Abstract 2901: Visualization and delineation of high-risk fields in the oral cavity
Notice bibliographique
Résumé
Abstract Oral cancer is a substantial, though often unrecognized issue globally, with close to 300,000 new cases reported annually. The disease represents a management conundrum: this is a cancer site that is easily examined; yet more that 40% of oral cancers are diagnosed at a late stage when the chance of death is high and treatment can be disfiguring and devastating. Visualization of high-risk fields can be improved by application of contrast agents, such as toluidine blue (TB) or through use of devices that measure alteration to tissue optics, such as fluorescence visualization (FV) both of which could facilitate assessment of abnormalities. This study's objective was to evaluate FV, within a high-risk clinic, to look for associations between loss of autofluorescence (FVL) and alterations to clinical, histological and molecular features and to determine its ability to detect high-risk oral premalignant fields and cancer. Methods: The study involved 170 patients, with 192 oral lesions (64 cancers, 28 severe dysplasia, 66 low-grade (mild/moderate) dysplasia and 34 nondysplasia), being followed in the ongoing Oral Cancer Prediction Longitudinal Study. Four categories of data were collected: 1) demographic and habit information (age, gender, ethnicity and tobacco habits); 2) lesion histology; 3) clinicopathological features at time of biopsy (lesion size, site, appearance, toluidine blue (TB) staining and FV status); and 4) molecular risk patterns of the lesions (loss of heterozygosity, LOH). Results: Demographics and smoking habit were not associated with FV status. Clinicopathological features of the lesion, appearance (P<0.001) and presence at a high-risk site (P=0.018) were significantly associated with FVL. FV status was strongly associated with severity of histology (P<0.001) with 96% of severe dysplasia, and 97% cancer displaying FVL. FVL lesions showed a significantly higher frequency of loss at 3 molecular risk sites, 3p14 (P=0.050), 9p21 (P=0.021), and 17p11-13 (P=0.05), as well as loss at 2 or more arms (P=0.036). Within low-grade dysplasia and nondysplasias, FV status was not associated with clinical features and LOH at 3p, 9p and 17p (although there was a nonspecific trend, P =0.076, 0.054. 0.077, respectively) but was associated with the presence of LOH on multiple (>2) arms (P=0.009). TB positivity was found to be highly associated with FVL (P<0.001). To date, 7 premalignant lesions have progressed to a high-grade lesion or SCC and all were FVL at time of low-grade dysplasia biopsy (FVL and progression, P=0.047). Six of the 7 progressing lesions were TB+. Conclusion: FV was found to be a very useful adjunctive tool when used by experienced clinicians in high-risk clinics. Integrating TB and FV results may aid in the detection of low-grade lesions at risk of progression. Further study of FV in low-grade and nondysplasia with a larger sample size is required. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2901.
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 | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 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) | 0,003 | 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 ».