Morphological, Functional and Texture Analysis Magnetic Resonance Imaging Features in the Assessment of Radiotherapy-Induced Xerostomia in Oropharyngeal Cancer

Abstract

The aim of this single-center, observational, retrospective study was to investigate magnetic resonance imaging (MRI) biomarkers for the assessment of radiotherapy (RT)-induced xerostomia. Twenty-seven patients who underwent radiation therapy for oropharyngeal cancer were divided into three groups according to the severity of their xerostomia—mild, moderate, and severe—clinically confirmed with the Common Terminology Criteria for Adverse Events (CTCAE). No severe xerostomia was found. Conventional and functional MRI (perfusion- and diffusion- weighted imaging) performed both pre- and post-RT were studied for signal intensity, mean apparent diffusion coefficient (ADC) values, k-trans, and area under the perfusion curves. Contrast-enhanced T1 images and ADC maps were imported into 3D slicer software, and salivary gland volumes were segmented. A total of 107 texture features were derived. T-Student and Wilcoxon signed-rank tests were performed on functional MRI parameters and texture analysis features to identify the differences between pre- and post-RT populations. A p-value < 0.01 was defined as acceptable. Receiver operating characteristic (ROC) curves were plotted for significant parameters to discriminate the severity of xerostomia in the pre-RT population. Conventional and functional MRI did not yield statistically significant results; on the contrary, five texture features showed significant variation between pre- and post-RT on the ADC maps, of which only informational measure of correlation 1 (IMC 1) was able to discriminate the severity of RT-induced xerostomia in the pre-RT population (area under the curve (AUC) > 0.7). Values lower than the cut-off of −1.473 × 10−11 were associated with moderate xerostomia, enabling the differentiation of mild xerostomia from moderate xerostomia with a 73% sensitivity, 75% specificity, and 75% diagnostic accuracy. Therefore, the texture feature IMC 1 on the ADC maps allowed the distinction between different degrees of severity of RT-induced xerostomia in the pre-RT population. Accordingly, texture analysis on ADC maps should be considered a useful tool to evaluate salivary gland radiosensitivity and help identify patients at risk of developing more serious xerostomia before radiation therapy is administered.

Magnetic resonance imaging in naso-oropharyngeal carcinoma: role of texture analysis in the assessment of response to radiochemotherapy, a preliminary study

“…On the contrary, limited or lacking gadolinium spread in fibrotic tissue resulting in lower image voxel values and lower Energy values accordingly. In two studies conducted by Park et al [ 26 ] and Tomita et al [ 31 , 32 ], Energy was a substantial feature in the differentiation between metastatic and benign lymph nodes, underlining how it may be useful in discriminating neoplastic tissue from non-pathological tissue. Grey Level non-Uniformity was the other significant feature which we have found in the differentiation between ‘negative’ and ‘positive’ patients on post-gadolinium T1 images and especially between patients with inflammatory edema and residual cancer.…”

“…The most difficult challenge for a widespread implementation of texture analysis processes is the lack of a standardised method due to the large number of procedures for the texture feature extraction, resulting in a large amount of information that is hardly comparable [ 24 ]. Due to the large variability of many factors in the execution of examinations, such as MRI devices with their respective parameters, filters, acquisition sequences, post-processing algorithms, as well as the type of software used (free and non-free commercially available or custom-made in-house applications), attaining standardised and reproducible results is still rather difficult [ 17 , 26 , 36 ].…”

Imaging of human papilloma virus (HPV) related oropharynx tumour: what we know to date

“…Also, the full data processing should be conducted in the shortest possible time with the possibility to analyse them by implementing the software systems where radiologists are used to visualise images. The addition of texture analysis should lead to improved diagnostic performance and in clinical practice, should be integrated with other CT, MRI and clinical parameters to better characterize the tissue investigated [ 106 , 117 ].…”

Special Issue on Artificial Intelligence in Medical Imaging: The Beginning of a New Era

“…This discipline allows us to extrapolate information by evaluating the voxels themselves, their arrangement and the relationships that exist. Along with AI, radiomics can create a system that takes an incoming image and evaluating changes in characteristics can provide a diagnosis or prognosis [17]. AI is becoming increasingly used as new approaches develop.…”