Establishment and validation of 18F-FET PET radiomic features-based model in predicting IDH1 genotype in gliomas
10.3760/cma.j.cn321828-20200917-00349
- VernacularTitle:基于 18F-FET PET影像组学分析预测脑胶质瘤IDH1基因表型模型的建立与验证
- Author:
Weiyan ZHOU
;
Zhirui ZHOU
;
Qi HUANG
;
Ming LI
;
Yuhua ZHU
;
Tao HUA
;
Yihui GUAN
- From:
Chinese Journal of Nuclear Medicine and Molecular Imaging
2021;41(5):275-279
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish O-(2-[ 18F]fluoroethyl)- L-tyrosine( 18F-FET) PET radiomics features-based model and investigate its predictive efficacy for isocitrate dehydrogenase type 1 (IDH1) genotyping in untreated gliomas. Methods:From November 2017 to February 2019, 58 pathologically confirmed glioma patients (36 males, 22 females; age (41.8±15.1) years) with preoperative 18F-FET PET/CT imaging in Huashan Hospital, Fudan University were retrospectively enrolled. PyRadiomics software package was used to extract 105 radiomics features. Least absolute shrinkage and selection operator (LASSO) algorithm with 5-fold cross-validation was used to build the logistic regression model. And radiomic scores (RS) of each lesion were calculated according to their weighted coefficients. The area under the receiver operating characteristic (ROC) curve was used for evaluating the predictive efficacy for IDH1 prediction. The predictive efficacies of radiomics model and traditional semi-quantitative parameters including tumor-to-background ratio (TBR; maximum TBR (TBR max), mean TBR (TBR mean), peak TBR (TBR peak)), metabolic tumor volume (MTV) and total lesion tracer uptake (TLU), were compared by Delong test. Results:Seven radiomics features including maximum 2-dimensional (2D) diameter slice, first order_maximum, first order_range, gray level co-occurrence matrix (GLCM)_joint energy, GLCM_inverse variance, gray level dependence matrix (GLDM)_dependence entropy and GLDM_large dependence low gray level emphasis were selected for the LASSO regression model building and RS calculation. ROC analysis results showed that the predictive accuracy of RS for IDH1 genotyping (mutation, n=20; wild-type, n=38) was 81.0%(47/58), with sensitivity of 65.0%(13/20), specificity of 89.5%(34/38), and area under curve (AUC) of 0.842, respectively. The traditional 18F-FET semi-quantitative parameter TLU ranked the second regarding the diagnostic performance, with accuracy of 60.3%(35/58), sensitivity of 85.0%(17/20), specificity of 47.4%(18/38), and AUC of 0.661( z=3.426, P<0.01). Conclusion:Radiomics analysis based on 18F-FET PET images can improve the predictive efficacy for IDH1 genotyping in untreated adult glioma patients.
