ArticleAmerican journal of surgery2022
Validation of automated body composition analysis using diagnostic computed tomography imaging in patients with pancreatic cancer.
Article in American journal of surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 7 citations in OpenAlex.
- Pretherapeutic radiological sarcopenia in pancreatic ductal adenocarcinoma: a narrative review.Future science OA · 2026Review
- Body Composition and Melanoma Outcomes in Patients on Immunotherapy or Targeted Therapy: An Analysis from Canadian Melanoma Research Network.Current oncology (Toronto, Ont.) · 2026Article
- Quantitative Assessment of Body Composition in Cirrhosis.Diagnostics (Basel, Switzerland) · 2024Review
- Pancreatic cancer and sarcopenia: a narrative review of the current status.International journal of clinical oncology · 2024Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundSarcopenia is associated with complications and inferior oncologic outcomes in solid tumors. Axial computed tomography (CT) scans can be used to evaluate sarcopenia, however manual quantification is laborious. We sought to validate an automated method of quantifying muscle cross-sectional area (CSA) in patients with pancreatic adenocarcinoma (PDAC).
methodsMid-L3 CT images from patients with PDAC were analyzed: CSAs of skeletal muscle (SM) were measured using manual segmentation and the software AutoMATiCA, and then compared with linear regression.
resultsFive-hundred-twenty-five unique scans were analyzed. There was robust correlation between manual and automated segmentation for L3 CSA (R
conclusionsAutomated muscle CSA measurement with AutoMATiCA is highly efficient and yields results highly correlated with manual measurement. These findings support the potential use of high-throughput sarcopenia analysis with abdominal CT scans for both clinical and research purposes.
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Registered trials
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