European journal of radiology · 2025
Computed tomography-based absolute delta radiomics nomogram for predicting perineural invasion in hypopharyngeal squamous cell carcinoma.
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Li Jinyan, Jiang Nan, Zhang Juntao, Sun Wenyue, Wang Zhan, Sun Lixin, Wang Ximing
Аннотация
OBJECTIVE: To assess the efficacy of computed tomography (CT)-based radiomics nomogram in predicting perineural invasion (PNI) in patients with hypopharyngeal squamous cell carcinoma (HPSCC).
MATERIALS AND METHODS: Overall, 146 patients were retrospectively recruited and divided into training and test cohorts at a 7:3 ratio. Radiomics features were extracted and delta and absolute delta radiomics features were calculated. Feature selection was performed using maximum relevance minimum redundancy and least absolute shrinkage and selection operator methods. Preliminary models were built using logistic regression, and the optimal one was selected as the radiomics signature. A nomogram was constructed by combining independent clinical factors and the radiomics signature. Its performance was evaluated using the area under the curve (AUC) values of receiver operating characteristic curves, decision curve analysis (DCA), and calibration curves.
RESULTS: The radiomics signature comprised 14 absolute delta radiomics features. The nomogram, incorporating tumor thickness and radiomics signature, outperformed the other models (AUC = 0.79 and 0.78, training and test cohorts, respectively). The Delong test demonstrated that the nomogram's predictive performance was significantly higher than that of the clinical model (p < 0.05) in both cohorts. Calibration curves indicated good calibration, and the Hosmer-Lemeshow test confirmed a good fit (p = 0.969 and 0.429, training and test cohorts, respectively). DCA highlighted the nomogram's considerable clinical usefulness.
CONCLUSION: The CT-based absolute delta radiomics nomogram can noninvasively and preoperatively predict PNI status in patients with HPSCC, providing a valuable tool for clinical decision making and individualized treatment plans.
MATERIALS AND METHODS: Overall, 146 patients were retrospectively recruited and divided into training and test cohorts at a 7:3 ratio. Radiomics features were extracted and delta and absolute delta radiomics features were calculated. Feature selection was performed using maximum relevance minimum redundancy and least absolute shrinkage and selection operator methods. Preliminary models were built using logistic regression, and the optimal one was selected as the radiomics signature. A nomogram was constructed by combining independent clinical factors and the radiomics signature. Its performance was evaluated using the area under the curve (AUC) values of receiver operating characteristic curves, decision curve analysis (DCA), and calibration curves.
RESULTS: The radiomics signature comprised 14 absolute delta radiomics features. The nomogram, incorporating tumor thickness and radiomics signature, outperformed the other models (AUC = 0.79 and 0.78, training and test cohorts, respectively). The Delong test demonstrated that the nomogram's predictive performance was significantly higher than that of the clinical model (p < 0.05) in both cohorts. Calibration curves indicated good calibration, and the Hosmer-Lemeshow test confirmed a good fit (p = 0.969 and 0.429, training and test cohorts, respectively). DCA highlighted the nomogram's considerable clinical usefulness.
CONCLUSION: The CT-based absolute delta radiomics nomogram can noninvasively and preoperatively predict PNI status in patients with HPSCC, providing a valuable tool for clinical decision making and individualized treatment plans.