Prediction of pathological complete response in locally advanced head and neck squamous cell carcinoma treated with neoadjuvant chemo-immunotherapy using volumetric multisequence MRI histogram analysis.

PURPOSE: This study aimed to develop a multisequence MRI-based volumetric histogram metrics model for predicting pathological complete response (pCR) in advanced head and neck squamous cell carcinoma (HNSCC) patients undergoing neoadjuvant chemo-immunotherapy (NCIT) and compare its predictive performance with AJCC staging and RECIST 1.1 criteria. METHODS: Twenty-four patients with locally advanced HNSCC from a prospective phase II trial were enrolled for analysis. All patients underwent pre- and post-NCIT MRI examinations from which whole-tumor histogram features were extracted, including T1WI, T2WI, enhanced T1WI (T1Gd), diffusion-weighted imaging (DWI) sequences, and their corresponding apparent diffusion coefficient (ADC) maps. The pathological results divided the patients into pathological complete response (pCR) and non-pCR (N-pCR) groups. Delta features were calculated as the percentage change in histogram features from pre- to post-treatment. After data reduction and feature selection, logistic regression was used to build prediction models. ROC analysis was performed to assess the diagnostic performance. RESULTS: Eleven of 24 patients achieved pCR. Pre_T2_original_firstorder_Minimum, Post_ADC_original_firstorder_MeanAbsoluteDeviation, and Delta_T1Gd_original_firstorder_Skewness were associated with achieving pCR after NCIT. The Combined_Model demonstrated the best predictive performance (AUC 0.95), outperforming AJCC staging (AUC 0.52) and RECIST 1.1 (AUC 0.72). The Pre_Model (AUC 0.83) or Post-Model (AUC 0.83) had a better predictive ability than AJCC staging. CONCLUSION: Multisequence MRI-based volumetric histogram analysis can non-invasively predict the pCR status of HNSCC patients undergoing NCIT. The use of histogram features extracted from pre- and post-treatment MRI exhibits promising predictive performance and offers a novel quantitative assessment method for evaluating pCR in HNSCC patients receiving NCIT.

Differentiating sinonasal malignant melanoma from squamous cell carcinoma using DWI combined with conventional MRI.

PURPOSE: This study aimed to investigate the feasibility of diffusion-weighted imaging (DWI) in combination with conventional MRI features to differentiate sinonasal malignant melanoma (SNMM) from sinonasal squamous cell carcinoma (SNSCC). METHODS: A total of 37 patients with SNMM and 44 patients with SNSCC were retrospectively reviewed. Conventional MRI features and apparent diffusion coefficients (ADCs) were evaluated independently by two experienced head and neck radiologists. ADCs were obtained from two different regions of interest (ROIs) including maximum slice (MS) and small solid sample (SSS). Multivariate logistic regression analysis was performed to identify significant MR imaging features in discriminating between SNMM and SNSCC. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance. RESULTS: SNMMs were more frequently located in the nasal cavity, with well-defined border, T1 Septate Pattern (T1-SP) and heterogeneous T1 hyperintensity, whereas SNSCCs were more frequently located in the paranasal sinus, with homogenous T1 isointensity, ill-defined border, reticular or linear T2 hyperintensity, and pterygopalatine fossa or orbital involvement (all p < 0.05). The mean ADCs of SNMM (MS ADC, 0.85 × 10-3mm2/s; SSS ADC, 0.69 × 10-3mm2/s) were significantly lower than those of SNSCC (MS ADC, 1.05 × 10-3mm2/s; SSS ADC, 0.82 × 10-3mm2/s) (p < 0.05). With a combination of location, T1 signal intensity, reticular or linear T2 hyperintensity, and a cut-off MS ADC of 0.87 × 10-3mm2/s, the sensitivity, specificity, and AUC were 97.3%, 68.2%, and 0.89, respectively. CONCLUSION: DWI combined with conventional MRI can effectively improve the diagnostic performance in differentiating SNMM from SNSCC.