technical time, cells counted and objectivity of counting criteria.Results:Comparison manual vs image analysisMethodManualImage AnalysisTechnical time: minutes, average, range50 (45-135)25 (20-40)Cells counted: average, range58 (20-217)238 (18-1151)HER2 in clusterssubjectiveobjective and reproducibleASCO/CAP amplified/equivocal19/217/3ASCO/CAP not amplified/uninterpretable18/120/0 Seven discordant cases were observed. Five cases were downgraded by image analysis. Two cases, one uninterpretable manually and one seen as not amplified manually, were seen as amplified by image analysis.Discussion: Image analysis FISH with Visiopharm software allows for establishment of finite cell inclusion criteria reflecting size, circularity and other measurable cell features. Image analysis facilitates higher cell counts without observer selection bias in less time, and with smaller increases in technical time as more cells are counted. Discordance may be attributable to heterogeneity with larger sample of cells assessed and objective assessment of clusters in the image analysis method. Our image analysis protocol demonstrated successfully the quantification of HER2 in separate signals, in clusters and in split signals. HER2/CEP17 copy numbers were determined for each cell and for more cells in much less time while providing a permanent image record of all cells assessed.Image analysis has promise to improve substantially the quality of Her2 FISH assessment in breast cancer biopsies.Supported in part by an unrestricted grant from Hoffman-LaRoche Ltd. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 6015.
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 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| 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) | 0,009 | 0,003 |
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 ».