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| The value of machine learning models based on 18F-FDG PET/CT radiomics for predicting the degree of tumour differentiation in patients with non-small cell lung cancer |
| YU Jun, LI Yang, YANG Xue, BI Xiaofeng, REN Dongdong, REN Chunling, HUANG Lei. |
| Department of Nuclear Medicine, Ningbo Mingzhou Hospital, Ningbo 315100, China |
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| Cite this article: |
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YU Jun,LI Yang,YANG Xue, et al. The value of machine learning models based on 18F-FDG PET/CT radiomics for predicting the degree of tumour differentiation in patients with non-small cell lung cancer[J]. JOURNAL OF WEZHOU MEDICAL UNIVERSITY, 2024, 54(9): 709-717.
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Abstract Objective: To investigate the value of different machine learning models based on 18F-FDG PET/CT radiomics for predicting the degree of tumour differentiation in non-small cell lung cancer (NSCLC).Methods: Three hundred and twenty-five patients with NSCLC (191 males, 134 females, aged 40-85 years) who underwent 18F-FDG PET/CT followed by radical surgery from January 2019 to August 2023 were retrospectively enrolled, including 157 patients with non-poorly differentiated and 168 patients with poorly differentiated. Patients were randomly divided into a training cohort (227 cases) and a validation cohort (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 and 10-fold cross-validation were used for feature screening. Seven machine learning models, namely decision tree (DT), random forest (RF), K-nearest neighbour (KNN), naive bayesian (NB),extreme gradient boosting (XGBoost), support vector machine (SVM), logistic regression (LR) models, were constructed based on the selected optimal feature subsets. The ROC curve analysis was used to assess the predictive ability of various models. Results: A total of 250 radiomics features were extracted from PET/CT images, and 10 radiomics features were finally screened by the LASSO algorithm and 10-fold cross-validation, including 5 PET features and 5 CT features. Among the seven machine learning models constructed, the AUCs of the DT, RF,and XGBoost models in the training cohort were 0.858, 0.951, and 0.936, respectively, and decreased to 0.594,0.694, and 0.668 in the validation cohort, with obvious overfitting. The AUCs of KNN, NB, SVM, LR models in the training cohort were 0.773, 0.759, 0.801, 0.761, and in the validation cohort were 0.680, 0.668, 0.726, 0.688,respectively, which have strong generalisation ability and stability. Conclusion: The machine learning models based on 18F-FDG PET/CT radiomics can effectively predict the degree of tumour differentiation in patients with NSCLC, and the KNN, NB, SVM and LR models have high AUCs in both the training and validation cohorts,which may help in clinical decision-making and the formulation of individualized treatment plans.
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Received: 01 January 2024
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