Objective: 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).
Methods: 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 .
Results: 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).
Conclusion: 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.