Relationship between multi-slice computed tomography features and pathological risk stratification assessment in gastric gastrointestinal stromal tumors

被引:4
作者
Wang, Tian-Tian [1 ]
Liu, Wei-Wei [2 ]
Liu, Xian-Hai [3 ]
Gao, Rong-Ji [1 ]
Zhu, Chun-Yu [1 ]
Wang, Qing [4 ]
Zhao, Lu-Ping [5 ]
Fan, Xiao-Ming [1 ]
Li, Juan [1 ,6 ]
机构
[1] Shandong First Med Univ, Dept Med Imaging, Affiliated Hosp 2, Tai An 271000, Shandong Provin, Peoples R China
[2] Shandong First Med Univ, Dept Rheumatol, Affiliated Hosp 2, Tai An 271000, Shandong Provin, Peoples R China
[3] Shandong First Med Univ, Dept Network Informat Ctr, Affiliated Hosp 2, Tai An 271000, Shandong Provin, Peoples R China
[4] Shandong First Med Univ, Dept Ultrasound, Affiliated Hosp 2, Tai An 271000, Shandong Provin, Peoples R China
[5] Jining Med Univ, Dept Med Imaging, Affiliated Hosp, Jining 272000, Shandong Provin, Peoples R China
[6] Shandong First Med Univ, Dept Med Imaging, Affiliated Hosp 2, 366 Taishan St, Tai An 271000, Shandong Provin, Peoples R China
关键词
Computed tomography; Gastrointestinal stromal tumor; Risk stratification; Stomach; CT; DIAGNOSIS; PROGNOSIS;
D O I
10.4251/wjgo.v15.i6.1073
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
BACKGROUND Computed tomography (CT) imaging features are associated with risk stratification of gastric gastrointestinal stromal tumors (GISTs). AIM To determine the multi-slice CT imaging features for predicting risk stratification in patients with primary gastric GISTs. METHODS The clinicopathological and CT imaging data for 147 patients with histologically confirmed primary gastric GISTs were retrospectively analyzed. All patients had received dynamic contrast-enhanced CT (CECT) followed by surgical resection. According to the modified National Institutes of Health criteria, 147 lesions were classified into the low malignant potential group (very low and low risk; 101 lesions) and high malignant potential group (medium and high-risk; 46 lesions). The association between malignant potential and CT characteristic features (including tumor location, size, growth pattern, contour, ulceration, cystic degeneration or necrosis, calcification within the tumor, lymphadenopathy, enhancement patterns, unenhanced CT and CECT attenuation value, and enhancement degree) was analyzed using univariate analysis. Multivariate logistic regression analysis was performed to identify significant predictors of high malignant potential. The receiver operating curve (ROC) was used to evaluate the predictive value of tumor size and the multinomial logistic regression model for risk classification. RESULTS There were 46 patients with high malignant potential and 101 with low-malignant potential gastric GISTs. Univariate analysis showed no significant differences in age, gender, tumor location, calcification, unenhanced CT and CECT attenuation values, and enhancement degree between the two groups (P > 0.05). However, a significant difference was observed in tumor size (3.14 +/- 0.94 vs 6.63 +/- 3.26 cm, P < 0.001) between the low-grade and high- grade groups. The univariate analysis further revealed that CT imaging features, including tumor contours, lesion growth patterns, ulceration, cystic degeneration or necrosis, lymphadenopathy, and contrast enhancement patterns, were associated with risk stratification (P < 0.05). According to binary logistic regression analysis, tumor size [P < 0.001; odds ratio (OR) = 26.448; 95% confidence interval (CI): 4.854-144.099)], contours ( P = 0.028; OR = 7.750; 95% CI: 1.253- 47.955), and mixed growth pattern (P = 0.046; OR = 4.740; 95%CI: 1.029-21.828) were independent predictors for risk stratification of gastric GISTs. ROC curve analysis for the multinomial logistic regression model and tumor size to differentiate high-malignant potential from low-malignant potential GISTs achieved a maximum area under the curve of 0.919 (95%CI: 0.863-0.975) and 0.940 (95%CI: 0.893-0.986), respectively. The tumor size cutoff value between the low and high malignant potential groups was 4.05 cm, and the sensitivity and specificity were 93.5% and 84.2%, respectively. CONCLUSION CT features, including tumor size, growth patterns, and lesion contours, were predictors of malignant potential for primary gastric GISTs.
