ArticleInsights into imaging2026
A nomogram combining clinical variables and MR imaging features for predicting response in head-neck cancer.
Article in Insights into imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectivesThis study aims to develop a multimodal nomogram to predict neoadjuvant chemoimmunotherapy (NCIT) outcomes in head and neck squamous cell carcinoma (HNSCC). MATERIALS AND
methodsTreatment-naive HNSCC patients receiving neoadjuvant NCIT were retrospectively analyzed. Clinical information, conventional MR imaging features, dynamic contrast-enhanced-MRI (DCE-MRI) parameters and ADC values were analyzed in relation to pathological complete response (pCR). The predictive accuracy of clinical and MRI parameters was evaluated using the receiver operating characteristic (ROC) curve, with the area under the curve (AUC) serving as a key metric.
resultsFollowing NCIT, 55.0% (67/122) of patients achieved pCR. Significant differences were observed in clinical variables, including tumor location, combined positive score (CPS) and neutrophil-to-lymphocyte ratio (NLR) between pCR and non-pCR groups (p < 0.05). Imaging features (tumor margin, growth pattern, T2 homogeneity, necrosis, three distinct enhancement patterns, tumor diameter and lymph node short-axis diameter) also differed significantly (p < 0.05). The enhancement pattern was the most efficient predictor of pCR (AUC = 0.83). A combined model incorporating CPS, tumor diameter, and enhancement pattern achieved an AUC of 0.86. The baseline K
conclusionsWe developed a novel combined model integrating CPS and routine pretreatment MRI features to predict NCIT response in HNSCC. The enhancement pattern was the strongest predictor of pCR, while functional MRI parameters also showed significant predictive value. CRITICAL RELEVANCE STATEMENT: This study demonstrates that systematically integrating combined positive score with routine pretreatment MRI features can effectively predict neoadjuvant chemoimmunotherapy response. These findings may help optimize therapeutic strategies for head and neck squamous cell carcinoma. KEY POINTS: Predicting neoadjuvant chemoimmunotherapy response in head and neck cancer remains challenging. A novel clinical-MRI model improves chemoimmunotherapy response prediction in head-neck cancer. The three enhancement patterns emerged as the most robust predictors.
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