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Enregistrement W2334150511 · doi:10.1158/1940-6207.prev-12-cn02-02

Abstract CN02-02: Malignant risk prediction for patients with oral premalignant lesions

2012· article· en· W2334150511 sur OpenAlexaffabout
Miriam P. Rosin, Lewei Zhang, Catherine F. Poh, Michele Williams, Denise M. Laronde, Ken Berean, Pamela J. Gardner, Huijun Jiang, Lang Wu, J. Jack Lee

Notice bibliographique

RevueCancer Prevention Research · 2012
Typearticle
Langueen
DomaineDentistry
ThématiqueOral Health Pathology and Treatment
Établissements canadiensBC Cancer Agency
Organismes subventionnairesnon disponible
Mots-clésMedicineCancerDiseaseDysplasiaTriageEpithelial dysplasiaIntensive care medicineFamily medicinePathologyInternal medicineMedical emergency

Résumé

récupéré en direct d'OpenAlex

Abstract Oral cancers remain both a challenge and an opportunity for clinicians and scientists working with this disease. Worldwide, it represents a significant global challenge, with close to 300,000 new cases diagnosed each year. The disease is potentially preventable: we have knowledge of its risk factors; it is also at an easily accessible site and is often preceded by oral premalignant lesions (OPLs). The challenge has been to develop a framework that would let us both detect and better manage such lesions. Unfortunately, only a small proportion of OPLs will progress to a cancer and histology alone has not allowed clinicians to differentiate high- from low-risk lesions. This uncertainty has also meant that, even when OPLs are detected, there is no consensus on who to treat and how. In 1999, we established the Oral Cancer Prevention Program in British Columbia to develop an integrated knowledge translation approach to facilitate systematic change for prevention, detection and management of this disease across the continuum of care throughout the province. This effort has involved optimization of screening in community dental practices, creation of a triage pathway from a centralized oral biopsy service to oral dysplasia clinics where patients are assessed, and formation of linkages to the cancer agency and local hospitals to facilitate both treatment and follow-up. A new Canadian Partnership Against Cancer-funded Oral Dysplasia Surveillance System has been developed in BC to monitor natural history of the disease. This province-wide structure has facilitated the formation of the Oral Cancer Prediction Longitudinal (OCPL) study, a unique resource for development and validation of biomarkers of risk of malignant progression. To date, ∼ 450 low-grade (mild and moderate) dysplasia cases have been accrued to this study and are in follow-up. In September of this year, we published results from the first 296 patients accrued to the OCPL study (Zhang et al., Cancer Prevention Research). We focused on the validation of a LOH risk model proposed by our group in 2000 that utilized a set of microsatellite markers at key chromosomal loci to predict progression of low-grade dysplasia in a retrospective cohort (Rosin et al., Clin Cancer Res 2000; 6:357–62). In the new study, cases were classified into high- or low-risk profiles to validate the 2000 model. Risk models were further refined using recursive partitioning and Cox regression analyses. The study showed that the high-risk lesions (3p &/or 9p LOH) had a 22•6 -fold increase in risk (P = 0•002) compared to low-risk lesions (3p & 9p retention). Addition of another two markers (loci on 4q/17p) further improved the risk prediction, with five-year progression rates of 3•1%, 16•3%, and 63•1% for the low-, intermediate-, and high-risk lesions, respectively. Compared to the low-risk group, intermediate- and high-risk groups had 11•6-fold and 52•1-fold increase in risk (P < 0•001). The new LOH profiles in the refined model were validated as risk predictors by using the initial retrospective cohort from 2000. Multi-covariate analysis with clinical features showed LOH models to be the most significant predictors of progression. The importance of this study is that it has direct implication for standard of care in the community setting, providing a tool by which patients could be triaged to different levels of intervention. Patients with elevated risk would be guided towards increased surveillance with this risk providing a rationale for the targeting of intervention regimes, even if these were associated with some morbidity. Two of three patients with such high-risk profiles progressed to cancer in 5 years. In contrast, patients with low-risk profiles could be spared in from aggressive monitoring and intervention. In our study, low-risk patients represented close to half of those individuals that were accrued to the study. Future research should be aimed towards exploring such possibilities, as an initial step towards personalizing oral cancer prevention strategies. LOH is currently being used in two chemoprevention trials of patients with OPLs to guide patient accrual: the phase III Erlotinib Prevention of Oral Cancer study and the Phase II Cetuximab for Treatment of High-Risk Pre- Malignant Upper Aerodigestive Lesions trial. The results of these studies should provide even greater knowledge upon which to build new strategies for patient triage that could better focus risk reduction programs for this disease. (Supported by grants from the NIH and the National Institute of Dental and Craniofacial Research (R01DE13124 and R01DE17013). Citation Format: Miriam P. Rosin, Lewei Zhang, Catherine Poh, Michele Williams, Denise M. Laronde, Ken Berean, Pamela J. Gardner, Huijun Jiang, Lang Wu, J. Jack Lee. Malignant risk prediction for patients with oral premalignant lesions. [abstract]. In: Proceedings of the Eleventh Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2012 Oct 16-19; Anaheim, CA. Philadelphia (PA): AACR; Cancer Prev Res 2012;5(11 Suppl):Abstract nr CN02-02.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,038
Score d'incertitude au seuil0,927

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,130
Tête enseignante GPT0,458
Écart entre enseignants0,328 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2012
Routes d'admission2
Résumé présentoui

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