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Outline
To explore the application value of multiparametric MRI techniques, including conventional MRI, contrast-enhanced MRI (CE-MRI), and dynamic contrast-enhanced MRI (DCE-MRI), in differentiating benign from malignant thyroid nodules.
A retrospective analysis was performed on 74 patients with 89 thyroid lesions who were confirmed by surgical pathology with definite pathological results and preoperative magnetic resonance imaging. Observe the nodule boundary and whether the nodule has become cystic. Nodule size, apparent diffusion coefficient (ADC), and various quantitative parameters including volume transfer constant (Ktrans), extracellular space volume fraction outside blood vessels (Ve), vascular return constant (Kep), initial area under the gadolinium-concentration–time curve (iAUC) from DCE-MRI were measured, and recorded. Independent sample t-tests and Mann-Whitney U tests were used to compare parameters between benign and malignant thyroid nodules. Multifactorial logistic regression was employed to select independent predictive factors and establish a combined diagnostic model, with ROC curves analyzed to assess the performance of individual parameters and the combined model.
Differences in lesion margin, ADC, Ktrans, and iAUC between benign and malignant thyroid nodules were all statistically significant (P<0.05). Multivariate logistic regression with stepwise selection identified iAUC, ADC, and margin status as the optimal predictor set; the corresponding odds ratios were 0.890, 0.984, and 64.759, respectively. The area under the ROC curve for this model was 0.986, with a sensitivity of 94.1%, and a specificity of 89.4%. Internal validation demonstrated excellent generalizability, with an AUC of 0.979 in the validation cohort.
This study shows that multiparametric MRI can improve the ability to distinguish between benign and malignant thyroid nodules. A combined diagnostic model incorporating independent risk factors such as iAUC value, ADC value, and boundary clarity has a high diagnostic value.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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