Abstract 3677: Prediction of oral cancer recurrence at post-surgery follow-up using fluorescence visualization
Notice bibliographique
Résumé
Abstract Background: Recurrence is observed in up to 30% of surgically treated high-risk oral lesions (HRLs, severe dysplasia, carcinoma in situ and cancer) and is associated with poor prognosis. Reactive tissue change observed post-treatment often masks recurrence and makes early detection difficult. New advances using fluorescence visualization (FV) represent a promising approach to this problem that may facilitate early detection of disease recurrence. Objectives: 1) To identify FV alterations post-treatment of HRLs and 2) To determine whether a relationship exists between FV alterations and local recurrence. Methods: In the BC Oral Cancer Longitudinal Study, we have recruited ∼400 HRLs with surgical treatment as the primary modality. Patients eligible for this analysis included those that had an initial follow-up appointment within 6 months of surgery with at least 2 follow-up appointments within the first year of treatment, with each visit involving FV examination of the treatment site. Recurrence was defined as the presence of biopsy-proven HRLs. The ‘plateau’ of the FV during the follow-ups is defined as the change of FV measurement in width within ± 1 mm (superior-inferiorly) in various time intervals of 3, 6, 9, or 12 months. Results: A total of 198 patients were identified of which 24 (12%) had lesion recurrence at the previously treated site. There was no difference in gender, age, ethnicity, smoking habit, anatomical site, primary diagnoses, and follow-up time between the recurrence and non-recurrence groups. The duration of plateau was longer in non-recurrence group compared to those in recurrence group (P = 0.03). When we examined the duration ‘plateau’ at various intervals of follow-ups, among 166 patients/lesions with at least 9 months follow-ups, we found out that the presence of plateau was more frequent in the non-recurrence group than those recurrent case (P = 0.03). Using linear mixed effects and logistic regression analyses, there was a significant difference of the individual slopes between recurrence and non-recurrence group (P = 0.001), adjusted for the individual intercepts (i.e., the original FV width). Conclusion: The stability, i.e., timing and duration of the plateau, of the FV alteration during post-surgical follow-ups can be potentially used to predict local recurrence of HRLs. (Supported by Supported by grant R01 DE17013 from the National Institute of Dental and Craniofacial Research and grant CCSRI-20336 from Canadian Cancer Society Research Institute; Canadian Institute for Health Research and Michael Smith Foundation from Health Research) Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3677. doi:10.1158/1538-7445.AM2011-3677
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| 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 | 0,001 |
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 ».