Carotid Artery Stenosis on Panoramic Radiographs and CT Scans in Patients With Malignancies After Radiotherapy.

BACKGROUND/AIM: This study aimed to identify the progression of carotid artery stenosis (CAS) in patients with head and neck cancer following radiation therapy (RT) by characterizing associated risk factors. PATIENTS AND METHODS: Panoramic radiographs (OPG), computed tomography (CT) scans, cone-beam CT (CBCT) scans, and ultrasonography (US) of 69 patients with head and neck tumors were selected and analyzed to identify the presence of CAS. Data on tumor location, smoking status, hypertension (HTN), hyperlipidemia (HLD), diabetes mellitus (DM), and treatment were collected from the patients' medical records. Patients who received chemotherapy or no treatment were excluded from the study. The differential diagnosis of other radiopacities and anatomical landmarks were excluded. Patients were divided into two groups: those with CAS (group1) and those without CAS (group 2) and their clinical information was compared. RESULTS: The overall prevalence of CAS on the panoramic radiographs was 16%. Of the 69 patients, 44 underwent radiography before and after radiotherapy, only seven had mild CAS on radiographs after radiotherapy, and no significant difference in CAS was identified before and after radiotherapy. There were also no differences between the groups regarding age, sex, smoking, hypertension, diabetes mellitus, hyperlipidemia, tumor location, and RT dose before and after radiation (p>0.05). CONCLUSION: Radiotherapy does not seem to affect the prevalence of CAS, although it has been identified in some patients after radiotherapy completion.

Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer.

Medical digital twins, which represent medical assets, play a crucial role in connecting the physical world to the metaverse, enabling patients to access virtual medical services and experience immersive interactions with the real world. One serious disease that can be diagnosed and treated using this technology is cancer. However, the digitalization of such diseases for use in the metaverse is a highly complex process. To address this, this study aims to use machine learning (ML) techniques to create real-time and reliable digital twins of cancer for diagnostic and therapeutic purposes. The study focuses on four classical ML techniques that are simple and fast for medical specialists without extensive Artificial Intelligence (AI) knowledge, and meet the requirements of the Internet of Medical Things (IoMT) in terms of latency and cost. The case study focuses on breast cancer (BC), the second most prevalent form of cancer worldwide. The study also presents a comprehensive conceptual framework to illustrate the process of creating digital twins of cancer, and demonstrates the feasibility and reliability of these digital twins in monitoring, diagnosing, and predicting medical parameters.