Computed Tomography-Based Assessment of Sarcopenia and Disease Progression in Pancreatic Ductal Adenocarcinoma: A Radiomics and Machine Learning Approach
Jean-Philippe R. Krieg, Luisa Gallee, Daniel Wolf, Konstantin Muller, Michael Goetz, Thomas J. Ettrich, Thomas Seufferlein, Christopher Kloth, Meinrad Beer, Daniel Vogele · 2026
Read Computed Tomography-Based Assessment of Sarcopenia and Disease Progression in Pancreatic Ductal Adenocarcinoma: A Radiomics and Machine Learning Approach by Jean-Philippe R. Krieg, Luisa Gallee, Daniel Wolf, Konstantin Muller, Michael Goetz, Thomas J. Ettrich, Thomas Seufferlein, Christopher Kloth, Meinrad Beer, Daniel Vogele (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13278679.txt.
Description
Jean-Philippe R Krieg leads a 2026 study of muscle loss and disease progression in pancreatic cancer, using CT scans over time. Sarcopenia, loss of muscle mass and strength, predicts worse outcomes in cancer. The team studied 62 patients with pancreatic ductal adenocarcinoma who had at least three scans over a year, measuring a hip muscle index and extracting image texture features.
Muscle-based sarcopenia affected about forty-five percent of patients by standard cutoffs. The muscle index fell from baseline to three months, then held steady, with women consistently lower. Machine learning models predicted sarcopenia fairly and predicted progression status at twelve months with good accuracy from baseline image features. Muscle and image measures may add prognostic value beyond tumor readings alone.
Catalog record
| CREATOR | Jean-Philippe R. Krieg, Luisa Gallee, Daniel Wolf, Konstantin Muller, Michael Goetz, Thomas J. Ettrich, Thomas Seufferlein, Christopher Kloth, Meinrad Beer, Daniel Vogele |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 36091 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |