| Autor: |
Lu Yu, Zhen Zhang, HeQing Yi, Jin Wang, Junyi Li, Xiaofeng Wang, Hui Bai, Hong Ge, Xiaoli Zheng, Jianjiao Ni, Haoran Qi, Yong Guan, Wengui Xu, Zhengfei Zhu, Ligang Xing, Andre Dekker, Leonard Wee, Alberto Traverso, Zhaoxiang Ye, Zhiyong Yuan |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Radiation Oncology, Vol 19, Iss 1, Pp 1-11 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1748-717X |
| DOI: |
10.1186/s13014-024-02402-z |
| Popis: |
Abstract Objectives Stereotactic body radiotherapy (SBRT) is a treatment option for patients with early-stage non-small cell lung cancer (NSCLC) who are unfit for surgery. Some patients may experience distant metastasis. This study aimed to develop and validate a radiomics model for predicting distant metastasis in patients with early-stage NSCLC treated with SBRT. Methods Patients at five institutions were enrolled in this study. Radiomics features were extracted based on the PET/CT images. After feature selection in the training set (from Tianjin), CT-based and PET-based radiomics signatures were built. Models based on CT and PET signatures were built and validated using external datasets (from Zhejiang, Zhengzhou, Shandong, and Shanghai). An integrated model that included CT and PET radiomic signatures was developed. The performance of the proposed model was evaluated in terms of its discrimination, calibration, and clinical utility. Multivariate logistic regression was used to calculate the probability of distant metastases. The cutoff value was obtained using the receiver operator characteristic curve (ROC), and the patients were divided into high- and low-risk groups. Kaplan-Meier analysis was used to evaluate the distant metastasis-free survival (DMFS) of different risk groups. Results In total, 228 patients were enrolled. The median follow-up time was 31.4 (2.0-111.4) months. The model based on CT radiomics signatures had an area under the curve (AUC) of 0.819 in the training set (n = 139) and 0.786 in the external dataset (n = 89). The PET radiomics model had an AUC of 0.763 for the training set and 0.804 for the external dataset. The model combining CT and PET radiomics had an AUC of 0.835 for the training set and 0.819 for the external dataset. The combined model showed a moderate calibration and a positive net benefit. When the probability of distant metastasis was greater than 0.19, the patient was considered to be at high risk. The DMFS of patients with high- and low-risk was significantly stratified (P |
| Databáze: |
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| Externí odkaz: |
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