System radiobiology modelling of radiation induced lung disease
Notice bibliographique
Résumé
Radiation induced lung disease (RILD) is a side effect of radiotherapy for treating thoracic cancers, limiting radiation dose to tumours and in turn the chance of treatment success.A current scheme for predicting and managing RILD risk is based on a population-based normal tissue complication probability (NTCP) model assuming the same response to given radiation dose in lung.However, recent research suggests that dose response can be modified by biological and clinical factors pertinent to pathogenesis of RILD.In this work, we explore systems radiobiology approaches to model RILD as a result of interactions between these factors.Clinical, dosimetric, and biological data on lung cancer patients were analyzed to identify markers associated with high RILD risk.Then, we applied machine learning methods to combine such markers into models that calculate patient-specific RILD risk.We investigated two RILD endpoints: radiation fibrosis (RF) and radiation pneumonitis (RP).RF is formation of scar tissues in lung and can be quantitatively measured from computed tomography (CT) images.We extended a classical NTCP model to explicitly model time-dependent dose response of RF risk.Our modelling results have shown significant change in dose-RF correlation after 3 months post-treatment as well as higher RF risk when tumour was in lower lung.We extended the dose modelling to intra-treatment CT images.However, we did not find association between early CT changes and biological states or clinical outcomes.Subsequent investigations on radiation pneumonitis (RP) also suggest that dose response is modified by factors not related to lung dose distribution, such as dose to heart or production of proteins i guidance that he carried out with his intelligence and dedication.Meeting him the first time in May 2009 -the day he delivered an interview presentation about his vision on systems radiobiology -was a turning point of my academic career.That was the time he brought me into the world of machine learning which was obscure to most of us in this field.I see myself so lucky to having conducted this truly interesting research under his direction.Second, I would also like to thank my co-supervisor Jan Seuntjens for supporting the biomarker protocol as well as his leadership in the department which brought the CREATE program.Many thanks to Norma Ybarra for all the hard work in the wet lab and educating me with her endless knowledge in biology.Asha Jeyaseelan, my "the other lung", worked diligently for recruiting patients and setting collaboration with CHUM, which she did exceptionally well with her charm.Thank you Seema Ambereen for continuing Asha's work to keep this project progressing.So many thanks to Neil Kopek and Nathalie Japkowicz for their expert advices and commitment in the thesis committee.The CT imaging projects would have been impossible
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) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».