The British journal of radiology · 2026
Multi-parametric dual-energy CT radiomics for differentiating laryngeal benign and malignant lesions.
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Zhan Yang, Zhang Chengxiu, Wang Yuzhe, Wang Peng, Duan Fei, Wang Yin, Cheng Jingfeng, Yang Guang, Tang Zuohua
Аннотация
OBJECTIVES: To evaluate a multi-parametric dual-energy CT (DECT) radiomics model combined with clinical factors and DECT findings for differentiating between laryngeal benign and malignant lesions.
METHODS: For this retrospective study, the radiomic models were constructed based on a whole cohort of 282 patients underwent DECT examination. The radiomics signature was selected based on reproducible features by using the least absolute shrinkage and selection operator method, and highly related radiomic, clinical features and DECT findings were selected to establish the radiomics-clinical model by using multivariable logistic regression analysis. Nomogram performance was evaluated by its discrimination, calibration, and clinical use.
RESULTS: Compared with the single-parametric DECT radiomic models, the multi-parametric model can improve the diagnostic efficacy for differentiating laryngeal benign from malignant lesions, with area under the curves (AUCs) of 0.938 in training set and 0.920 in test set, respectively. Moreover, multi-parametric DECT-based radiomics-clinical model incorporates the radiomics signature and DECT-reported lesion size showed the best diagnostic performance (AUC of 0.945) in the whole cohort, when compared with the radiomics model (AUC of 0.933, P <.05) and clinical model (AUC of 0.855, P <.05).
CONCLUSIONS: The multi-parametric DECT-based radiomics-clinical model can serve as an important modality for non-invasively differentiating laryngeal benign lesions from malignant ones.
ADVANCES IN KNOWLEDGE: This study highlights the value of DECT-based radiomics model for differentiating laryngeal benign from malignant lesions. Moreover, the combination of radiomic, clinical features and DECT findings demonstrated good performance for differential diagnosing in laryngeal lesions.
METHODS: For this retrospective study, the radiomic models were constructed based on a whole cohort of 282 patients underwent DECT examination. The radiomics signature was selected based on reproducible features by using the least absolute shrinkage and selection operator method, and highly related radiomic, clinical features and DECT findings were selected to establish the radiomics-clinical model by using multivariable logistic regression analysis. Nomogram performance was evaluated by its discrimination, calibration, and clinical use.
RESULTS: Compared with the single-parametric DECT radiomic models, the multi-parametric model can improve the diagnostic efficacy for differentiating laryngeal benign from malignant lesions, with area under the curves (AUCs) of 0.938 in training set and 0.920 in test set, respectively. Moreover, multi-parametric DECT-based radiomics-clinical model incorporates the radiomics signature and DECT-reported lesion size showed the best diagnostic performance (AUC of 0.945) in the whole cohort, when compared with the radiomics model (AUC of 0.933, P <.05) and clinical model (AUC of 0.855, P <.05).
CONCLUSIONS: The multi-parametric DECT-based radiomics-clinical model can serve as an important modality for non-invasively differentiating laryngeal benign lesions from malignant ones.
ADVANCES IN KNOWLEDGE: This study highlights the value of DECT-based radiomics model for differentiating laryngeal benign from malignant lesions. Moreover, the combination of radiomic, clinical features and DECT findings demonstrated good performance for differential diagnosing in laryngeal lesions.