Cytokine release syndrome (CRS) is a phenomenon of immune hyperactivation described in the setting of immunotherapy. Unlike other immune-related adverse events, CRS triggered by immune checkpoint inhibitors (ICIs) is not well described. The clinical characteristics and course of 25 patients with ICI-induced CRS from 2 tertiary hospitals were abstracted retrospectively from the medical records and analyzed. CRS events were confirmed by 2 independent reviewers and graded using the Lee et al. scale. The median duration of CRS was 15.0 days (Q1; Q3 6.3; 29.8) and 10 (40.0%) had multiple episodes of CRS flares. Comparing the clinical factors and biomarkers in Grades 1-2 and 3-5 CRS, we found that patients with Grades 3-5 CRS had following: (i) had longer time to fever onset [25.0 days (Q1; Q3 13.0; 136.5) vs. 3.0 days (Q1; Q3 0.0; 18.0), p=0.027]; (ii) more cardiovascular (p=0.002), neurologic (p=0.001), pulmonary (p=0.044) and rheumatic (p=0.037) involvement; (iii) lower platelet count (p=0.041) and higher urea (p=0.041) at presentation compared to patients with Grades 1-2 CRS. 7 patients (28.0%) with Grades 1-2 CRS were rechallenged using ICIs without event. 9 patients (36.0%) were treated with pulse methylprednisolone and 6 patients (24.0%) were treated with tocilizumab. Despite this, 3 patients (50%) who received tocilizumab had fatal (Grade 5) outcomes from ICI-induced CRS. Longer time to fever onset, lower platelet count and higher urea at presentation were associated with Grade 3-5 CRS. These parameters may be used to predict which patients are likely to develop severe CRS.

Invasive lobular carcinoma (ILC) has a greater tendency to metastasize to the peritoneum, retroperitoneum, and gastrointestinal (GI) tract as compared to invasive carcinoma of no special type (NST). Like primary ILC in the breast, ILC metastases are frequently infiltrative and hypometabolic, rather than mass forming and hypermetabolic in nature. This renders them difficult to detect on conventional and metabolic imaging studies. As a result, intra-abdominal ILC metastases are often detected late, with patients presenting with clinical complications such as liver failure, hydronephrosis, or bowel obstruction. In patients with known history of ILC, certain imaging features are very suggestive of infiltrative metastatic ILC. These include retroperitoneal or peritoneal nodularity and linitis plastica appearance of the bowel. Recognition of linitis plastica on imaging should prompt deep or repeat biopsies. In this pictorial review, the authors aim to familiarize readers with imaging features and pitfalls for evaluation of intra-abdominal metastatic ILC. Awareness of these will allow the radiologist to assess these patients with a high index of suspicion and aid detection of metastatic disease. Also, this can direct histopathology and immunohistochemical staining to obtain the correct diagnosis in suspected metastatic disease.

Spinal metastasis is the most common malignant disease of the spine. Recently, major advances in machine learning and artificial intelligence technology have led to their increased use in oncological imaging. The purpose of this study is to review and summarise the present evidence for artificial intelligence applications in the detection, classification and management of spinal metastasis, along with their potential integration into clinical practice. A systematic, detailed search of the main electronic medical databases was undertaken in concordance with the PRISMA guidelines. A total of 30 articles were retrieved from the database and reviewed. Key findings of current AI applications were compiled and summarised. The main clinical applications of AI techniques include image processing, diagnosis, decision support, treatment assistance and prognostic outcomes. In the realm of spinal oncology, artificial intelligence technologies have achieved relatively good performance and hold immense potential to aid clinicians, including enhancing work efficiency and reducing adverse events. Further research is required to validate the clinical performance of the AI tools and facilitate their integration into routine clinical practice.