Discrimination between malignant and nonmalignant ascites using serum and ascitic fluid proteins in a multivariate analysis model

Dig Dis Sci. 2000 Mar;45(3):500-8. doi: 10.1023/a:1005437005811.


Our objectives were to study the value of different proteins in the serum and ascitic fluid and assess their potential in discriminating between malignant and nonmalignant ascites in a model that could be developed to aid clinical diagnosis. In all, 57 different measurements (30 in serum and 27 in ascitic fluid) including erythrocyte sedimentation rate, number of white blood cells, cytokines, interleukin-1a (IL-1a), IL-1b, IL-2, IL-6, IL-8, tumor necrosis factor-alpha, immunoglobulins (IgG, IgA, IgM), complement factors C3 and C4, acute-phase proteins such as alpha1-acid glycoprotein, alpha2-macroglobulin, alpha1-antitrypsin, haptoglobin, C-reactive protein, ferritin, ceruloplasmin and transferin, were performed in 61 patients with ascites (25 with malignant exudates, 13 with nonmalignant exudates, and 23 with transudates). Patients with sepsis were excluded. Correlation tests and one-way ANOVAs were used for comparisons between different groups. Discriminant analyses were used to assess the significance of each parameter in the differentiation process. Correct classification of 100% of cases required the use of all 57 ascitic fluid measurements in the model, which was not considered practical in clinical diagnosis. Discriminant analysis showed that five ascitic fluid measurements-total protein, LDH, TNF-alpha, C4, and haptoglobin-were sufficient for a model to correctly classify 89% of cases. Cross-validation showed that 70% of unknown cases were correctly classified using this model. In conclusion, we have shown that five easily taken protein measurements in the ascitic fluid can differentiate to a large extent between cases with ascites and have proposed a relatively simple statistical model with these parameters that could be developed to be extremely useful in the clinical setting.

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Ascitic Fluid / chemistry*
  • Female
  • Humans
  • Male
  • Mathematics
  • Middle Aged
  • Models, Theoretical
  • Multivariate Analysis
  • Neoplasms / diagnosis*
  • Proteins / analysis*


  • Proteins