Abstract A13: Quantitative pathology toolbox: Improvement in prediction of progression risk for oral premalignant lesions using both interactive and automated image analysis
Notice bibliographique
Résumé
Abstract For the promise of Quantitative Pathology (QP) to be clinically accepted it must provide new information that is clinically usable (and hence actionable). This must be accomplished in a fashion that is not onerous (expensive/time-consuming/require specialized training) to the pathologist relative to the value of the information provided. As an example a major barrier to oral cancer prevention is the inability to predict progression risk for oral premalignant lesions by conventional pathology alone that can be addressed by QP. We present two approaches for the quantitative analysis of FFPE sectioned oral tissue. In one approach the user selects the area of epithelium to be analyzed and visually filters the cells to be analyzed, the other is an automated approach in which the user only circles the epithelium to be analyzed. In both approaches the cell nuclei are automatically segmented, 110 features per nuclei calculated and used to determine how normal or cancer like the nucleus is (and in the fully automated approach if the objects are single intact nucleus or not), then the distribution of nuclei values within the area of interest is used to generate a Quantitative Pathology Scores (QPS) for the tissue. These tissue measures were used alone or in combination with other markers to perform risk assessment in patients from a very large oral cancer prediction longitudinal study. Also the scores can be combined with other risk markers such as Loss of Heterozygosity (LOH) analysis to improve risk stratification. A combination of LOH based predictors and QPS thresholds were trained to refine three previously validated LOH defined- risk groups. The combined model defined a low, a medium and a high risk of progression to cancer categories. For the 104 low risk cases so classified, 98.1% do not progress to cancer (used to define a relative risk [RR] of 1). In contrast, 15% of the 106 classified medium risk cases (RR= 7.85) and 65% of the 26 high-risk cases (RR = 34) progress. This is a substantial improvement over just the LOH based classification and significantly better than dysplasia grade for risk prediction. In a validation set of 43 mild to moderate dysplasia cases with long term follow-up, 100% of the 23 cases classified as low risk by the combined algorithm did not progress, 43% of the 7 cases classified as medium risk by the combined algorithm progressed and 92.3% (12 out 13) of the cases classified as high risk by the combined algorithm progressed. These validation results strongly support the combination of these approaches for facilitating risk prediction and improving patient management. This combined risk model is also a suitable intermediate endpoint biomarker of transformation risk for oral tissue and is being used in multicenter (8) Canadian Optically guides Oral Cancer Surgical Trial as part of the quantitative evaluation of surgical margin tissue. This work was funded by the NIDCR, NIH and by the TFRI. Note: This abstract was withdrawn after the Proceedings were printed and, therefore, was not presented at the conference. Citation Format: Calum MacAulay, Miriam Rosin, Lewei Zhang, Catherine Poh, Michele Williams, Martial Guillaud. Quantitative pathology toolbox: Improvement in prediction of progression risk for oral premalignant lesions using both interactive and automated image analysis. [abstract]. In: Proceedings of the Thirteenth Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2014 Sep 27-Oct 1; New Orleans, LA. Philadelphia (PA): AACR; Can Prev Res 2015;8(10 Suppl): Abstract nr A13.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| 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) | 0,000 | 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 tête enseignante, 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 ».