FDG PET-CT in rheumatological diseases

2-deoxy-2[18F]fluoro-D-glucose (FDG) positron emission tomography-computed tomography (PET-CT) has revolutionised oncological imaging. The cellular processes that make cancer cells visible on FDG PET-CT, also occur in a wide range of inflammatory cells; exploiting this phenomenon has led to a growth of evidence supporting the use of FDG PET-CT in a wide range of infective and inflammatory diseases. Rheumatological diseases can affect multiple sites within the musculoskeletal system alongside multi-organ extra-articular disease manifestations. Inflammation is central to these diseases, making FDG PET-CT a logical choice. In this review article, we will describe the various applications of FDG PET-CT in rheumatological diseases using illustrative examples to highlight the beneficial role of FDG PET-CT in each case.

Interference of COVID-19 Vaccination With PET/CT Leads to Unnecessary Additional Imaging in a Patient With Metastatic Cutaneous Melanoma—Case Report

The COVID-19 pandemic has widely influenced oncological imaging mainly by presenting unexpected pulmonary and mediastinal lesions. The ongoing global program of vaccination has led to incidental diagnosis of axillary lymphadenopathy. We present a case of increased accumulation of 18F-FDG in an axillary lymph node in a PET/CT scan performed in a 43-year-old female patient with metastatic melanoma. The scan was performed 4 days after the AZD1222 vaccination. The occurrence of lymphadenopathy was verified with another PET/CT scan scheduled one month later. This case report presents a possible misinterpretation of PET/CT images caused by the recent COVID-19 vaccination. To avoid distress of the patient and unnecessary oncological diagnostics to verify the findings, we recommend avoiding scheduling PET/CT shortly after vaccination.

Use of PET Imaging in Neuro-Oncological Surgery

This review provides an overview of current applications and perspectives of PET imaging in neuro-oncological surgery. The past and future of PET imaging in the management of patients with glioma and brain metastases are elucidated with an emphasis on amino acid tracers, such as O-(2-[18F]fluoroethyl)-L-tyrosine (18F-FET). The thematic scope includes surgical resection planning, prognostication, non-invasive prediction of molecular tumor characteristics, depiction of intratumoral heterogeneity, response assessment, differentiation between tumor progression and treatment-related changes, and emerging new tracers. Furthermore, the role of PET using specific somatostatin receptor ligands for the management of patients with meningioma is discussed. Further advances in neuro-oncological imaging can be expected from promising new techniques, such as hybrid PET/MR scanners and the implementation of artificial intelligence methods, such as radiomics.

Distinct Imaging Modalities in Head and Neck Cancer: Benefits and Drawbacks

This review mainly focuses on the distinct imaging techniques in for head and neck cancer (HNC), its altered techniques used in diagnosis and its applications. It also depicts the upcoming imaging methods present in the field of HNC causing areas. It explains precise determination regarding the degree and extends of neoplasm. We mainly look on to the MRI(Magnetic resonance imaging), PET(Positron emission tomography), CT (computed tomography)imaging biomarkers for the management of HNC. It plays an important role in the therapy selection strategies and also enhances the therapeutic ratio in the management of HNC. The role of imaging techniques become increasingly more crucial in the management process in locally progressed head and neck squamous cell carcinoma(HNSCC). In this structure, PET allows non-invasive assessment of a range of tumour biomarkers such as metabolism, hypoxia and proliferation. MRI techniques such as can characterize different tissues by probing into their microstructure, providing a novel methodology in oncological imaging. CT, MRI, and PET/CT are widely used to determine the presence and extent of the tumours before and after treatment. This review depicts a synopsis of the most recent imaging strategies and imaging recommendations for every one of the different strides along the clinical way of patients with head and neck malignant growth.

Standardization of imaging methods for machine learning in neuro-oncology

Radiomics is a novel technique in which quantitative phenotypes or features are extracted from medical images. Machine learning enables analysis of large quantities of medical imaging data generated by radiomic feature extraction. A growing number of studies based on these methods have developed tools for neuro-oncology applications. Despite the initial promises, many of these imaging tools remain far from clinical implementation. One major limitation hindering the use of these models is their lack of reproducibility when applied across different institutions and clinical settings. In this article, we discuss the importance of standardization of methodology and reporting in our effort to improve reproducibility. Ongoing efforts of standardization for neuro-oncological imaging are reviewed. Challenges related to standardization and potential disadvantages in over-standardization are also described. Ultimately, greater multi-institutional collaborative effort is needed to provide and implement standards for data acquisition and analysis methods to facilitate research results to be interoperable and reliable for integration into different practice environments.

Imaging and Radiomics of Immuno-oncology of Primary and Secondary Gastrointestinal Malignancies

In recent years, systemic cancer treatment has been revolutionized with the advent of immunotherapy, which utilizes the body's immune system to target cancer cells and results in unique and novel imaging patterns of cancer response and therapy-associated toxicities. Hyperprogression is defined as a rapid tumor progression after treatment initiation. In contrast, pseudoprogression is defined as a tumor response after an initial increase in tumor burden, or appearance of new tumor lesions, and observed in <10% of patients undergoing PD-1/PD-L1 immunotherapy. Since traditional radiological strategies might not fully capture tumor response of patients receiving immunotherapy, several efforts have been made to better quantify specific immuno-oncological imaging patterns, including immune-related response criteria, immune-related RECIST, immunotherapy RECIST, and modified RECIST. These criteria account for potential pseudoprogression, and thus may prevent preemptive immunotherapy cessation. Immunotherapy is also associated with specific immune-related adverse events, including colitis (8–22% of patients), hypophysitis (8–13%), pneumonitis (<4%), lymphadenopathy (5–7%), hepatitis (1–7%), and pancreatitis (2%). Quantification of imaging studies using radiomic features has shown promising results in immuno-oncology, including prediction of individual patient's treatment response and survival, as well as characterization of tumoral expression of immunotherapy-relevant targets.