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Randomized Controlled Trial
. 2023 Jun;36(3):1029-1037.
doi: 10.1007/s10278-023-00792-2. Epub 2023 Feb 24.

Classification of Histological Types and Stages in Non-small Cell Lung Cancer Using Radiomic Features Based on CT Images

Jing Lin  1 , Yunjie Yu  2 , Xianlong Zhang  2 , Zhenglei Wang  2 , Shujuan Li  2
Affiliations
Randomized Controlled Trial

Classification of Histological Types and Stages in Non-small Cell Lung Cancer Using Radiomic Features Based on CT Images

Jing Lin et al. J Digit Imaging. 2023 Jun.

Abstract

Non-invasive diagnostic method based on radiomic features in patients with non-small cell lung cancer (NSCLC) has attracted attention. This study aimed to develop a CT image-based model for both histological typing and clinical staging of patients with NSCLC. A total of 309 NSCLC patients with 537 CT series from The Cancer Imaging Archive (TCIA) database were included in this study. All patients were randomly divided into the training set (247 patients, 425 CT series) and testing set (62 patients, 112 CT series). A total of 107 radiomic features were extracted. Four classifiers including random forest, XGBoost, support vector machine, and logistic regression were used to construct the classification model. The classification model had two output layers: histological type (adenocarcinoma, squamous cell carcinoma, and large cell) and clinical stage (I, II, and III) of NSCLC patients. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with 95% confidence interval (CI) were utilized to evaluate the performance of the model. Seven features were selected for inclusion in the classification model. The random forest model had the best classification ability compared with other classifiers. The AUC of the RF model for histological typing and clinical staging of NSCLC patients in the testing set was 0.700 (95% CI, 0.641-0.759) and 0.881 (95% CI, 0.842-0.920), respectively. The CT image-based radiomic feature model had good classification ability for both histological typing and clinical staging of patients with NSCLC.

Keywords: CT; Classification model; Clinical stage; Histological type; Non-small cell lung cancer; Radiomic feature.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Workflow of the current study
Fig. 2
Fig. 2
The receiver operator characteristic (ROC) curves of different models for histological typing and staging of non-small cell lung cancer (NSCLC) patients in the training set and testing set. A Histological typing in the training set; B histological typing in the testing set; C staging in the training set; D staging in the testing set. RF, random forest; XGB, XGBoost; SVM, support vector machine; LR, logistic regression
Fig. 3
Fig. 3
Feature importance of the random forest model
See this image and copyright information in PMC

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