Abstract 3251: Assessing risk markers for oral cancer recurrence
Notice bibliographique
Résumé
Abstract Oral cancer has a poor 5-year survival rate, partly due to a high local recurrence (REC) rate. Clinical indicators such as size, color, and site have been found to be associated with progressing primary oral premalignant lesions (OPL), but have yet to be evaluated for OPL development at treated cancer sites. Similarly, recent LOH regions highly predictive of primary progression (Zhang et al., Cancer Prevention Research 2012), have not been evaluated as predictive markers of OPLs in oral cancer REC. Methods: The analysis involved 194 patients enrolled in the Oral Cancer Prevention Longitudinal (OCPL) study with early stage (Stage I & II) squamous cell carcinoma (SCC) or carcinoma-in-situ (CIS), treated with curative intent. Data was collected every 3 months post-treatment up to 2 years, then every 6 months. Demographic (age, gender, risk habits), tumor information (site, stage, treatment) and clinical data (presence of an OPL, size, color, texture and appearance) were collected. Biopsies taken during follow-up were tested for high-risk molecular patterns. REC was defined as development of a severe dysplasia, CIS or SCC within 3 cm of the primary tumor site. Results: 31(16%) patients suffered a REC at the former tumor site. Presence of an OPL in follow-up had an almost 7-fold increased risk of REC (P<0.001). 19 (10%) patients ‘always’ had an OPL, 74 (38%) had an OPL ‘sometimes’, while 101 (52%) ‘never’ had an OPL during follow-up. 74% of the ‘always’ OPL group (RR=68, P<0.001), 18% of the ‘sometimes’ OPL group (RR=5, P<0.01) and 6% of the ‘never’ OPL group developed a REC (RR=1). Patients who developed an OPL during the first year of follow-up had the highest risk of REC (RR= 6) compared to the patients who developed an OPL in the second year of follow-up. None of the OPL clinical characteristics associated with primary progression was associated with risk of REC. Of 76 patients who had a biopsy in follow-up that was available for analysis, patients with a molecular profile showing a loss at 9p (RR=4) were more likely to develop a REC than samples with 9p retention. Patients with low-grade dysplasia at the surgical margins were more apt to develop a REC (RR=2.7). Conclusion: The presence of an OPL at a former tumor site is a critical predictor of oral cancer REC regardless of its characteristics, with nearly all cases preceded in time by its appearance. The issue is whether it is reactive or a true OPL with risk. Risk of REC increases if an OPL is present at each follow-up visit and/or is present within the first year following treatment. OPLs persisting during follow-up should be biopsied. Regardless of histology, the presence of high-risk molecular patterns in these samples offers an additional opportunity to assess risk of REC. Citation Format: Denise M. Laronde, Lewei Zhang, P. Michele Williams, Bertrand Chan, Jay H. Park, Catherine F. Poh, Miriam P. Rosin. Assessing risk markers for oral cancer recurrence. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3251. doi:10.1158/1538-7445.AM2014-3251
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 | 0,001 | 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) | 0,004 | 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 ».