Automated classification of pathological differentiation in head and neck squamous cell carcinoma using combined radiomics models from CET1WI and T2WI

This study aims to develop an automated radiomics-based model to grade the pathological differentiation of head and neck squamous cell carcinoma (HNSCC) and to assess the influence of various magnetic resonance imaging (MRI) sequences on the model’s performance.

We retrospectively analyzed MRI data from 256 patients across two medical centers, including both contrast-enhanced T1-weighted images (CET1WI) and T2-weighted images (T2WI). Regions of interest were delineated for radiomics feature extraction, followed by dimensionality reduction. An XGBoost classifier was then employed to build the predictive model, with its classification efficiency assessed using receiver operating characteristic curves and the area under the curve (AUC).

In validation cohort, the AUC (macro/micro) values for models utilizing CET1WI, T2WI, and the combination of CET1WI and T2WI were 0.801/0.814, 0.741/0.798, and 0.885/0.895, respectively. The AUC for the three differentiations, ranging from well-differentiated to poorly differentiated, were 0.867, 0.909, and 0.837, respectively. The macro/micro precision, recall, and F1 scores of 0.688/0.736, 0.744/0.828, and 0.685/0.779 for the CET1WI + T2WI model.

This study demonstrates that constructing a radiomics model based on CET1WI and T2WI sequences can be used to predict the pathological differentiation grading of HNSCC patients.

This study suggests that a radiomics model integrating CET1WI and T2WI MRI sequences can effectively predict the pathological differentiation of HNSCC, providing an alternative diagnostic approach through non-invasive preoperative methods.