Pembrolizumab-induced vasculitis demonstrated by FDG-PET/CT.

A 76-year-old man with a history of malignant pleural mesothelioma treated with pembrolizumab underwent FDG-PET/CT for restaging. The images demonstrated FDG uptake overlying the right hepatic and splenic artery, which were new from the previous FDG-PET/CT 2.5 years prior before the patient started pembrolizumab, suspicious for vasculitis. A follow-up MRI supported the diagnosis with evidence of celiac, splenic, common hepatic, and right hepatic artery involvement. Pembrolizumab was discontinued and the patient received a short course of oral glucocorticoids. Subsequent FDG-PET/CT performed 14 months after initiation of treatment for vasculitis demonstrated resolution of vasculitis. Immune checkpoint inhibitors can cause vasculitis, which can be recognized on FDG-PET/CT and lead to appropriate treatment.

Bayesian Networks in Radiology.

A Bayesian network is a graphical model that uses probability theory to represent relationships among its variables. The model is a directed acyclic graph whose nodes represent variables, such as the presence of a disease or an imaging finding. Connections between nodes express causal influences between variables as probability values. Bayesian networks can learn their structure (nodes and connections) and/or conditional probability values from data. Bayesian networks offer several advantages: (a) they can efficiently perform complex inferences, (b) reason from cause to effect or vice versa, (c) assess counterfactual data, (d) integrate observations with canonical ("textbook") knowledge, and (e) explain their reasoning. Bayesian networks have been employed in a wide variety of applications in radiology, including diagnosis and treatment planning. Unlike deep learning approaches, Bayesian networks have not been applied to computer vision. However, hybrid artificial intelligence systems have combined deep learning models with Bayesian networks, where the deep learning model identifies findings in medical images and the Bayesian network formulates and explains a diagnosis from those findings. One can apply a Bayesian network's probabilistic knowledge to integrate clinical and imaging findings to support diagnosis, treatment planning, and clinical decision-making. This article reviews the fundamental principles of Bayesian networks and summarizes their applications in radiology. Keywords: Bayesian Network, Machine Learning, Abdominal Imaging, Musculoskeletal Imaging, Breast Imaging, Neurologic Imaging, Radiology Education Supplemental material is available for this article. © RSNA, 2023.