DOI: 10.1097/mnm.0000000000001742 ISSN:

A radiomics nomogram based on 18F-FDG PET/CT and clinical risk factors for the prediction of peritoneal metastasis in gastric cancer

Jiageng Xie, Beihui Xue, Shuying Bian, Xiaowei Ji, Jie Lin, Xiangwu Zheng, Kun Tang
  • Radiology, Nuclear Medicine and imaging
  • General Medicine


Peritoneal metastasis (PM) is usually considered an incurable factor of gastric cancer (GC) and not fit for surgery. The aim of this study is to develop and validate an 18F-FDG PET/CT-derived radiomics model combining with clinical risk factors for predicting PM of GC.


In this retrospective study, 410 GC patients (PM − = 281, PM + = 129) who underwent preoperative 18F-FDG PET/CT images from January 2015 to October 2021 were analyzed. The patients were randomly divided into a training cohort (n = 288) and a validation cohort (n = 122). The maximum relevance and minimum redundancy (mRMR) and the least shrinkage and selection operator method were applied to select feature. Multivariable logistic regression analysis was preformed to develop the predicting model. Discrimination, calibration, and clinical usefulness were used to evaluate the performance of the nomogram.


Fourteen radiomics feature parameters were selected to construct radiomics model. The area under the curve (AUC) of the radiomics model were 0.86 [95% confidence interval (CI), 0.81–0.90] in the training cohort and 0.85 (95% CI, 0.78–0.92) in the validation cohort. After multivariable logistic regression, peritoneal effusion, mean standardized uptake value (SUVmean), carbohydrate antigen 125 (CA125) and radiomics signature showed statistically significant differences between different PM status patients(P < 0.05). They were chosen to construct the comprehensive predicting model which showed a performance with an AUC of 0.92 (95% CI, 0.89–0.95) in the training cohort and 0.92 (95% CI, 0.86–0.98) in the validation cohort, respectively.


The nomogram based on 18F-FDG PET/CT radiomics features and clinical risk factors can be potentially applied in individualized treatment strategy-making for GC patients before the surgery.

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