Abstract A10: A new in silico physical optical model (iPOM) for the study of cervical intraepithelial neoplasia
Notice bibliographique
Résumé
Abstract Background: Cervical cancer is the second leading cause of deaths in women worldwide. More than 80% of cervical cancers and deaths occur in the developing world. New optical and imaging technologies (confocal imaging, fluorescence/absorbance spectroscopy, OCT, etc…) are being developed and tested as potential keys components of a necessary global solution to this problem. The success of these new technologies requires a better understanding of the precise interactions between the incident light, the underlying biological events and the physical structure of the cervical tissue. Herein, we present a new platform based on an in silico Physical Optical Model (iPOM), which is designed to simulate, analyze, and quantify the cell-and tissue-level biological changes that are associated with cervical preneoplastic growths. By reproducing these features, iPOM also gives insights into the ways in which dysplastic changes will be detected by different optical technologies being developed for cervical cancer screening, detection, diagnosis or treatment. Methods: The physical module of this platform is based on a database of more than 4000 cervical histological intraepithelial neoplastic specimens, collected from about 1800 women. As part of previous multicenters clinical trials (NIH funded Po1 project), Feulgen-stained sections were quantitatively analyzed by an high-resolution imaging software. Features describing the nuclear phenotype such as shape, size, orientation, nuclear membrane irregularities, DNA amount and DNA chromatin organization (ratio and spatial organization of euchromatin and heterochromatin) were calculated for each nucleus and the position of each of them relatively to the basal membrane automatically recorded. At the tissue level, epithelial thickness, cell density and tissue organization were assessed using sophisticated graph-theory algorithms measuring the intrinsic order/disorder and the loss of differentiation, trademarks of the dysplastic growth process. On a subset of 350 biopsies representing the entire spectrum of dysplastic and non-dysplastic abnormalities, p16 protein and Ki-67 protein expression were precisely evaluated by measuring the spatial distribution of Ki67-positive and p16-positive cells in the each individual epithelial layer. HPV typing and ploidy analysis were also performed for each patient. All these data were extrapolated to build a realistic (real metric dimension) cell-based physical model of the stratified normal cervical epithelium and of epithelium from each pathological grade. In the optical module, we used Finite-Difference Time-Domain (FDTD) modeling to simulate light scattering from normal, hyperplastic, metaplastic, CIN 1, CIN2, and CIS cervical cell nuclei at different epithelial depths and we constructed models of basal, parabasal, and other layers nuclei up to the last superficial layer for computational analysis. Simulation results give significant insights into the depth-dependent scattering profile of cervical epithelium as dysplasia progresses. We also intend to carry out meticulous investigation that will also enable mapping of azimuthal asymmetry in angle-dependent scattering patterns; this may lead to observation of fine pattern changes that can further be linked to precancer progression. Results: We will be presenting this novel conceptual framework illustrated by some preliminary data of our ongoing analyses using iPOM predicted results to guide acquisition of confocal imaging as well as spectroscopy probes from clinical cervical tissues. Ultimately, this strategy will allow us to identify the most robust in vivo macroscopic imaging approaches for detection of cervical neoplasia and aid in determining which image features must be acquired to achieve accurate diagnoses (e.g. variations in excitation and emission wavelength, numerical aperture, polarization, etc.). Citation Information: Cancer Prev Res 2011;4(10 Suppl):A10.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».