The construction of the model of pulmonary adenocarcinoma classification based on radiomics and random forest

He Ren; Zhengguang Xiao; Yijie Li; Mengting Tu; Qi Sun; Ping Lu; Yejun Cao; Hanqing Chen; Ping Li

doi:10.1117/12.3048034

11 September 2024 The construction of the model of pulmonary adenocarcinoma classification based on radiomics and random forest

He Ren, Zhengguang Xiao, Yijie Li, Mengting Tu, Qi Sun, Ping Lu, Yejun Cao, Hanqing Chen, Ping Li

Author Affiliations +

Proceedings Volume 13270, International Conference on Future of Medicine and Biological Information Engineering (MBIE 2024); 132700C (2024) https://doi.org/10.1117/12.3048034
Event: 2024 International Conference on Future of Medicine and Biological Information Engineering (MBIE 2024), 2024, Shenyang, China

Abstract

Purpose: The present study aimed to construct classification models for pulmonary adenocarcinoma using computed tomography (CT)‐based radiomics features and random forest method. Methods: A total of 289 patients with 295 lung adenocarcinomas were included in this study. A total of 1066 CT images were extracted. The final data set was randomized into the training set and validation set at the ratio of 80%:20%. A total of 1082 features were captured from a semi‐automatic segmentation method segmented lesion of a CT image. 9 optimal radiomic features obtained from root mean squared error (REMS) through cross validation and 14 radiographic characteristic features were selected to construct a random forest classification model. At the same time, compared with the results of the Support Vector Machine (SVM), Logistic Regression and C5.0 algorithm. Results: The area under the curve (AUC) scores of training feature set, radiographic characteristics feature set, and the optimal radiomic feature set for testing dataset were 0.974, 0.483, and 0.835, respectively, and the corresponding AUC values for validation dataset were 0.964, 0.915, and 0.841, separately. Conclusion: The developed random forest‐based classification models using radiomics features and radiographic features of CT showed a relatively acceptable performance in lung adenocarcinoma and could assist clinical rapid diagnosis and triage.

Citation Download Citation

He Ren, Zhengguang Xiao, Yijie Li, Mengting Tu, Qi Sun, Ping Lu, Yejun Cao, Hanqing Chen, and Ping Li "The construction of the model of pulmonary adenocarcinoma classification based on radiomics and random forest", Proc. SPIE 13270, International Conference on Future of Medicine and Biological Information Engineering (MBIE 2024), 132700C (11 September 2024); https://doi.org/10.1117/12.3048034

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KEYWORDS

Radiomics

Data modeling

Performance modeling

Computed tomography

Random forests

Artificial intelligence

Education and training

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