The utility of MRI histogram and texture analysis for the prediction of histological diagnosis in head and neck malignancies

Abstract

BackgroundTo assess the utility of histogram and texture analysis of magnetic resonance (MR) fat-suppressed T2-weighted imaging (Fs-T2WI) for the prediction of histological diagnosis of head and neck squamous cell carcinoma (SCC) and malignant lymphoma (ML).MethodsThe cases of 57 patients with SCC (45 well/moderately and 12 poorly differentiated SCC) and 10 patients with ML were retrospectively analyzed. Quantitative parameters with histogram features (relative mean signal, coefficient of variation, kurtosis and skewness) and gray-level co-occurrence matrix (GLCM) features (contrast, correlation, energy and homogeneity) were calculated using Fs-T2WI data with a manual tumor region of interest (ROI).ResultsThe following significantly different values were obtained for the total SCC versus ML groups: relative mean signal (3.65 ± 0.86 vs. 2.61 ± 0.49), contrast (72.9 ± 16.2 vs. 49.3 ± 8.7) and homogeneity (2.22 ± 0.25 × 10− 1 vs. 2.53 ± 0.12 × 10− 1). In the comparison of the SCC histological grades, the relative mean signal and contrast were significantly lower in the poorly differentiated SCC (2.89 ± 0.63, 56.2 ± 12.9) compared to the well/moderately SCC (3.85 ± 0.81, 77.5 ± 13.9). The homogeneity in poorly differentiated SCC (2.56 ± 0.15 × 10− 1) was higher than that of the well/moderately SCC (2.1 ± 0.18 × 10− 1).ConclusionsParameters obtained by histogram and texture analysis of Fs-T2WI may be useful for noninvasive prediction of histological type and grade in head and neck malignancy.

Ability of Radiomics in Differentiation of Anaplastic Oligodendroglioma From Atypical Low-Grade Oligodendroglioma Using Machine-Learning Approach

“…Radiomics has been investigated in recent studies, implying that the parameters are associated with tumor histopathology and abnormal microenvironment. The texture features calculate the image characteristics from different aspects, statistically reflecting intratumoral heterogeneity, cellular density, and level of vascularization (19)(20)(21). This theory has been verified by previous researches that the shift of texture parameters was associated with irregularity in blood vessel distribution and intratumoral hypoxia (22,23).…”

Prediction of the degree of pathological differentiation in tongue squamous cell carcinoma based on radiomics analysis of magnetic resonance images

“…Fujima. et al [29] studied the utility of the MRI histogram and texture analysis in head and neck malignancies; however, it is limited to the correlation analysis between rst-order features and apparent dispersion coe cient(ADC) values, and no external veri cation was carried out. For TSCC, larger resections generally result in a worse functional outcome [30].…”

Machine-Learning-Based Prediction of Treatment Outcomes Using MR Imaging-Derived Quantitative Tumor Information in Patients with Sinonasal Squamous Cell Carcinomas: A Preliminary Study

“…Textural features included the contrast, correlation, energy, and homogeneity, based on gray-level co-occurrence matrix (GLCM) features, namely, spatially detailed signal intensity data within the tumor ROI; GLCM were previously described the most common and sensitive texture descriptor to calculate lesion heterogeneity in greater detail from the texture data [36]. Texture parameters were calculated based on a previously reported method [37].…”