๐ค Authors: Katarzyna Borys, Johannes Haubold, Julius Keyl, Maria A Bali, Riccardo De Angelis, Kรฉvin Brou Boni, Nicolas Coquelet, Judith Kohnke, Giulia Baldini, Lennard Kroll, Sara Schramm, Andreas Stang, Eugen Malamutmann, Jens Kleesiek, Moon Kim, Stefan Kasper, Jens T Siveke, Marcel Wiesweg, Anja Merkel-Jens, Benedikt M Schaarschmidt, Viktor Gruenwald, Sebastian Bauer, Arzu Oezcelik, Servet Bรถlรผkbas, Ken Herrmann, Rainer Kimmig, Stephan Lang, Jรผrgen Treckmann, Martin Stuschke, Boris Hadaschik, Lale Umutlu, Michael Forsting, Dirk Schadendorf, Christoph M Friedrich, Martin Schuler, Renรฉ Hosch, Felix Nensa
Leveraging Sarcopenia index by automated CT body composition analysis for pan cancer prognostic stratification.
This study evaluates the CT-based volumetric sarcopenia index (SI) as a baseline prognostic factor for overall survival (OS) in 10,340 solid tumor patients (40% female). Automated body composition analysis was applied to internal baseline abdomen CTs and to thorax CTs.
SI's prognostic value was assessed using multivariable Cox proportional hazards regression, accelerated failure time models, and gradient-boosted machine learning. External validation included 439 patients (40% female).
Higher SI was associated with prolonged OS in the internal abdomen (HR 0.56, 95% CI 0.52-0.59; Pโ<โ0.001) and thorax cohorts (HR 0.40, 95% CI 0.37-0.43; Pโ<โ0.001), as well as in the external validation cohort (HR 0.56, 95% CI 0.41-0.79; Pโ<โ0.001). Machine learning models identified SI as the most important factor in survival prediction.
Our results demonstrate SI's potential as a fully automated body composition feature for standard oncologic workflows.