引用
收藏
页码:1073 / 1085
页数:13
相关论文
共 50 条
[41]   Postoperative 320 multi-slice computed tomography in assessment of pedicle screw insertion in thoraco-lumbar fixation [J].
Salama, Alsiagy A. ;
Amin, Mohamed A. ;
Soliman, Ahmed Y. ;
El-Tantaway, Ahmed .
EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE, 2019, 50 (01)
[42]   Value of multi-slice spiral computed tomography in the diagnosis of metastatic lymph nodes and N-stage of gastric cancer [J].
Jiang, Min ;
Wang, Xiaoxiao ;
Shan, Xiuhong ;
Pan, Donggang ;
Jia, Yingjun ;
Ni, Enzhen ;
Hu, Yuan ;
Huang, Hao .
JOURNAL OF INTERNATIONAL MEDICAL RESEARCH, 2019, 47 (01) :281-292
[43]   Evaluation of Clinical Plus Imaging Features and Multidetector Computed Tomography Texture Analysis in Preoperative Risk Grade Prediction of Small Bowel Gastrointestinal Stromal Tumors [J].
Yan, Jiayi ;
Zhao, Xuesong ;
Han, Shuai ;
Wang, Ting ;
Miao, Fei .
JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY, 2018, 42 (05) :714-720
[44]   Relationship Between Computed Tomography Manifestations of Thymic Epithelial Tumors and the WHO Pathological Classification [J].
Liu, Guo-Bing ;
Qu, Yan-Juan ;
Liao, Mei-Yan ;
Hu, Hui-Juan ;
Yang, Gui-Fang ;
Zhou, Su-Jun .
ASIAN PACIFIC JOURNAL OF CANCER PREVENTION, 2012, 13 (11) :5581-5585
[45]   Risk stratification for 1-to 2-cm gastric gastrointestinal stromal tumors: visual assessment of CT and EUS high-risk features versus CT radiomics analysis [J].
Jia, Xiaoxuan ;
Wan, Lijuan ;
Chen, Xiaoshan ;
Ji, Wanying ;
Huang, Shaoqing ;
Qi, Yuangang ;
Cui, Jingjing ;
Wei, Shengcai ;
Cheng, Jin ;
Chai, Fan ;
Feng, Caizhen ;
Liu, Yulu ;
Zhang, Hongmei ;
Sun, Yingshi ;
Hong, Nan ;
Rao, Shengxiang ;
Zhang, Xinhua ;
Xiao, Youping ;
Ye, Yingjiang ;
Tang, Lei ;
Wang, Yi .
EUROPEAN RADIOLOGY, 2023, 33 (04) :2768-2778
[46]   Computed-Tomography-Based Radiomics Model for Predicting the Malignant Potential of Gastrointestinal Stromal Tumors Preoperatively: A Multi-Classifier and Multicenter Study [J].
Wang, Minhong ;
Feng, Zhan ;
Zhou, Lixiang ;
Zhang, Liang ;
Hao, Xiaojun ;
Zhai, Jian .
FRONTIERS IN ONCOLOGY, 2021, 11
[47]   Computed tomography imaging characteristics of synchronous gastrointestinal stromal tumors in patients with gastric cancer and correlation with clinicopathological findings [J].
Shi, Zhenshan ;
Zhuang, Qian .
MOLECULAR AND CLINICAL ONCOLOGY, 2015, 3 (06) :1311-1314
[48]   The Multi-slice Spiral Computed Tomography (MSCT) Features of Thin-Walled Cystic Lung Cancer with Ground Glass Opacity [J].
Yu, Yejun .
IRANIAN JOURNAL OF RADIOLOGY, 2021, 18 (02)
[49]   CT Radiomics Model for Discriminating the Risk Stratification of Gastrointestinal Stromal Tumors: A Multi-Class Classification and Multi-Center Study [J].
Chen, Zhonghua ;
Xu, Linyi ;
Zhang, Chuanmin ;
Huang, Chencui ;
Wang, Minhong ;
Feng, Zhan ;
Xiong, Yue .
FRONTIERS IN ONCOLOGY, 2021, 11
[50]   Value of radiomics model based on enhanced computed tomography in risk grade prediction of gastrointestinal stromal tumors [J].
Chu, Hairui ;
Pang, Peipei ;
He, Jian ;
Zhang, Desheng ;
Zhang, Mei ;
Qiu, Yingying ;
Li, Xiaofen ;
Lei, Pinggui ;
Fan, Bing ;
Xu, Rongchun .
SCIENTIFIC REPORTS, 2021, 11 (01)