Abstract 2539: DNA content and chromatin texture measurement to predict malignant transformation in low-grade oral dysplasia
Notice bibliographique
Résumé
Abstract Objectives: The key to reducing oral cancer morbidity and mortality is the early identification of oral potentially malignant lesions (OPMLs) at high risk of malignant transformation (MT). The gold standard to assess OPML risk is the grade of dysplasia. Risk prediction of low-grade dysplasias (LGDs) is challenging as only a small proportion progress. Currently no tools or biomarkers are used clinically to predict MT. Abnormal DNA content has shown to be a marker of malignancy in various sites including the oral cavity. Changes in nuclear morphology and chromatin texture have also been observed in cells with normal DNA content adjacent to cancerous fields as a result of genetic or epigenetic alterations that drive MT. The aim of this study is to use OPML lesion brushings to measure nuclear DNA content and morphometric features to identify oral LGDs at high risk of MT. Methods: Patients with oral LGDs consented into the British Columbia Oral Cancer Prediction Longitudinal Study were included in the study. Inclusion criteria included patients with mild or moderate dysplasia with lesion brushings prior to or concurrent to biopsy dates and had more than 6 months of follow up. Outcome was defined as progression to severe dysplasia or cancer. Thin monolayer slides were prepared and stained using Feulgen Thionin. Slides were scanned using the Cancer Imaging Scanner at BC Cancer using machine learning algorithms to measure DNA amount and over 120 nuclear morphometric features. Linear discriminant function (LDF) analysis of nuclear features from normal diploid cells were used to train classifiers to differentiate progressing from non-progressing lesions. Results: A total of 143 lesion brushings from 140 patients were selected. Forty-nine mild, 48 moderate, and 46 lesions with a previous history of dysplasia at the same site were included of which 16 (11%) progressed. DNA content of each nucleus was measured by a normalized scale known as DNA Index (DI) and were classified into groups: diploid (0.9 2.5). Abnormal DNA content threshold was determined using combinations of cell frequencies/percentage of each group. A lesion was considered at high-risk of MT when it showed 5 or more non-diploid cells (DI>1.2). 12 out of 16 progressors and 41 out of 116 non-progressors showed high-risk DNA content. Preliminary results from LDF show that chromatin texture features best differentiated progressing cells from non-progressing. Analysis to determine the best threshold for chromatin texture features is ongoing. Conclusion: This suggests that chromatin texture changes can be observed in cells with normal DNA content during MT. Together, they can serve as a quick, non-invasive, cost effective tool to triage high risk OPMLs to cancer care centres for monitoring and early intervention. A larger sample size is required to validate our results. Citation Format: Madhurima Datta, Denise M. Laronde, Miriam Rosin, Anita Carraro, Korbelik Jagoda, Alan Harrison, Zhaoyang Chen, Martial Guillaud. DNA content and chromatin texture measurement to predict malignant transformation in low-grade oral dysplasia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2539.
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) | 0,002 | 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 ».