Related Experiment Video
Updated: Aug 9, 2026
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
[Application of CT Radiomics in Predicting Differentiation Level of Lung Adenocarcinoma]
Shuai Zhang1, Peng Han1, Suya Zhang1
1Department of Radiology, Jilin Cancer Hospital, Changchun, 130021.
Objective:
To investigate the value of prediction of the differentiation level in lung adenocarcinoma based on CT radiomics model.
Methods:
Data from 507 patients with postoperative pathological confirmed lung adenocarcinoma and clearly defined differentiation level of lung adenocarcinoma were retrospective analyzed. The enrolled cases were divided into poorly differentiation group and moderate-to-high differentiation group based on the grading criteria. CT image features were extracted, and seven machine learning algorithms were used to construct prediction models to obtain the AUC, accuracy, specificity, and sensitivity.
Results:
The poorly differentiation group consisted of 175 cases, while the moderate-to-high differentiation group had 332 cases. The XGBoost model demonstrated the best performance, with the AUC, accuracy, specificity, and sensitivity of this model on the validation set being 0.878, 0.829, 0.667, and 0.727, respectively.
Conclusion:
CT radiomics model can effectively predict the differentiation level of poorly differentiation and moderate-to-high differentiation in lung adenocarcinoma.
