Value of CT radiomics combined with morphological features in predicting the prognosis of patients with non-small cell lung cancer(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2024年第1期
- Page:
- 18-26
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Value of CT radiomics combined with morphological features in predicting the prognosis of patients with non-small cell lung cancer
- Author(s):
- ZHOU Jie1; ZHENG Yanting1; JIANG Shuqi1; AN Jie1; QIU Shijun1; SUWAL Sushant2; HUANG Suidan2; CHEN Huai2; LI Cui3; FANG Jiaqi3
- 1. Department of Imaging, the First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510405, China
2. Department of Radiology, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510260, China 3. The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou 510405, China
- Keywords:
- Keywords: non-small cell lung cancer radiomics morphological feature prognosis survival
- PACS:
- R318；R734.2
- DOI:
- DOI:10.3969/j.issn.1005-202X.2024.01.003
- Abstract:
- 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.
Last Update: 2024-01-23