Ultrasound-Based Peri-nodular and Intra-nodular Habitat Radiomics for Preoperative Prediction of Lymph Node Metastasis in Papillary Thyroid Carcinoma
Xingzhi Huang, Qi Qi, Shuangting Cai, Xinchun Yuan, Pan XuIntroduction:
This study aimed to develop and validate a model integrating ultrasound-based radiomic features from intra-nodular habitats and the peri-nodular region for predicting cervical lymph node (LN) metastasis in papillary thyroid carcinoma (PTC).
Methods:
A total of 433 PTC patients (256, 111, and 66 in the training, internal, and external validation cohorts, respectively) from two institutions from January 2020 to December 2024 were retrospectively enrolled. The intra-nodular region was segmented and clustered into three heterogeneous habitats by the K-Means method, while the peri-nodular region was defined as the 1-mm area surrounding the nodule. After feature extraction and selection, a predictive model (ITH-Peri model) combining intra-nodular habitat and peri-nodular features was developed using the optimal classifier selected from five machine learning algorithms in the training cohort and was evaluated in the validation cohorts. For comparison, singlehabitat, all-habitats, intra-nodular, and peri-nodular radiomic models were constructed.
Results:
The ITH-Peri model yielded the area under the curve (AUC) of 0.857 and 0.850 in the internal and external validation cohorts, respectively. It demonstrated significantly higher discriminative ability than comparator models, whose AUCs ranged from 0.637 to 0.800 (internal validation) and from 0.641 to 0.787 (external validation) (p < 0.05). Calibration curves and the Hosmer-Lemeshow test (p = 0.307 and 0.583) confirmed the model’s good calibration, while decision curves demonstrated its higher net benefit. Additionally, the model showed markedly enhanced accuracy and sensitivity for junior radiologists.
Discussion:
The ITH-Peri model significantly outperformed comparator radiomic models in both validation cohorts, with good calibration and higher net benefit, suggesting that integrating intra-nodular habitat and peri-nodular features captures complementary predictive information. It could also improve junior radiologists’ accuracy and sensitivity, thereby supporting more precise preoperative risk stratification in clinical management.
Conclusion:
The ITH-Peri model shows promise as a non-invasive and accurate tool for preoperatively predicting cervical LN status in PTC patients.