Analysis on different 18F-FDG PET/CT radiomics-based machine learning models for predicting occult lymph node metasta-sis in non-small cell lung cancer
Objective To investigate the value of pre-treatment 18F-FDG PET/CT radiomic analysis in predicting occult lymph node metastasis(OLM)of non-small cell lung cancer(NSCLC),and to evaluate the influence of different machine learning models on the predictive outcomes.Methods Three hundred and twenty-four patients with NSCLC(186 males,138 females,aged 36-85 years)who underwent 18F-FDG PET/CT examination followed by radical surgery and systematic lymph node dissection at Ningbo Mingzhou Hospital from January 2019 to May 2023 were retrospectively enrolled.Among them,258 cases were OLM-negative and 66 cases were OLM-positive.Patients were randomly divided into a training set(226 cases)and a validation set(98 cases)in a 7∶3 ratio.LIFEx 7.4.3 software was used to extract the PET/CT radiomics features,and the least absolute shrinkage and selection operator(LASSO)method was used for feature screening.Three machine learning models,namely logistic regression(LR),support vector machine(SVM),and random forest(RF)models,were constructed based on the selected optimal feature subsets.The ROC curve analysis was used to assess the predictive ability of the models,and decision curve analysis(DCA)was used to analyze their clinical values.Results A total of 250 radiomics features were extracted from PET/CT images,and eight were finally screened out by the LASSO algorithm,including four PET features[histogram(HISTO)_Uniformity,grey level co-occurrence matrix(GLCM)_Difference Entropy(DE),grey level run length matrix(GLRLM)_Short Run Low Grey Level Emphasis(SRLGLE),and grey level size zone matrix(GLSZM)_Small Zone Low Grey Level Emphasis(SZLGLE)],and four CT features[morphological(MORPH)_Centre Of Mass Shift(CMS),HISTO_Quartile Coefficient of Dispersion(QCD),HISTO_Maximum Histogram Gradient(MHG),and GLSZM_Large Zone Emphasis(LZE)].Of the three constructed machine learning models,the SVM model demonstrated the most effective predictive performance,with AUCs of 0.846 and 0.849 in the training and validation sets,respectively.The LR model had AUCs of 0.696 and 0.711 in the training and validation sets,respectively;while the RF model obtained AUCs of 0.943 and 0.568 in the training and validation sets,respectively,and an obvious over-fitting phenomenon was observed.DCA demonstrated that both the SVM model and the LR model had superior clinical value and net benefit.Conclusion 18F-FDG PET/CT radiomics analysis can effectively predict the presence of OLM in patients with NSCLC,and the SVM model demonstrated the best predictive performance,which may help in clinical decision-making and the formulation of individualized treatment plans.
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