关键词:

甲状腺结节, 能谱CT, 超声C-TIRADS分级, 诊断

Abstract:

Objective　To explore the value of spectral CT combined with ultrasound C-TIRADS grading in the differentiation of benign and malignant thyroid nodules. Methods　Seventy patients with thyroid nodules treated at Binzhou Medical University Hospital from May 2023 to May 2024 were selected for the retrospective study, including 26 cases of benign nodules and 44 cases of malignant nodules. All the patients took ultrasound examination and spectral CT scanning. The ages, genders, maximum nodule diameters, and spectral CT parameters were compared between the two groups. The independent predictive factors of spectral CT were selected   through univariate and multivariate analyses, and ultrasound C-TIRADS grading was introduced to construct the nomogram model. The model's stability was internally validated by the Bootstrap method with 1 000 iterations. The independent-sample t test, Mann-Whitney U test, and χ² test were used for the statistical analysis. Results　There were statistical differences in the CT spectral parameters, including iodine concentration (IC) in the arterial and venous phases, normal iodine concentration (NIC), and slope of the energy spectrum curve (λHU), as well as the maximum nodule diameter between the benign group and the malignant group (all P<0.05). The multivariate analysis revealed that arterial phase IC and venous phase NIC were independent predictive factors for determining the benign or malignant thyroid nodules (both P<0.05). A predictive model was constructed based on these variables, and its area under the curve (AUC) was 0.940. Utilizing ultrasound C-TIRADS grading for diagnosing benign or malignant thyroid nodules resulted in an AUC of 0.823. When ultrasound C-TIRADS grading was combined with spectral CT parameters to create a nomogram, its AUC was 0.982. The calibration curve demonstrated excellent calibration performance for the nomogram, with a Brier score of 0.051. The decision curve analysis indicated that the nomograms consistently provided good clinical net benefit across a wide range of threshold probabilities. The internal validation of the nomogram model using the Bootstrap method with 1 000 iterations further confirmed its robustness, yielding an average AUC of 0.961. Conclusions　The AUC of the spectral CT predictive model is higher than that of the ultrasound C-TIRADS grading. The combined model improves the performance of both spectral CT and ultrasound C-TIRADS grading in distinguishing benign and malignant thyroid nodules.

Key words:

Thyroid nodules, Spectral CT, Ultrasound C-TIRADS grading, Diagnosis