Associations between body composition measurements (BCM) and phase 1 (P1) oncology clinical trial outcomes.
Notice bibliographique
Résumé
e18648 Background: Cancer-related malnutrition and cachexia can lead to body composition changes. BCM can be assessed at the third lumbar (L3) vertebra by CT, which is available as part of pre-trial evaluation. We previously found that malnutrition and low psoas muscle area (PMA) are associated with adverse P1 outcomes including higher rates of ≥ Grade 3 toxicity (G3T). Here we evaluate the relationships between comprehensive cross-sectional muscle and adipose tissue BCM at L3 on P1 outcomes. Methods: Baseline CT scans for 82 patients (pts) were reviewed and images at the level of L3 were identified by 3 independent reviewers. A CT L3 image selected by at least 2 reviewers underwent analysis by Slice-O-Matic software (Tomovision, Canada) to generate BCM including: skeletal muscle area (SMA), skeletal muscle radiodensity (SMD), and adipose tissue area [intermuscular (IMAT), visceral (VAT), subcutaneous (SAT), and total adipose (TAT)] in cm2. SMA was normalized by height (m2), yielding cross-sectional skeletal muscle index (SMI). We stratified pts by having a SMI, SMD, IMAT, VAT, SAT, and TAT above or below the median value. We evaluated for associations between BCM and the following outcomes: rates of ≥ G3T, frequency of dose reductions/interruptions, hospitalizations, tumor response, disease control, duration on study (DOS), and overall survival (OS). Chi-square analysis was used to determine statistical significance between groups. Kaplan-Meier curves were used to compare DOS and OS. A multivariable analysis (MVA) was conducted via logistic regression to evaluate the association between SMI, VAT, PMA and ≥ G3T controlling for age and gender. Results: 82 P1 pts were included (38 M, 45 F), with a median age of 60 (range 28-85). The most common disease site was gastrointestinal (33%). Mean SMI was 44.78 cm2/m2 (range 25.70-79.89). Higher SMI was associated with a reduced risk of ≥ G3T (36.6% vs 58.5%; p = 0.047) and a trend towards improved OS (p = 0.07). There was no association between SMD, IMAT, SAT, or TAT and toxicity, however, higher VAT was associated with reduced risk of ≥ G3T (31.7% vs 63.4%, p = 0.004), and improved response to therapy (p = 0.001). A MVA controlling for age and gender showed that reduced SMI (AUC 0.7072), increased VAT (AUC 0.7597), and reduced PMA (AUC 0.757) were similar in their ability to predict ≥ G3T. Conclusions: P1 trials are designed to determine the safety and tolerability of investigational agents. In this population of P1 pts, BCM including higher baseline CT L3 SMI and VAT were associated with a reduced risk of ≥ G3T. BCM were also tied to efficacy as high VAT was associated with improved tumor response while a trend towards improved OS was noted for pts with higher baseline SMI. Future research should examine the value of integrating CT-based BCM into dose-selection algorithms when evaluating safety in P1 trials to minimize treatment-related toxicity and optimize therapeutic benefit.
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 | 0,008 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».