Application of CT Radiomics in Predicting Differentiation Level of Lung Adenocarcinoma
10.12455/j.issn.1671-7104.240152
- VernacularTitle:CT影像组学在肺腺癌分化水平预测中的应用
- Author:
Shuai ZHANG
1
;
Peng HAN
;
Suya ZHANG
;
Dingli YE
;
Zhicheng HUANG
Author Information
1. 吉林省肿瘤医院放射线科,长春市,130021
- Keywords:
lung adenocarcinoma;
tumour differentiation;
CT imaging;
feature extraction;
machine learning
- From:
Chinese Journal of Medical Instrumentation
2024;48(6):591-594
- CountryChina
- Language:Chinese
-
Abstract:
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.
