Abilities of radiomic analysis of ultrasound images in the diagnostics of tumors of the maxillofacial region

Background. Automated quantitative analysis of radiographic phenotyping refers to a modern digital research method that allows differential diagnosis of various pathological conditions of the maxillofacial region. Radiological data reflect the characteristics of tissues and lesions, such as heterogeneity and shape, and can, alone or in combination with demographic, histological, genomic or proteomic data, be used to solve clinical problems. Ultrasound is one of the most widely used imaging techniques worldwide. Due to its safety, low cost and accessibility, it is often used as a non-invasive diagnostic and follow-up method in various applications.
The aim of the study was to evaluate the possibilities of radiomic analysis in the differential diagnosis of the maxillofacial region masses for further development of an artificial intelligence-based program that can make a preliminary diagnosis using radiomic analysis of ultrasound images.
Material and methods. Literature review, examination results of 77 patients with various pathological conditions of the maxillofacial region aged from 25 to 72 years, 56 females and 21 males (the diagnosis was confirmed radiologically and pathologically), statistical analysis of the results. Results. According to the literature review, Loïc Duron et al., 2021 proved the possibility of using radiomic analysis of ultrasound images for the diagnosis of pathological conditions of the head and neck. The most frequent cases out of 77 were neoplasms (pleomorphic adenoma) – 29 (78.39%) and cysts – 8 (21.62%) of the large salivary glands. After pathological confirmation of the diagnosis, the ultrasound images obtained were subjected to manual segmentation, then quantitative analysis using the Slicer 5.6.1 software, as a result of which radiomic features (n=120), represented by digital values, were calculated. Principal component analysis confirmed the presence of radiomic features characteristic of only one condition. Further, we selected features (n=50) with a coefficient of repeatability below 1. Of these, 5 radiomic features were characteristic of only one condition, which can be interpreted as a potential imaging biomarker for these nosologies.
Conclusion: Five imaging biomarkers for the diagnosis of pleomorphic adenomas and large salivary gland cysts were identified (Original Glcm JointAverage, Original Glrlm RunEntropy and Original Glszm GreyLevelNonUniformityNorma lized for pleomorphic adenomas, Original Glszm GreyLevelVariance and Original Glcm SumEntropy for cysts). Further research is needed to obtain more data. This radiomics model facilitates proper patient routing and selection of the optimal treatment method.
Key words: radiomics, radiomics analysis, segmentation, radiomic feature, artificial intelligence, ultrasound, biomarker
Conflicts of interest. The authors have no conflicts of interest to declare.
Funding. There was no funding for this study