Diagnosing postoperative lymph node metastasis in thyroid cancer with multimodal radiomics and clinical features.

[PURPOSE] This study aims to evaluate the diagnostic value of texture analysis for lymph node metastasis after thyroid cancer surgery.

[METHODS] We retrospectively analyzed patients who underwent positron emission tomography/computed tomography (PET/CT) examination before I treatment at Shanghai Tenth People's Hospital between 2017 and 2020. Clinical follow-up results were used as the criterion for determining the presence of lymph node metastasis. The study included 119 patients, who were then randomly divided into training and test groups in a 7:3 ratio. Regions of interest were identified, and radiomics features were extracted using LIFEx 7.3.0. Mann-Whitney U test and LASSO regression were employed to screen radiomics parameters for modeling. Subsequently, a nomogram model was built by combining radscore and clinical features. SPSS 26.0 software was utilized for statistical analysis, and p < 0.05 was considered statistically significant.

[RESULTS] Follow-up confirmed 54 patients with thyroid cancer lymph node metastasis and 65 patients in the non-metastasis group. A total of 119 lymph nodes were delineated. For each lesion, 164 CT texture features and 164 PET texture features were extracted, and 107 significant parameters were identified, including 16 CT texture parameters and 91 PET texture parameters. After screening, 3 CT parameters, 4 PET parameters and 12 PET/CT parameters were selected to establish three radiomic models. The AUC values were as follows: AUC (CT) = 0.730, AUC (PET) = 0.759 and AUC (PET/CT) = 0.864. We then combined clinical features and radscore to construct a nomogram, resulting in a C-index of 0.915 in the training group. In the test group, the C-index was confirmed to be 0.868.

[CONCLUSIONS] Radiomics may enhance the diagnostic efficiency of lymph node metastases after thyroid cancer surgery and could potentially assist clinicians in future diagnoses. The developed nomogram, which combines radiomics and clinical features, offers relatively high accuracy in helping clinicians assess the risk of metastasis in thyroid patients after surgery.