18F-FDG PET/CT-based radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer

Xiu-Qing Xue; Wen-Ji Yu; Xun Shi; Xiao-Liang Shao; Yue-Tao Wang

doi:10.3389/fonc.2022.911168

¹⁸F-FDG PET/CT-based radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer

Front Oncol. 2022 Aug 8:12:911168. doi: 10.3389/fonc.2022.911168. eCollection 2022.

Authors

Xiu-Qing Xue^{1

2}, Wen-Ji Yu^{3

4}, Xun Shi^{1

2}, Xiao-Liang Shao^{3

4}, Yue-Tao Wang^{3

4}

Affiliations

¹ Department of Nuclear Medicine, The First People's Hospital of Yancheng, Yancheng, China.
² The Yancheng Clinical College of Xuzhou Medical University, Yancheng, China.
³ Department of Nuclear Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, China.
⁴ Institute of Clinical Translation of Nuclear Medicine and Molecular Imaging, Soochow University, Changzhou, China.

Abstract

Objective: Lymph node metastasis (LNM) is not only one of the important factors affecting the prognosis of gastric cancer but also an important basis for treatment decisions. The purpose of this study was to investigate the value of the radiomics nomogram based on preoperative ¹⁸F-deoxyglucose (FDG) PET/CT primary lesions and clinical risk factors for predicting LNM in gastric cancer (GC).

Methods: We retrospectively analyzed radiomics features of preoperative ¹⁸F-FDG PET/CT images in 224 gastric cancer patients from two centers. The prediction model was developed in the training cohort (n = 134) and validated in the internal (n = 59) and external validation cohorts (n = 31). The least absolute shrinkage and selection operator (LASSO) regression was used to select features and build radiomics signatures. The radiomics feature score (Rad-score) was calculated and established a radiomics signature. Multivariate logistic regression analysis was used to screen independent risk factors for LNM. The minimum Akaike's information criterion (AIC) was used to select the optimal model parameters to construct a radiomics nomogram. The performance of the nomogram was assessed with calibration, discrimination, and clinical usefulness.

Results: There was no significant difference between the internal verification and external verification of the clinical data of patients (all p > 0.05). The areas under the curve (AUCs) (95% CI) for predicting LNM based on the ¹⁸F-FDG PET/CT radiomics signature in the training cohort, internal validation cohort, and external validation cohort were 0.792 (95% CI: 0.712-0.870), 0.803 (95% CI: 0.681-0.924), and 0.762 (95% CI: 0.579-0.945), respectively. Multivariate logistic regression showed that carbohydrate antigen (CA) 19-9 [OR (95% CI): 10.180 (1.267-81.831)], PET/CT diagnosis of LNM [OR (95% CI): 6.370 (2.256-17.984)], PET/CT Rad-score [OR (95% CI): 16.536 (5.506-49.660)] were independent influencing factors of LNM (all p < 0.05), and a radiomics nomogram was established based on those factors. The AUCs (95% CI) for predicting LNM were 0.861 (95% CI: 0.799-0.924), 0.889 (95% CI: 0.800-0.976), and 0.897 (95% CI: 0.683-0.948) in the training cohort, the internal validation cohort, and the external validation cohort, respectively. Decision curve analysis (DCA) indicated that the ¹⁸F-FDG PET/CT-based radiomics nomogram has good clinical utility.

Conclusions: Radiomics nomogram based on the primary tumor of ¹⁸F-FDG PET/CT could facilitate the preoperative individualized prediction of LNM, which is helpful for risk stratification in GC patients.

Keywords: gastric cancer; lymph node metastasis (LNM); nomogram; positron emission tomography - computed tomography (PET-CT); radiomics.