Application of 18F-FDG PET metabolic parameters in evaluating histopathologic grading of soft tissue sarcoma
10.3760/cma.j.cn321828-20230320-00071
- VernacularTitle:基于 18F－FDG PET代谢参数预测软组织肉瘤组织学分级的价值研究
- Author:
Bo CHEN
1
;
Tong WU
;
Hua ZHANG
;
Hongbo FENG
;
Juan TAO
;
Shaowu WANG
Author Information
1. 大连医科大学附属第一医院核医学科，大连 116011
- Keywords:
Sarcoma;
Neoplasm grading;
Positron-emission tomography;
Fluorodeoxyglucose F18;
Forecasting
- From:
Chinese Journal of Nuclear Medicine and Molecular Imaging
2024;44(3):141-146
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To evaluate the value of 18F-FDG PET metabolic parameters in predicting histopathological grade of soft tissue sarcoma (STS). Methods:From December 2012 to December 2021, 51 patients (26 males, 25 females, age range: 32-84 years) who underwent 18F-FDG PET/CT imaging before treatment and confirmed STS pathologically in the First Affiliated Hospital of Dalian Medical University were retrospectively collected. 18F-FDG PET metabolic parameters SUV max, metabolic tumor volume (MTV), total lesion glycolysis (TLG) and intertumoral FDG uptake heterogeneity (IFH) were measured. Kruskal-Wallis rank sum test was used to analyze the differences in metabolic parameters among different groups and Spearman rank correlation analysis was used to analyze the correlation of each metabolic parameter and histological grade. Logistic regression was used to screen and construct the prediction model for high-grade STS. ROC curve was plotted and Delong test was used to analyze the differences among AUCs. Results:The metabolic parameters SUV max, MTV, TLG and IFH were significantly different among French Federation of Cancer Centers Sarcoma Group (FNCLCC)Ⅰ( n=8), Ⅱ( n=10) and Ⅲ ( n=33) grade groups ( H values: 16.24, 10.52, 19.29 and 16.99, all P<0.05), and each metabolic parameter was positively correlated with histological grade ( rs values: 0.58, 0.45, 0.52, and 0.62, all P<0.05). Multivariate logistic regression analysis showed that SUV max(odds ratio ( OR)=1.27, 95% CI: 1.06-1.51, P=0.009) and IFH ( OR=6.83, 95% CI: 1.44-32.27, P=0.015) were independent risk indicators for high-grade STS. The prediction model constructed by combining SUV max and IFH had better diagnostic efficacy for differentiating high-grade STS with the AUC of 0.93, and the sensitivity of 93.9%(31/33) and the specificity of 16/18, respectively. The AUC of prediction model was significant different from SUV max, MTV, TLG and IFH (AUCs: 0.81, 0.78, 0.86 and 0.85; z values: 2.69, 2.53, 1.94 and 1.97, all P<0.05). Conclusions:The metabolic parameters SUV max, MTV, TLG and IFH are valuable predictors for histological grade of STS. The combination of SUV max and IFH may be a more meaningful method than using each of the above metabolic parameters alone.
