Texture analysis on parametric maps derived from dynamic contrast-enhanced magnetic resonance imaging in head and neck cancer

AIM: To investigate the merits of texture analysis on parametric maps derived from pharmacokinetic modeling with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as imaging biomarkers for the prediction of treatment response in patients with head and neck squamous cell carcinoma (HNSCC). METHODS: In this retrospective study, 19 HNSCC patients underwent pre- and intra-treatment DCE-MRI scans at a 1.5T MRI scanner. All patients had chemo-radiation treatment. Pharmacokinetic modeling was performed on the acquired DCE-MRI images, generating maps of volume transfer rate (K-trans) and volume fraction of the extravascular extracellular space (v(e)). Image texture analysis was then employed on maps of K-trans and v(e), generating two texture measures: Energy (E) and homogeneity. RESULTS: No significant changes were found for the mean and standard deviation for K-trans and v(e) between pre- and intra-treatment (P > 0.09). Texture analysis revealed that the imaging biomarker E of v(e) was significantly higher in intra-treatment scans, relative to pretreatment scans (P < 0.04). CONCLUSION: Chemo-radiation treatment in HNSCC significantly reduces the heterogeneity of tumors.