Notice bibliographique
Résumé
The purpose of this Thesis is to present a Bayesian analysis of oncological data sets with particular focus on cervical carcinomas and ovarian cancers. \n \nBayesian methods of data analysis have a very long history, and have been used with great success in many disciplines, from Physics to Econometrics. Nonetheless, they remain very controversial among statisticians who belong to the orthodox - i.e, frequentist school, and are not well known by the medical community. To help in that direction, we reviewed in the introductory chapter the basic philosophical and practical differences between the two schools, and in the second chapter, we briefly reviewed the history of Bayesian methodology, from the early efforts of Thomas Bayes and of Pierre Simon de Laplace to the modern contributions of Harold Jeffreys, Richard Cox, and Edwin Jaynes. \n \nIn many aspects of medical research, we deal with experimental data from which a certain proposition or hypothesis is validated. Unlike in physics, where we have strong and solid foundations such as Newton's law of motion, Snell's optical laws, Kirchoff's laws, Einstein's relativity theory, and many more, we do not have such privileges in medical research. Hence, many hypotheses are constantly tested as new evidence becomes available. One of the actively-researched medical areas is cancer research about which our understanding is still in its infancy. Numerous experiments (both in vivo and in vitro) and clinical trials have been conducted to further our knowledge; thus, Bayesian methodology finds its place to aid us in obtaining scientific inferences about certain propositions or hypotheses from available data and resources. \n \nIn this work, we use data given to us by our medical collaborators at the Princess Margaret Hospital (PMH) in Toronto to carry out two main projects: Firstly, to make an inference about the oxygenation status (oxygen partial pressure, pO$_2$) within human cervical carcinomas and secondly, an inference about the effectiveness of various molecularly-targeted agents (MTAs) in phase II clinical trials of relapsed ovarian cancer patients. \n \nIn the first problem, we address the challenges of tumor hypoxia - a state of oxygen deprivation in tumors. Currently, there are two methods to obtain tumor oxygen status, namely the direct Eppendorf needle electrode and the indirect immunohistochemical assay of a protein marker, Carbonic Anhydrase IX (CAIX). In this project, we introduce Bayesian probability theory to obtain inferences about tumor oxygenation from each technique and the concordance between the two techniques. From this study, we conclude that under certain conditions, two biopsies are sufficient to infer the tumor oxygenation level based on the immunohistochemical assays of CAIX. Additionally, there is a fair concordance between the direct and the indirect measurements of tumor oxygenation. \n \nIn the latter problem, ovarian cancer is the topic of study. Ovarian cancer has the highest mortality rate among gynecological cancers and one that is known to relapse. CA-125 is still the most inexpensive biomarker for monitoring ovarian cancers. From the phase II clinical trial data, we demonstrate the survival advantage of CA-125 responsive group of patients by means of a non-parametric Kaplan-Meier statistic.
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 | 0,022 | 0,054 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,005 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,005 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
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 ».