Improving outcome prediction in oropharyngeal carcinoma through the integration of diffusion-weighted magnetic resonance imaging radiomics.

BACKGROUND AND PURPOSE: Locoregional recurrence (LRR) is the primary pattern of failure in head and neck cancer (HNC) following radiation treatment (RT). Predicting an individual patient's LRR risk is crucial for pre-treatment risk stratification and treatment adaptation during RT. This study aimed to evaluate the feasibility of integrating pre-treatment and mid-treatment diffusion-weighted (DW)-MRI radiomic parameters into multivariable prognostic models for HNC.

MATERIALS AND

METHODS

A total of 178 oropharyngeal cancer (OPC) patients undergoing (chemo)radiotherapy (CRT) were analyzed on DW-MRI scans. 105 radiomic features were extracted from ADC maps. Cox regression models incorporating clinical and radiomic parameters were developed for pre-treatment and mid-treatment phases. The models' discriminative ability was assessed with the Harrel C-index after 5-fold cross-validation.

RESULTS

Gray Level Co-occurrence Matrix (GLCM)-correlation emerged as a significant pre-treatment radiomic predictor of locoregional control (LRC) with a C-index (95 % CI) of 0.66 (0.57-0.75). Significant clinical predictors included HPV status, stage, and alcohol use, yielding a C-index of 0.70 (0.62-0.78). Combining clinical and radiomic data resulted in a C-index of 0.72 (0.65-0.80), with GLCM-correlation, disease stage and alcohol use as significant predictors. The mid-treatment model, which included delta (Δ) mean ADC, stage, and additional chemotherapy, achieved a C-index of 0.74 (0.65-0.82). Internal cross-validation yielded C-indices of 0.60 (0.51-0.69), 0.56 (0.44-0.66), and 0.63 (0.54-0.73) for the clinical, combined, and mid-treatment models, respectively.

CONCLUSION

The addition of Δ ADC improves the clinical model, highlighting the potential complementary value of radiomic features in prognostic modeling.