Deep learning habitat radiomics based on ultrasound for predicting preoperative locally progression and postoperative recurrence risk of thyroid cancer: a multicenter study

Background: Locally advanced thyroid cancer (LATC), characterized by lymph node metastasis or extrathyroidal extension, is associated with increased surgical complexity and worse prognosis compared to intrathyroidal cancer (ITC). Accurate preoperative identification of LATC is critical for optimizing surgical and postoperative management. This study aimed to develop a deep learning-based habitat radiomics (DLH) model using ultrasonographic features to predict LATC and assess recurrence risk. Methods: A retrospective cohort of 1881 thyroid cancer patients from nine medical centers. 1383 patients from eight centers were divided into a training cohort and an internal test cohort at a 7:3 ratio, while 498 patients from the remaining center served as the external test cohort. An additional prospective cohort of 130 patients serving as validation. The inclusion criteria required preoperative ultrasound examination and postoperative pathological confirmation of thyroid cancer, with exclusion of cases lacking complete clinical data. Patients were classified as LATC or intrathyroidal cancer based on postoperative pathology. Ultrasound tumor regions were manually segmented, and intratumoral subregions were delineated using K-means clustering to capture spatial heterogeneity. Peritumoral regions were generated by isotropic expansion of tumor regions of interest (ROIs). Radiomic features from intra- and peritumoral regions were used to train the DLH model. A clinical–radiomic nomogram incorporating DLH and clinical variables was constructed. Model performance was assessed using area under the curve (AUC), and recurrence-free survival (RFS) was evaluated via Kaplan–Meier and Cox regression analyses. Results: DLH was an independent predictor for both RFS and LATC (P<0.05). The nomogram achieved robust performance in preoperative LATC identification (AUC: 0.852 in the internal test cohort and 0.897 in the external test cohort). Cox regression identified DLH, tumor size, surgical approach, and lymph node metastasis count as significant RFS predictors. Conclusion: Our multicenter model effectively predicts LATC and RFS based on routine ultrasound, with potential to guide individualized treatment planning. Further validation in diverse clinical settings and longer follow-up are warranted.