Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz

Logo MUG-Forschungsportal

Gewählte Publikation:

SHR Neuro Krebs Kardio Lipid

Barth, DA; Brenner, C; Riedl, JM; Prinz, F; Klocker, EV; Schlick, K; Kornprat, P; Lackner, K; Stöger, H; Stotz, M; Gerger, A; Pichler, M.
External validation of the prognostic relevance of the advanced lung cancer inflammation index (ALI) in pancreatic cancer patients.
Cancer Med. 2020;
PubMed FullText FullText_MUG

 

Autor/innen der Med Uni Graz:
Barth Dominik
Gerger Armin
Klocker Eva Valentina
Kornprat Peter
Lackner Karoline
Pichler Martin
Prinz Felix
Riedl Jakob
Stoeger Herbert
Stotz Michael
Altmetrics:

Dimensions Citations:

Plum Analytics:
Abstract:
The advanced lung cancer inflammation index (ALI) was first introduced for prognosis prediction in lung cancer patients and since then evaluated in several other malignancies. However, in pancreatic cancer (PC) the ALI and its prognostic utility were only investigated in a comparably small and specific cohort of locally advanced PC patients treated with chemoradiotherapy. In our single-center cohort study, we included 429 patients with histologically verified PC who were treated between 2003 and 2015 at our academic institution. The ALI was defined as body mass index (BMI; kg/m2 ) × serum albumin levels (g/dL)/neutrophil-lymphocyte ratio (NLR) and we defined the optimal cutoff for biomarker dichotomization by ROC-analysis. Kaplan-Meier method as well as uni- and multivariate Cox regression Hazard proportional models were implemented to assess the prognostic potential of ALI in PC patients. We considered cancer-specific survival (CSS) as the primary endpoint of the study. The ALI showed a significant negative correlation with CA19-9 levels and C-reactive protein levels whereas we found an association with localized tumor stage and better performance status (P < .05 for all mentioned variables). As opposed to patients with a high ALI, decreased ALI was significantly associated with shorter CSS (HR = 0.606, 95% CI: 0.471-0.779, P = .001). Multivariate analysis demonstrated tumor grade, tumor stage, chemotherapy, C-reactive protein levels, and CA19-9 levels to independently predict for CSS (all P < .05). In contrast the ALI failed to independently predict for CSS in the performed multivariate models (HR = 0.878, 95% CI: 0.643-1.198, P = .411). In this large cohort of PC patients, the ALI did not complement existing clinicopathological factors for outcome determination. © 2020 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

© Med Uni Graz Impressum