A nomogram model for differentiating gastric schwannoma from gastric stromal tumor based on CT imaging features
10.3760/cma.j.cn115455-20231031-00412
- VernacularTitle:基于CT影像特征构建鉴别胃神经鞘瘤与胃间质瘤的列线图模型
- Author:
Luping ZHAO
1
;
Haoran LU
;
Yuhong WANG
;
Jingjing XU
;
Zhanguo SUN
;
Yueqin CHEN
;
Zecan WENG
;
Sen MAO
Author Information
1. 济宁医学院附属医院医学影像科，济宁 272000
- Keywords:
Neurilemmoma;
Stomach;
Gastrointestinal stromal tumors;
Tomography, spiral computed;
Nomograms
- From:
Chinese Journal of Postgraduates of Medicine
2024;47(7):624-630
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a nomogram model for differentiating gastric schwannoma (GS) from gastric stromal tumor (GST) (diameters 2 to 5 cm) based on CT imaging features before surgery.Methods:The clinical and imaging data of 49 patients with GS and 240 patients with GST in the Affiliated Hospital of Jining Medical University from July 2009 to April 2023 and Guangdong Provincial People′s Hospital from June 2017 to September 2022 were analyzed retrospectively. The independent factors for differentiating GS from GST were obtained by multivariate Logistic regression analysis. The nomogram model was constructed by R4.3.1 software. The efficacy of the nomogram model for differentiating GS from GST was evaluated by the receiver operating characteristics (ROC) curve, and calibration curve and decision curve analysis were used to evaluate the predictive efficacy and clinical application value of the nomogram model.Results:There were no statistical differences in the clinical symptom rate, calcification rate, ulcer rate, tumor vessel rate, ratio of long diameter to short diameter and CT value difference during the arterial and nonenhanced phases (CTV A-N) between GS patients and GST patients ( P>0.05). The proportion of female, incidence of lesions located in central or lower part of stomach, extraluminal or mixed growth rate, tumor-associated lymph node rate, strong enhancement rate, CT value difference during the portal and nonenhanced phases (CTV P-N), CT value difference during the delayed and nonenhanced phases (CTV D-N), CT value difference during the portal and arterial phases (CTV P-A) and CT value difference during the delayed and portal phases (CTV D-P) in GS patients were significantly higher than those in GST patients: 75.51% (37/49) vs. 58.33% (140/240), 85.71% (42/49) vs. 54.17% (130/240), 75.51% (37/49) vs. 45.00% (108/240), 44.90% (22/49) vs. 5.42% (13/240), 51.02% (25/49) vs. 27.08% (65/240), 32.0 (26.0, 43.5) HU vs. 29.0 (22.0, 37.7) HU, (44.59 ± 13.46) HU vs. (32.94 ± 12.47) HU, 20.0 (11.5, 25.0) HU vs. 10.0 (5.0, 17.0) HU and 9.0 (6.0, 12.0) HU vs. 4.0 (-2.7, 7.0) HU, the age, irregular shape rate, cystic degeneration rate and heterogeneous enhancement rate were significantly lower than those in GST patients: (58.12 ± 12.59) years old vs. (62.05 ± 11.22) years old, 16.33% (8/49) vs. 38.33% (92/240), 18.37% (9/49) vs. 51.25% (123/240) and 34.69% (17/49) vs. 56.25% (135/240), and there were statistical differences ( P<0.05 or<0.01). Multivariate Logistic regression analysis result showed that location, cystic degeneration, tumor-associated lymph node, CTV P-A and CTV D-P were the independent factors for differentiating GS from GST ( OR= 3.599, 0.201, 19.031, 1.124 and 1.160; 95% CI 1.184 to 10.938, 0.070 to 0.578, 6.159 to 58.809, 1.066 to 1.185 and 1.094 to 1.231; P<0.05 or<0.01). The nomogram model for differentiating GS from GST was constructed based on location, cystic degeneration, tumor-associated lymph node, CTV P-A and CTV D-P. The area under curve of the nomogram model for differentiating GS from GST was 0.924 (95% CI 0.887 to 0.951). The calibration curve analysis result showed that there was a good agreement between the predicted GS curve and the actual GS curve (the mean absolute error was 0.033). The result of the Hosmer-Lemeshow goodness-of-fit test indicated that the calibration of the nomogram model was appropriate ( χ2 = 2.52, P = 0.961). The clinical decision curve analysis result showed that when the threshold for the nomogram model for differentiating the two tumors was>0.03, the nomogram yielded more net benefits than the "all patients treated as GS" or "all patients treated as GST" scenarios. Conclusions:The nomogram model based on CT imaging features can be used to differentiate GS from GST before surgery.
