Frontiers in endocrinology · 2026
Phantom-based evaluation of radiomics feature stability for low-dose CT lung cancer screening.
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Zheng Sunyi, Yang Xiaomeng, Wang Hongren, Gao Zhipeng, Ye Pengchun, Wang Weiping, Li Wenhua, Meng Donghua, Zhang Shuai, Zhang Wenjia, Liu Houpu, Huang Shuyuan, Zhang Chunlin, Wang Jing, Hao Jihui, Cui Xiaonan
Аннотация
OBJECTIVE: To assess the stability of radiomic features derived from lung nodules under low-dose CT lung cancer screening conditions and to evaluate the influence of feature stability on model performance.
METHODS: A chest phantom containing eight simulated lung nodules was scanned on five CT scanners using varying tube voltages (100-120 kVp) and tube currents (20-60 mA·s) to simulate inter-scanner and intra-scanner variability. Nodule radiomic features were extracted accordingly and their stability was evaluated using the intraclass correlation coefficient. Stable features were grouped by hierarchical clustering, from which representative stable features were selected for each cluster. Models constructed using representative stable features and remaining unstable features were compared in two independent lung cancer screening datasets for nodule malignancy assessment and growth prediction. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC).
RESULTS: Inter-scanner variability had a greater impact on feature stability than intra-scanner variability, with tube current exerting more influence than tube voltage. Models based on representative stable features achieved better performance than those using unstable features in both the malignancy assessment cohort (AUC, 0.995 vs 0.945) and nodule growth prediction cohort (AUC, 0.799 vs 0.610). Models using representative stable features also showed smaller performance differences between training and test sets than those using unstable features.
CONCLUSION: Radiomic features with high stability under lung cancer screening conditions were associated with improved performance consistency in independent screening datasets. These findings suggest stability-informed feature selection may contribute to more reliable radiomics applications in lung cancer screening.
METHODS: A chest phantom containing eight simulated lung nodules was scanned on five CT scanners using varying tube voltages (100-120 kVp) and tube currents (20-60 mA·s) to simulate inter-scanner and intra-scanner variability. Nodule radiomic features were extracted accordingly and their stability was evaluated using the intraclass correlation coefficient. Stable features were grouped by hierarchical clustering, from which representative stable features were selected for each cluster. Models constructed using representative stable features and remaining unstable features were compared in two independent lung cancer screening datasets for nodule malignancy assessment and growth prediction. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC).
RESULTS: Inter-scanner variability had a greater impact on feature stability than intra-scanner variability, with tube current exerting more influence than tube voltage. Models based on representative stable features achieved better performance than those using unstable features in both the malignancy assessment cohort (AUC, 0.995 vs 0.945) and nodule growth prediction cohort (AUC, 0.799 vs 0.610). Models using representative stable features also showed smaller performance differences between training and test sets than those using unstable features.
CONCLUSION: Radiomic features with high stability under lung cancer screening conditions were associated with improved performance consistency in independent screening datasets. These findings suggest stability-informed feature selection may contribute to more reliable radiomics applications in lung cancer screening.