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

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).

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.

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.

The radiomic-clinical nomogram enhances preoperative prediction of cervical lymph node metastasis in LSCC, offering the potential to optimize clinical decision-making.