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April 1, 2026BMC Medical Imaging0 citationsOpen Access

A scoring model for differentiating gastric calcifying fibrous tumors from gastrointestinal stromal tumors less than 2 cm based on CT features

HYHuijia YinJXJianxia XuBWBeiran Wang

Key Points

  • The research aims to compare CT features of calcifying fibrous tumors and gastrointestinal stromal tumors under 2 cm to create a scoring system for differentiation.
  • Compared clinical and CT features of 22 CFTs and 63 GISTs.
  • Used univariate and multivariate logistic regression analyses to identify predictors.
  • Developed a simplified scoring system based on regression coefficients and model performance evaluated by ROC analysis.
  • Identified eight significant variables for differentiation, including LD/SD ratio and calcification level.
  • Developed a scoring system ranging from −2 to 5 points with higher scores indicating greater likelihood of CFT.
  • Achieved AUCs of 0.833 for the predictive model and 0.822 for the scoring model, indicating good diagnostic performance.

Abstract

Abstract Objectives This study aimed to compare the CT features of calcifying fibrous tumors (CFTs) and gastrointestinal stromal tumors (GISTs) less than 2 cm, and to establish a scoring system to differentiate them. Methods A total of 85 patients were included, comprising 22 CFTs and 63 GISTs. Clinical and CT imaging features were compared between the two groups. Independent predictors were identified using univariate and multivariate logistic regression analyses. A predictive model was constructed and converted into a simplified scoring system based on regression coefficients. Model performance was evaluated using receiver operating characteristic (ROC) analysis and the Hosmer–Lemeshow goodness-of-fit test. Results The analysis revealed eight variables with statistically significant differences ( P < 0.05). Four variables were included in the score model, including long diameter/short diameter (LD/SD) ratio, calcification (mild), calcification (moderate), and degree of enhancement (moderate), and identified as independent predictors of CFT. The scoring system ranged from − 2 to 5 points, where higher scores correlated with a greater likelihood of CFT. The AUCs for the predictive model and scoring model were 0.833 and 0.822, respectively, with no significant difference between the two ( P = 0.595). To facilitate clinical application, the scoring system was divided into four ranges, with corresponding probabilities of CFT of 7.14%, 14.29%, 64.71%, and 80.00%. Conclusion The proposed CT-based predictive model and scoring system demonstrate good diagnostic performance and may enhance diagnostic confidence, potentially reducing unnecessary invasive procedures when CFT is suspected.

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Cite This Study

Yin et al. (2026) studied this question.

synapsesocial.com/papers/69cd7ac55652765b073a8408https://doi.org/10.1186/s12880-026-02322-2
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