Dynamic blood dose estimates in radiotherapy and correlations with adverse clinical outcomes in brain, head‐and‐neck, and lung cancer patients
Notice bibliographique
Résumé
Abstract Background In cancer radiotherapy, radiation‐induced lymphopenia (RIL) has been reported to be correlated with adverse clinical outcomes such as reduced locoregional control (LRC), distant‐metastasis‐free survival (DMFS), and overall survival (OS) in various treatment sites. Frameworks to simulate the radiation dose to circulating blood have been developed in response, and simulated blood dose values have been reported to be correlated with severe RIL and/or adverse clinical outcomes. However, validations with different patient datasets or expansions to additional treatment sites, as well as the identification of particularly relevant blood dose metrics and blood compartments to allow for their inclusion during radiotherapy treatment planning, remain lacking. Purpose This study aims to investigate a potential correlation between simulated blood dose values and adverse clinical outcomes in 215 patients with head‐and‐neck squamous cell carcinoma (HNSCC), 180 patients with glioblastoma (GBM), and 490 patients with non‐small‐cell lung cancer (NSCLC), and to identify particularly relevant blood dose metrics and blood compartments to allow for their inclusion during radiotherapy treatment planning and thereby enable the optimization of the estimated dose delivered to circulating blood. Methods For all 885 patients, TotalSegmentator was used to automatically delineate additional organs‐at‐risk (OARs), blood vessels, and tissues which were not already manually delineated for radiotherapy treatment planning. Subsequently, the dynamic HEDOS model, which considers temporal aspects such as blood flow dynamics and treatment delivery time, was used to simulate the radiation dose delivered to circulate blood during radiotherapy. Static blood dose models consisting of the mean dose to the union of all HEDOS blood compartments ( D static,HEDOS ) and the integral body dose ( D static,body ) were also investigated to verify whether a simplified blood dose model equally exhibited any correlations with adverse clinical outcomes. Results During multivariable Cox regression analysis, the blood dose estimates from the dynamic blood dose model exhibited a statistically significant ( p < 0.05) correlation with DMFS and OS in the HNSCC and NSCLC datasets as well as with LRC in the HNSCC dataset. D static,body and D static,HEDOS only exhibited a statistically significant correlation with OS in the GBM and NSCLC datasets. Within a dataset, different dynamic blood dose metrics generally consistently exhibited correlations with the same clinical outcomes. Large arteries and veins were found to be a particularly relevant blood compartment within the HNSCC dataset, while the dose to the healthy portion of the brain and the dose to the heart and lungs were found to exhibit particularly strong correlations with dynamic blood dose estimates in the GBM and NSCLC datasets, respectively. Conclusions The dynamic blood dose model exhibited a statistically significant correlation with adverse clinical outcomes in five out of seven cases, compared to just two cases for the static blood dose models. Consideration of temporal aspects such as blood flow dynamics and treatment delivery time was therefore essential to some of the observed correlations. For each treatment site, particularly relevant blood compartments were identified, allowing for their inclusion during radiotherapy treatment planning as part of future studies which aim to reduce the estimated dose to circulating blood.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 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 | 0,001 |
| 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 ».