Value of CT radiomics combined with morphological features in predicting the prognosis of patients with non-small cell lung cancer
10.3969/j.issn.1005-202X.2024.01.003
- VernacularTitle:CT影像组学联合形态学特征模型评估非小细胞肺癌患者预后生存期的价值
- Author:
Jie ZHOU
1
;
Yanting ZHENG
;
Shuqi JIANG
;
Jie AN
;
Shijun QIU
;
Sushant SUWAL
;
Suidan HUANG
;
Huai CHEN
;
Cui LI
;
Jiaqi FANG
Author Information
1. 广州中医药大学第一附属医院影像科,广东广州 510405
- Keywords:
non-small cell lung cancer;
radiomics;
morphological feature;
prognosis;
survival
- From:
Chinese Journal of Medical Physics
2024;41(1):18-26
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the predictive value of CT radiomics and morphological features for the prognosis and survival in non-small cell lung cancer(NSCLC)patients.Methods The clinic data of 300 NSCLC patients(300 lesions)were downloaded from the Cancer Imaging Archive,with 210 randomly selected as the training set and 90 as the test set.According to the prognosis and survival,the patients were divided into two groups with survival period≤3 and＞3 years.3D Slicer software was used to delineate the regions of interest layer by layer in CT images,and the radiomics features were extracted from each region of interest.Both t-test and least absolute shrinkage and selection operator were utilized for radiomics feature screening.Three types of prediction models,namely radiomics model,morphological model and combined model,were constructed with Logistic regression,whose performances were evaluated using the receiver operating characteristic(ROC)curve.Results The differences in radiomics labels and mediastinal lymph node metastasis between the training set and the test set were statistically significant.For radiomics model,morphological model and combined model,the area under the ROC curve was 0.784(95%CI:0.722-0.847),0.734(95%CI:0.664-0.804)and 0.748(95%CI:0.680-0.815)in the training set,and 0.737(95%CI:0.630-0.844),0.665(95%CI:0.554-0.777)and 0.687(95%CI:0.578-0.797)in the test set,which demonstrated that radiomics model had the best diagnostic performance.Conclusion The CT radiomics model can effectively predict the prognosis and survival in NSCLC patients.
