Diagnostic CT-Enabled Planning (DART): Results of a Randomized Trial in Palliative Radiation Therapy
Notice bibliographique
Résumé
PurposeUsing diagnostic computed tomography (dCT) scans instead of CT simulation (CTsim) scans can increase departmental efficiency and reduce patient burden. The goal of the DART trial was to assess the efficacy and acceptability of dCT-based planning workflows with a focus on patient experiences, plan deliverability and adequacy of target coverage, and workflows.Methods and MaterialsPatients undergoing same-day CTsim and treatment for palliative radiation therapy to thoracic, abdominopelvic, or proximal limb targets with a recent dCT (within 28 days) in a reproducible position were eligible. After stratifying by target type (bone or soft tissue vs. visceral), participants were randomized (1:2 ratio) between CTsim-based (CTsim arm) vs. dCT-based planning (dCT arm). The primary endpoint was time in center (TIC), defined as total time spent in the cancer center on first day of treatment, from first radiation department appointment to first fraction completion. Secondary endpoints included plan deliverability, adequacy of target coverage, and stakeholder acceptability.ResultsThirty-three patients (42 treatment sites) were enrolled between June 2022 and April 2023. The median age was 72 (interquartile range [IQR]: 67-78), 73% were male, and the most common primary cancers were lung (33%), prostate (24%), and breast (12%). The most common dose and fractionations were 8 Gy in 1 and 20 Gy in 5 fractions (50% and 43% of plans, respectively). TIC was 4.7 ± 1.1 hours (mean ± SD) in the CTsim arm vs. 0.41 ± 0.14 hours in the dCT arm (P < .001). All dCT plans were deliverable. All plans in both arms were rated as “acceptable” (80% CTsim; 81% dCT) or “acceptable with minor deviation” (20% CTsim; 19% dCT). Patient perception of acceptability was similar in both arms with the exception of time burden, which was rated as “acceptable” by 50% in the CTsim arm vs. 90% in the dCT arm (P = .025).ConclusiondCT-based radiation planning substantially reduced TIC without detriment in plan deliverability or quality and had a tangible impact on patient experience with reduced patient-reported time burden. Using diagnostic computed tomography (dCT) scans instead of CT simulation (CTsim) scans can increase departmental efficiency and reduce patient burden. The goal of the DART trial was to assess the efficacy and acceptability of dCT-based planning workflows with a focus on patient experiences, plan deliverability and adequacy of target coverage, and workflows. Patients undergoing same-day CTsim and treatment for palliative radiation therapy to thoracic, abdominopelvic, or proximal limb targets with a recent dCT (within 28 days) in a reproducible position were eligible. After stratifying by target type (bone or soft tissue vs. visceral), participants were randomized (1:2 ratio) between CTsim-based (CTsim arm) vs. dCT-based planning (dCT arm). The primary endpoint was time in center (TIC), defined as total time spent in the cancer center on first day of treatment, from first radiation department appointment to first fraction completion. Secondary endpoints included plan deliverability, adequacy of target coverage, and stakeholder acceptability. Thirty-three patients (42 treatment sites) were enrolled between June 2022 and April 2023. The median age was 72 (interquartile range [IQR]: 67-78), 73% were male, and the most common primary cancers were lung (33%), prostate (24%), and breast (12%). The most common dose and fractionations were 8 Gy in 1 and 20 Gy in 5 fractions (50% and 43% of plans, respectively). TIC was 4.7 ± 1.1 hours (mean ± SD) in the CTsim arm vs. 0.41 ± 0.14 hours in the dCT arm (P < .001). All dCT plans were deliverable. All plans in both arms were rated as “acceptable” (80% CTsim; 81% dCT) or “acceptable with minor deviation” (20% CTsim; 19% dCT). Patient perception of acceptability was similar in both arms with the exception of time burden, which was rated as “acceptable” by 50% in the CTsim arm vs. 90% in the dCT arm (P = .025). dCT-based radiation planning substantially reduced TIC without detriment in plan deliverability or quality and had a tangible impact on patient experience with reduced patient-reported time burden.
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 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 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 | 0,000 |
| 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 ».