HPB P36 The influence of sarcopenia and systemic inflammation on survival in resected pancreatic cancer
Notice bibliographique
Résumé
Abstract Background Sarcopenia, cachexia and systemic inflammation are early hallmarks of pancreatic cancer (PC) which hamper systemic therapy and accelerate the terminal stages of the disease. The significant impact of sarcopenia on survival in PC has been demonstrated in numerous meta-analyses using standardised thresholds to define body composition measurements. We sought to determine the influence of sarcopenia and systemic inflammation on survival in our cohort of resected PC patients. We also interrogated the impact of the introduction of neoadjuvant therapy (NAT) protocols on survival, body composition and systemic inflammation. Finally we investigated the use of varying thresholds to define sarcopenia and the impact of this on survival. Methods Analysis of a prospectively maintained database of resected patients with PC accrued through the tertiary West of Scotland Pancreatic Unit was undertaken. Patients with cancer types other than pancreatic ductal adenocarcinoma were excluded. Body composition analysis was carried out using cross-sectional measurements of skeletal muscle area, skeletal muscle density and visceral fat area at the L3 level from pre-treatment or pre-operative CT scan using Slice-o-Matic software (Tomovision, Montreal). Disease-specific survival was used with Log-rank test for survival differences. Results 188 patients underwent resection between 2008–2020. 44.1% received NAT and 65.9% were resected up-front. Using Prado / Martin cutoffs of skeletal muscle index (SMI) to define sarcopenia demonstrated no significant survival difference in either cohort. Visceral obesity and myosteatosis (defined by Doyle / Martin) also demonstrated no significant survival difference. Modified thresholds tailored to our cohort were used and a median survival difference of 34.5 vs 23.1 months was identified (p < 0.001). Similarly for modified BMI-specific thresholds a median survival difference of 35.2 vs 22.8 months was identified (p < 0.001). On further analysis of the NAT cohort, 60.2% of patients exhibited gain in SMI with treatment and 39.8% of patients exhibited loss of SMI. There was a tendency towards poorer survival in the latter group however this was not significant. On further stratification of cohorts in to NAT and up-front resection it was revealed that the predictive effect of sarcopenia was entirely the effect of the up-front resected cohort, with little difference in survival between sarcopenic and non-sarcopenic groups in the NAT cohort. Median survival in all patients (NAT and up-front resection combined) increased from 27.4 months to 32.6 months (p = 0.016) with the introduction of NAT protocols in 2013. Conclusions Defining sarcopenia using modified thresholds tailored specifically to our cohort of patients demonstrates the significant impact of sarcopenia on survival in resected PC. The introduction of NAT protocols in 2013 has had a significant impact on survival, an effect which may be due to the selection of smaller tumours for up-front resection. Further work is needed as part of intention-to-treat analysis to identify patients who undergo NAT who do not progress to resection. Analysis of this cohort will validate our findings and shed further light on the impact of neoadjuvant therapy on sarcopenia and body composition. This analysis is ongoing and further novel data will be presented in September should this abstract be accepted.
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 | 0,001 | 0,003 |
| 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 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».