Assessment of the impact of delays to radiotherapy on prostate cancer mortality in localized prostate cancer.
Notice bibliographique
Résumé
5100 Background: Resource constraints and patient preferences may lead to delays in the treatment of localized prostate cancer, but the implications of such delays remain unclear. We aimed to investigate the impact of time from diagnosis to treatment initiation (TTI, including neoadjuvant ADT) on prostate cancer-specific mortality (PCSM) in patients receiving radiotherapy for localized prostate cancer. Methods: Patients diagnosed with localized prostate cancer from 2004 to 2020 who received radiotherapy as part of their first course of treatment were identified from the SEER 17 database. Those who initially underwent active surveillance or surgery, as well as those whose TTI exceeded 24 months, were excluded. The remaining patients were divided into cohorts with prespecified TTI intervals of 0-3 months, 4-6 months, and >6 months. Covariates were age, race, county median income, county remoteness, diagnosis year, T stage, PSA, Gleason grade, and treatment modality (external beam radiotherapy, brachytherapy, or a combination). Missing covariates were imputed 50 times using multiple imputations with chained equations, after which propensity score weighting using Bayesian additive regression trees was performed for each imputed dataset. Pooled marginal Cox models, in accordance with Rubin's rules, were used to compare the PCSM of the three TTI cohorts. Additionally, a prespecified subgroup analysis based on NCCN risk classification was completed. Results: A total of 230,278 patients with a median follow-up of 7.8 years were eligible for analysis, of whom 168,432 (73.1%) had a TTI of 0-3 months, 46,738 (20.3%) had a TTI of 4-6 months, and 15,108 (6.6%) had a TTI of >6 months. After propensity score weighting, the maximal standardized mean difference across all covariates and imputations was less than 0.03. Weighted 10-year PCSMs were 5.9%, 5.6%, and 7.1% for patients with TTIs of 0-3 months, 4-6 months, and >6 months, respectively. The PCSM of patients with a TTI of 4-6 months did not differ from that of patients with a TTI of 0-3 months (HR 0.95, 95% CI 0.89-1.01; P=0.09). However, patients with a TTI of >6 months had a higher risk of PCSM than those with TTIs of 0-3 months (HR 1.22, 95% CI 1.09-1.36; P<0.001) or 4-6 months (HR 1.28, 95% CI 1.13-1.45; P<0.001). There was no significant interaction between TTI and NCCN risk group (P=0.49). Conclusions: A TTI exceeding 6 months was associated with an increased risk of prostate cancer mortality. These findings support the timely initiation of treatment for patients undergoing radiotherapy for localized prostate cancer. PCSM by NCCN risk subgroup. Subgroup 4-6 months vs. 0-3 monthsHR (95% CI) >6 months vs. 0-3 monthsHR (95% CI) >6 months vs. 4-6 monthsHR (95% CI) Overall 0.95 (0.89-1.01) 1.22 (1.09-1.36) 1.28 (1.13-1.45) Low risk 1.02 (0.89-1.17) 1.05 (0.86-1.29) 1.04 (0.83-1.30) Intermediate risk 0.94 (0.86-1.03) 1.19 (1.02-1.38) 1.27 (1.07-1.50) High risk 0.94 (0.85-1.03) 1.28 (1.06-1.54) 1.37 (1.11-1.68)
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 | 0,003 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| 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 | 0,001 |
| 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 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 ».