Ultrasonic Radiomics Utilised to Predict Lateral Neck Lymph-Node Metastasis in Papillary Thyroid Carcinoma.

Abstract

Objective: To investigate the clinical significance of preoperative prediction for detecting metastasis in lateral neck lymph nodes (LNLN) among patients diagnosed with papillary thyroid carcinoma (PTC).

Study design: Descriptive study. Place and Duration of the Study: Department of Medical Ultrasound, Wuxi People's Hospital of Nanjing Medical University, Wuxi, China, from March 2021 to March 2023.

Methodology: Data from PTC patients with complete ultrasound and clinical records were collected, including 112 patients in the training group and 49 in the validation group. Based on pathological analysis, the individuals were categorised into the LNLN metastasis- positive group (50 cases) and the LNLN metastasis-negative group (111 cases). Logistic regression analysis showed that independent factors affected gender (OR = 3.167) as well as the maximum diameter of the tumour (OR = 1.177) and LNLN metastasis. Based on the above two independent influencing factors, the clinical model and ultrasound image feature model were constructed, respectively. A total of 6 non-zero coefficients of ultrasound radiomics features were screened by the LASSO regression dimensionality reduction to construct ultrasound radiomics models.

Results: Significant differences existed in Rad scores (RS) between the LNLN metastasis-positive group and the LNLN metastasis-negative group in the training set (p <0.05). The ROC curve indicated that the combined model exhibited a significantly higher area under the curve (AUC) compared to the ultrasound radiomics model. The calibration curve demonstrated high calibration for both the ultrasound radiomics model and the combined model, in good consistency with the actual results.

Conclusion: The clinical-ultrasonic feature-radiomics model holds significant clinical value in predicting LNLN metastasis in PTC patients.

Key words: Radiomics, Lateral neck lymph node, Papillary thyroid carcinoma, Preoperative prediction, Logistic regression analysis.