Radiomics analysis of MRI improves prediction of lymph node metastasis in laryngeal squamous cell carcinoma.

OBJECTIVE: To explore the role of multi-sequence magnetic resonance imaging (MRI) images in preoperative prediction of lymph node metastasis in laryngeal squamous cell carcinoma (LSCC). METHODS: Patients with LSCC undergoing open surgery and lymph node dissection were enrolled (n = 224 training, n = 96 testing). Radiomic features (n = 2394) were extracted from T1-enhanced and T2-weighted images. Features were screened using least absolute shrinkage and selection operator (LASSO) regression, and the best-performing classification model was identified among Logistic Regression, Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting Machine. An imaging biomarker-based nomogram integrating radiomic and clinical features was developed via logistic regression. RESULTS: LASSO regression identified 14 stable features (6 from T1-enhanced images, 8 from T2-weighted). The Random Forest model showed the best radiomics-only performance (area under the receiver operating characteristic curve [AUC]: 0.877 training; 0.875 testing). The combined clinical - radiomics nomogram achieved higher discrimination (AUC: 0.942 training; 0.908 testing), outperforming standalone clinical or radiomic models. CONCLUSION: The radiomic-clinical nomogram enhances preoperative prediction of cervical lymph node metastasis in LSCC, offering the potential to optimize clinical decision-making.