Classification of pathological subtypes of non-small-cell lung cancer based on 18F-FDG PET CT radiomics(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2023年第4期
- Page:
- 416-422
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Classification of pathological subtypes of non-small-cell lung cancer based on 18F-FDG PET CT radiomics
- Author(s):
- DAI Qian1; WANG Meng1; HUANG Gang2
- 1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 2. Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- Keywords:
- Keywords: radiomics non-small-cell lung cancer 18F-FDG PET/CT pathological subtype machine learning
- PACS:
- R318；R734.2
- DOI:
- DOI:10.3969/j.issn.1005-202X.2023.04.004
- Abstract:
- Abstract: Objective To establish a comprehensive clinical-radiomics model based on 18F-FDG PET/CT for differentiating adenocarcinoma and squamous cell carcinoma in non-small-cell lung cancer. Methods A total of 120 cases of pathologically verified adenocarcinoma (n=65) and squamous cell carcinoma (n=55) from Shanghai Chest Hospital were collected retrospectively. In addition to 1 218 and 108 radiomics signatures extracted from the preprocessed CT images and PET images, 10 clinical features were included. Chi-square test and Wilcoxon test were used to screen clinical features, and radiomic signatures were screened using Relief algorithm and least absolute shrinkage and selection operator. Six machine learning classifiers were used to build clinical, radiomics, and comprehensive models. The classification ability of the model was evaluated using receiver operating characteristic (ROC) curve and area under curve (AUC). Results The comprehensive model exhibited the highest AUC and accuracy in both training and test sets, with random forest (RF) and Bagging classifiers showing the best classification results. After 5-fold cross-validation, the AUC and accuracy of RF in the training set were 0.92±0.03, 0.86±0.06, while those of Bagging were 0.92±0.02, 0.83±0.02. In the test set, RF and Bagging also had the optimal classification performances (RF: AUC=0.92, accuracy=0.81 Bagging: AUC=0.91, accuracy=0.86). Conclusion The classification prediction model combining 18F-FDG PET/CT clinical features and radiomics signatures can be well used to distinguish adenocarcinoma and squamous cell carcinoma.
Last Update: 2023-04-25