ArticleBMC gastroenterology2025
Establishment of normative spleen volume in a Japanese cohort using automated CT segmentation: diagnostic implications for liver fibrosis.
Article in BMC gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Nationwide organ volume distributions and cross-sectional age-associated differences in abdominal CT from Japan.Japanese journal of radiology · 2026Article
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6 authors.
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Abstract
backgroundIn this study, we aimed to establish specific reference values for spleen volume in Japan using automated deep learning-based segmentation as well as evaluate the diagnostic utility of volume-defined splenomegaly in staging liver fibrosis.
methodsWe analyzed 4,732 healthy Japanese adults (Data Set 1) who underwent unenhanced abdominal computed tomography. Spleen volumes were measured using a deep learning-based segmentation tool (mean Dice coefficient = 0.95; median processing time = 26.6 s). A multivariable linear regression model incorporating sex, age, weight, and height was developed and validated. An external cohort of 136 patients with histologically confirmed liver disease (Data Set 2) was used to assess the clinical utility of volume-based splenomegaly. Z-scores were calculated from prediction residuals, using thresholds of Z ≥ + 1 and Z ≥ + 2. Diagnostic performance for cirrhosis (F4) was evaluated using receiver operating characteristic analysis.
resultsThe final predictive model used weight alone (Predicted spleen volume = 3.08 + 1.96 × Weight [kg]), with a mean absolute error of 38.5 mL. In Data Set 2, splenomegaly prevalence increased with fibrosis stage: for Z ≥ + 1, it was 0.0% (F1), 32.7% (F2), 54.3% (F3), and 74.4% (F4); and for Z ≥ + 2, it was 0.0%, 16.3%, 34.3%, and 48.8%, respectively. The area under the curve for detecting F4 was 0.73.
conclusionWe established population-specific spleen volume reference values using deep learning-based segmentation. The model enables objective identification of splenomegaly from routine clinical parameters, supporting its integration into artificial intelligence-assisted radiology workflows.
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