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Clinical Medicine | Publishing Language: Chinese | Open Access

Combined CT-based extracellular volume with pathologic indicators predicts early postoperative recurrence of gastric mesenchymal tumors

Hong LU1,2Haitao LI2Ping CAI2Xinyue DU1Xiaoqin YIN2Guishen JIANG1Huafeng CHAI1Xue WEI1Yali DENG2Guolan DENG1( )
Department of Cardiovascular Diseases, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016
Department of Radiology, First Affiliated Hospital, Army Medical University (Third Military Medical University), Chongqing, 400038, China
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Abstract

Objective

To investigate the clinical value of extracellular volume (ECV) based on CT delayed phase in combination with pathologic indicators in predicting early recurrence of gastric mesenchymal tumors after surgery.

Methods

A retrospective case-control trial was conducted on the imaging, clinical and pathological data of 110 patients with gastric mesenchymal tumors who were surgically resected at the First Affiliated Hospital of Army Medical University from January 2011 to August 2022. They were 60 males and 50 females, at a mean age of 58±10 years. All of them received preoperative multiphase dynamic CT enhancement examination of the abdomen, and ECV value was calculated with the formula: ECV=(1-hematocrit) × (ΔHU tumor/ΔHU aorta). According to the postoperative recurrence within 24 months after surgery, they were divided into early recurrence group and non early recurrence group. Statistical indexes: ① Consistency analysis. ② The factors affecting early recurrence after resection of gastric mesenchymal stromal tumors were analyzed and a prediction model was conducted. Delong test was used to assess the predictive value of the model. Then a nomogram was plotted based on the combines model, and calibration curves were drawn to assess the efficacy of the column charts, and decision curve analysis (DCA) was adopted to assess the value of the model for clinical application.

Results

① Consistency analysis. After 2 radiologists outlined the region of interest and obtained ECV value according to the above formula, The intraclass correlation coefficient (ICC) was 0.806. ② For the 110 subjected patients, 21 cases of them had early recurrence, and 89 one did not. Multivariate analysis showed that ECV value, risk degree, and tumor length were independent influencing factors for predicting early recurrence. Receiver operating characteristic (ROC) curve analysis indicated that the area under the curve (AUC) value of ECV, hazard degree, and tumor length diameter in predicting early recurrence was 0.838 (95%CI 0.758~0.918), 0.774 (95%CI 0.656~0.892), and 0.700 (95%CI 0.589~0.810), respectively, and the value of their combined model was 0.899 (95%CI 0.811~0.987), which was higher than that of each independent model. The sensitivity and specificity of the combined model was 85.71% and 86.52%, respectively, and the optimal cutoff value was 0.19. Delong test revealed that there was statistical difference between the combined model and the clinical model established by the hazard level (Z=6.548, P < 0.001, 95%CI 0.140~0.259). Calibration curve analysis suggested that the combined model had a better fit, and DCA displayed that the combined model had a better net benefit.

Conclusion

The model established by ECV combined with pathological indicators has good predictive performance and can be used as a more effective predictor of early recurrence of gastric mesenchymal tumors after surgery.

CLC number: R730.7;R735.2;R814.42 Document code: A

References

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Journal of Army Medical University
Pages 2661-2669

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Cite this article:
LU H, LI H, CAI P, et al. Combined CT-based extracellular volume with pathologic indicators predicts early postoperative recurrence of gastric mesenchymal tumors. Journal of Army Medical University, 2024, 46(23): 2661-2669. https://doi.org/10.16016/j.2097-0927.202409117

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Received: 18 September 2024
Revised: 10 October 2024
Published: 15 December 2024
© 2024 Journal of Army Medical University

This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/).