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Updated: Oct 3, 2026

Gene Regulation and Targeted Therapy in Gastric Cancer Peritoneal Metastasis: Radiological Findings from Dual Energy CT and PET/CT
Published on: January 22, 2018
An ascites grading system for predicting the prognosis of gastric cancer with peritoneum dissemination
Michitaka Honda1,2, Hidetaka Kawamura1,2, Hiroshi Kobayashi1,2
1Department of Minimally Invasive Surgical and Medical Oncology Fukushima Medical University Fukushima Japan.
Aim:
Gastric cancer with peritoneum dissemination is intractable with surgical resection. The evaluation of the degree of dissemination using computed tomography (CT) is difficult. We focused on the amount of ascites based on CT findings and established a scaling system to predict these patients' prognoses.
Methods:
We extracted individual data from a population-based cohort. Patients diagnosed with histologically proven gastric adenocarcinoma with peritoneum dissemination were enrolled. Two raters evaluated the CT images and determined the grade of ascites in each patient: grade 0 indicated no ascites in all slices; grade 1 indicated ascites detected only in the upper or lower abdominal cavity; grade 2 indicated ascites detected in both the upper and lower abdominal cavities; and grade 3 indicated ascites extending continuously from the pelvic cavity to the upper abdominal cavity. We evaluated the relationship between the ascites grade and survival time. After adjusting for other clinical factors, we calculated hazard ratios of each ascites grade.
Results:
A total of 718 patients were enrolled. The number of patients with grades 0, 1, 2, and 3 were 303, 223, 94, and 98, respectively. The median overall survival times were 16.0, 8.7, 5.4, and 3.0 months for ascites on CT grades 0, 1, 2, and 3, respectively (P < .001). The adjusted hazard ratios for the survival time were 1.74 (1.33-2.26, P < .001), 3.20 (2.25-4.57, P < .001), and 4.76 (3.16-7.17, P < .001) for grades 1, 2, and 3, respectively.
Conclusion:
We established a new grading system of pretreatment ascites to better predict the prognosis of gastric cancer.

