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. 2022 Apr 22;11(9):2356.
doi: 10.3390/jcm11092356.

Influence of Baseline CT Body Composition Parameters on Survival in Patients with Pancreatic Adenocarcinoma

Nick Lasse Beetz  1 , Dominik Geisel  1 , Christoph Maier  1 , Timo Alexander Auer  1   2 , Seyd Shnayien  1 , Thomas Malinka  3 , Christopher Claudius Maximilian Neumann  4 , Uwe Pelzer  4 , Uli Fehrenbach  1
Affiliations

Influence of Baseline CT Body Composition Parameters on Survival in Patients with Pancreatic Adenocarcinoma

Nick Lasse Beetz et al. J Clin Med. .

Abstract

Pancreatic cancer is the seventh leading cause of cancer death in both sexes. The aim of this study is to analyze baseline CT body composition using artificial intelligence to identify possible imaging predictors of survival. We retrospectively included 103 patients. First, the presence of surgical treatment and cut-off values for sarcopenia and obesity served as independent variates. Second, the presence of surgery, subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and skeletal muscle index (SMI) served as independent variates. Cox regression analysis was performed for 1-year, 2-year, and 3-year survival. Possible differences between patients undergoing surgical versus nonsurgical treatment were analyzed. Presence of surgery significantly predicted 1-year, 2-year, and 3-year survival (p = 0.01, <0.001, and <0.001, respectively). Across the follow-up periods of 1-year, 2-year, and 3-year survival, the presence of sarcopenia became an equally important predictor of survival (p = 0.25, 0.07, and <0.001, respectively). Additionally, increased VAT predicted 2-year and 3-year survival (p = 0.02 and 0.04, respectively). The impact of sarcopenia on 3-year survival was higher in the surgical treatment group (p = 0.02 and odds ratio = 2.57) compared with the nonsurgical treatment group (p = 0.04 and odds ratio = 1.92). Fittingly, a lower SMI significantly affected 3-year survival only in patients who underwent surgery (p = 0.02). Especially if surgery is performed, AI-derived sarcopenia and reduced muscle mass are unfavorable imaging predictors.

Keywords: AI; CT; artificial intelligence; body composition; computed tomography; imaging predictors; oncology; pancreatic cancer; surgery; survival.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flowchart depicting the inclusion and exclusion criteria for this study. CT = computed tomography, n = number.
Figure 2
Figure 2
Example illustrating the result of the PACS-integrated AI-based body composition analysis in a patient with pancreatic adenocarcinoma. The patient has reduced muscle mass with a SMI of 28.2 cm2/m2 indicating the presence of sarcopenia. There is accumulation of gas in the gallbladder caused by a common bile duct stent. Each segmented tissue is coded with a different color: psoas muscle = purple, skeletal muscle = green, SMI = skeletal muscle index, visceral fat = dark green, subcutaneous fat = blue. Tissue areas were automatically calculated.
Figure 3
Figure 3
Kaplan-Meier curve demonstrating that over the total follow-up period of three years the AI-derived body composition parameter sarcopenia evolves as a significant imaging predictor of survival in patients with pancreatic adenocarcinoma. Patients suffering from sarcopenia had significantly poorer survival rates (log-rank, p = 0.006).
See this image and copyright information in PMC

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