To investigate the value of machine learning model based on ¹⁸F-FDG PET/CT radiomics features in preoperative differential diagnosis of gastric cancer (GC) and primary gastric lymphoma (PGL).

A total of 155 patients with GC (104 males, 51 females; age (59.3±12.8) years) and 82 patients with PGL (40 males, 42 females; age (56.8±14.6) years) who underwent ¹⁸F-FDG PET/CT imaging before treatment from January 2012 to December 2020 in Tianjin Medical University Cancer Institute and Hospital were included in this retrospective study. Patients were randomly divided into training set and test set by using Python3.7.1 software. Volumes of interest (VOIs) in PET and CT images were drawn and two-dimensional and three-dimensional radiomics features were extracted. Two machine learning models, including multi-layer perceptron (MLP) and support vector machine (SVM), were established based on CT radiomics features alone, PET radiomics features alone and PET/CT radiomics features to differentiate GC and PGL, respectively. The predictive performance of each model was evaluated by ROC curve analysis.

There were 166 patients in training set and 71 patients in test set. Generally, SVM machine learning model based on PET/CT radiomics features showed a trend to be superior to MLP machine learning model in the differential diagnosis of GC and PGL (PET-SVM: AUC=0.88, 95% CI: 0.83-0.94); PET/CT-MLP: AUC=0.80, 95% CI: 0.73-0.87; z=1.15, P=0.337). The AUC of PET/CT-SVM machine learning model was significantly higher than that of CT-SVM machine learning model (CT-SVM: AUC=0.74, 95% CI: 0.67-0.81; z=2.28, P=0.022).

Machine learning model based on ¹⁸F-FDG PET/CT radiomics features is expected to be a non-invasive, effective tool for preoperative differential diagnosis of GC and PGL.

Stomach neoplasms; Lymphoma; Neural networks (computer); Positron-emission tomography; Tomography, X-ray computed; Fluorodeoxyglucose F18