Evaluation of Head and Neck Tumors with Functional MR Imaging

Abstract

Synopsys Head and neck (HN) cancer is one of the most common cancers worldwide. Magnetic Resonance Imaging (MRI) based diffusion and perfusion techniques enable the non-invasive assessment of tumor biology and physiology, which supplement information obtained from standard structural scans. Diffusion and perfusion MRI techniques provide novel biomarkers that can aid the monitoring pre-, during, and post-treatment stages to improve patient selection for therapeutic strategies, provide evidence for change of therapy regime, and evaluation of treatment response. This review discusses pertinent aspects of the role of diffusion and perfusion MRI and computational analysis methods in studying HN cancer.

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

“…However, different tumor types and treatment regimens may impact the results of DCE-MRI-derived parametric maps in different ways. Validation studies with histopathology [27] or other measures of tumor microenvironment [10,25,28,29] open up scope for future studies in this area. The limitations of this study are that: (1) the study had a small cohort of patients with only 2 patients with loco-regional failures, therefore an investigation with a large population is needed; (2) in this study, we made an assumption of an accurate T1 and AIF measurement and ignorable patient movements, which may affect the values of pharmacokinetic metrics and generated texture features; and (3) We did not compare the sensitivity and specificity of our method with the results from other investigators.…”

Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE‐MRI derived biomarkers in multicenter oncology trials

“… Areas showing a rapid, high concentration uptake and fast washout of the CA are generally correlated with regions of malignancy within suspicious neoplastic lesions . The kinetics of the DCE time‐course data depend on unique tumor vascular characteristics and thus the derived imaging metrics have found relevance in oncological applications …”

“…Two prime qMRI examples are diffusion‐weighted imaging (DWI) and dynamic contrast‐enhanced (DCE)‐MRI, which allow characterization of tissue cellularity inferred from water mobility and microvascular properties, derived from exogenous contrast agent (CA) kinetics, respectively. Applications of qMRI include detection of disease and its evolution in progression or response to therapies that affect the relevant biophysical property of tissue (eg, cytotoxic therapies that reduce cellularity) . These methods have been covered in excellent reviews detailing the technical aspects and their applications .…”

Advanced diffusion models in head and neck squamous cell carcinoma patients: Goodness of fit, relationships among diffusion parameters and comparison with dynamic contrast-enhanced perfusion

“…The apparent diffusion coefficient (ADC), calculated using the mono-exponential decay function of DWI signal intensity with two or more b-values, has been reported to be useful in head and neck squamous cell carcinoma (HNSCC) cases for differentiating benign and malignant tissues, and to assess therapeutic efficacy and predict treatment outcomes [4,5]. Although the biological characteristics of sinonasal and pharynx SCC are somewhat different and these two types of HNSCC also slightly differ in regard to characteristics such as causes, risk factors, frequency of lymph node and distant metastases, and treatment strategies, the utility of the ADC was reported for both types of HNSCC [4][5][6].…”

“…Although the biological characteristics of sinonasal and pharynx SCC are somewhat different and these two types of HNSCC also slightly differ in regard to characteristics such as causes, risk factors, frequency of lymph node and distant metastases, and treatment strategies, the utility of the ADC was reported for both types of HNSCC [4][5][6]. However, the water diffusion behavior in cancer tissues is complicated, and it is difficult to explain the water diffusion behavior in a completely free water diffusion model using the mono-exponential decay function.…”