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Radiomics model based on preoperative 18F-fluorodeoxyglucose PET predicts N2-3b lymph node metastasis in gastric cancer patients

Authors :
Xiu-Qing, Xue
Wen-Ji, Yu
Xiao-Liang, Shao
Xiao-Feng, Li
Rong, Niu
Fei-Fei, Zhang
Yun-Mei, Shi
Yue-Tao, Wang
Source :
Nuclear Medicine Communications. 43:340-349
Publication Year :
2021
Publisher :
Ovid Technologies (Wolters Kluwer Health), 2021.

Abstract

The aim of the study was to construct and validate 18F-fluorodeoxyglucose (18F-FDG) PET-based radiomics nomogram and use it to predict N2-3b lymph node metastasis in Chinese patients with gastric cancer (GC).A total of 127 patients with pathologically confirmed GC who underwent preoperative 18F-FDG PET/CT imaging between January 2014 and September 2020 were enrolled as subjects in this study. We use the LIFEx software to extract PET radiomic features. A radiomics signature (Rad-score) was developed with the least absolute shrinkage and selection operator algorithm. Then a prediction model, which incorporated the Rad-score and independent clinical risk factors, was constructed and presented with a radiomics nomogram. Receiver operating characteristic (ROC) analysis was used to assess the performance of Rad-score and the nomogram. Finally, decision curve analysis (DCA) was applied to evaluate the clinical usefulness of the nomogram.The PET Rad-score, which includes four selected features, was significantly related to pN2-3b (all P0.05). The prediction model, which comprised the Rad-score and carcinoembryonic antigen (CEA) level, showed good calibration and discrimination [area under the ROC curve: 0.81(95% confidence interval: 0.74-0.89), P0.001)]. The DCA also indicated that the prediction model was clinically useful.This study presents a radiomics nomogram consisting of a radiomics signature based on PET images and CEA level that can be conveniently used for personalized prediction of high-risk N2-3b metastasis in Chinese GC patients.

Details

ISSN :
01433636
Volume :
43
Database :
OpenAIRE
Journal :
Nuclear Medicine Communications
Accession number :
edsair.doi.dedup.....f2a3c0f2f8a942fa5fef88f422b05f70
Full Text :
https://doi.org/10.1097/mnm.0000000000001523