Zhang Li, Tian Yueli, He Yong, Li Haiyan, Wang Min, Ding Ying, Liu Yu, Zhang Yongxue, Lan Xiaoli, Cao Wei
Abstract
ObjectiveTo investigate the predictive values of 18F-FDG PET/CT image feature and metabolic parameters for the malignant potential of gastrointestinal stromal tumor (GIST).
MethodsFrom March 2014 to June 2020, the 18F-FDG PET/CT imaging and surgical pathological data of 35 patients with GIST (27 males, 8 females; age 44-84 years) from Union Hospital, Tongji Medical College, Huazhong University of Science and Technology and Zhongnan Hospital of Wuhan University were analyzed retrospectively. Patients were divided into ring-shaped uptake group and other uptake patterns group according to 18F-FDG PET/CT image feature. Fisher′s exact test was used to analyze the differences of tumor necrosis and National Institutes of Health (NIH) risk classification (short for NIH classification) between different image feature groups. Mann-Whitney U test was used to analyze the differences of SUVmax , metabolic parameters at different thresholds (2.5, 40%, 50%) of SUVmax (metabolic tumor volume (MTV; MTV2.5, MTV40%, MTV50%) and total lesion glycolysis (TLG; TLG2.5, TLG40%, TLG50%)) between different clinicopathological features (lesion location, tumor diameter, mitotic count, Ki-67, necrosis, image feature, NIH classification) groups. Spearman rank correlation analysis was used to explore the correlation between clinicopathological features and metabolic parameters. ROC curve analysis was used to distinguish NIH classification of different metabolic parameters. Delong test was used to compared differences between different AUCs.
ResultsOf 35 GIST patients, 11(31.4%) were ring-shaped uptake and 24(68.6%) were other uptake patterns, and the differences of necrosis (7/11 vs 12.5%(3/24); P=0.004) and NIH classification (11/11 vs 25.0%(6/24); P<0.001) between the two groups were significant. There were significant differences of metabolic parameters between different groups of tumor diameter, mitotic count, necrosis, image feature, NIH classification (z values: from -4.70 to -2.09, all P<0.05), while there were no significant differences of Ki-67 (z values: from -0.83 to -0.71, all P>0.05). Metabolic parameters were correlated with mitotic count, tumor diameter, necrosis, image feature and NIH classification (rs values: 0.36-0.81, all P<0.05), while was not correlated with Ki-67 (rs values: 0.12-0.14, all P>0.05). The differences of AUCs between SUVmax and MTV2.5, TLG2.5, TLG40%, TLG50%were significant (0.752, 0.856, 0.856, 0.882, 0.886; z values: 1.96-2.12, all P<0.05).
ConclusionsThe NIH classification of GIST with ring-shaped uptake on 18F-FDG PET/CT is higher and more prone to necrosis. The 18F-FDG PET/CT metabolic parameters based on different thresholds of SUVmax have certain significance for the prediction of NIH classification of GIST, and may be superior to SUVmax.
Key words:
Gastrointestinal stromal tumors; Positron-emission tomography; Tomography, X-ray computed; Fluorodeoxyglucose F18; Forecasting
Contributor Information
Zhang Li
Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
Tian Yueli
Department of Nuclear Medicine, Zhongnan Hospital of Wuhan University, Wuhan 430071, China
He Yong
Department of Nuclear Medicine, Zhongnan Hospital of Wuhan University, Wuhan 430071, China
Li Haiyan
Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
Wang Min
Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
Ding Ying
Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
Liu Yu
Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
Zhang Yongxue
Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
Lan Xiaoli
Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
Cao Wei
Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China