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INTRODUCTION: Locoregional therapies are a mainstay of treatment for patients with neuroendocrine liver metastases (NELM), yet the optimal transarterial approach remains undefined and recent studies have raised concern over the safety of transarterial chemoembolization (TACE). METHODS: Patients with NELM who underwent TACE or transarterial embolization (TAE) at a single institution between 2000-2022 were retrospectively reviewed. Propensity score matching (PSM) controlling for age, sex, bilateral disease, tumor size, lobar embolization, grade, and extrahepatic disease was utilized to compare short- and long-term outcomes. RESULTS: Among 412 patients with NELM, 329 underwent TACE and 83 TAE. Mean age was 60.7 ± 11.1 years. Patients primarily presented with synchronous (69.2%), bilateral (84.2%), and G1 disease (48.8%) and underwent staged procedures (55.8%). Following PSM, TACE was associated with slightly worse post-procedure laboratory values, but no difference in complications compared to TAE (23.3%vs29.3%, p = 0.247). TACE was associated with improved mean PFS (21.8vs10.7 months, p = 0.002), but no difference in radiographic size, chromogranin level, or median overall survival (50.0 months vs not met, p = 0.833). CONCLUSION: Among patients with NELM, TACE was associated with similar short-term outcomes and improved PFS, but no difference in OS compared to TAE. These findings highlight the need for additional research on the optimal locoregional therapy for NELM.
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The advent of artificial intelligence (AI) is revolutionizing medicine, particularly radiology. With the development of newer models, AI applications are demonstrating improved performance and versatile utility in the clinical setting. Thoracic imaging is an area of profound interest, given the prevalence of chest imaging and the significant health implications of thoracic diseases. This review aims to highlight the promising applications of AI within thoracic imaging. It examines the role of AI, including its contributions to improving diagnostic evaluation and interpretation, enhancing workflow, and aiding in invasive procedures. Next, it further highlights the current challenges and limitations faced by AI, such as the necessity of 'big data', ethical and legal considerations, and bias in representation. Lastly, it explores the potential directions for the application of AI in thoracic radiology.
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BACKGROUND AND OBJECTIVE: Well-differentiated pancreatic neuroendocrine tumors (pNETs) are a group of rare, heterogeneous tumors that originate in the endocrine tissue of the pancreas and account for 1-2% of all pancreatic neoplasms. The majority of pNETs are non-functional and typically follow a more indolent course. Especially at early stages, the primary management of pNETs is surgical resection which is associated with relatively low rates of recurrence and excellent long-term prognosis. On the other hand, some patients will present with locally advanced primary tumors or low volume metastatic disease in which complete surgical resection may be more difficult to achieve and recurrence rates are significant. Unlike treatment of borderline resectable (BR) pancreatic adenocarcinoma, in which neoadjuvant treatment strategies are becoming standardized, borderline resectability is not a currently established terminology for pNETs and the optimal multidisciplinary treatment approach is poorly understood. METHODS: We performed a literature search on PubMed, Google Scholar, and ClinicalTrials.gov using keywords, including 'pancreatic neuroendocrine tumor' and 'borderline resectable'. All studies and review articles in English with full text were considered. Each publication was independently reviewed. KEY CONTENT AND FINDINGS: We introduce the concept of BR-pNETs, focusing on important criteria that should be included in their definition by balancing the feasibility of resection and the clinical utility of surgery. We suggest that extended resection, involving vascular reconstruction, adjacent organ resection, and/or liver metastasis, should be considered at experienced, high volume centers. Furthermore, we outline multidisciplinary treatment strategies, including systemic and locoregional treatment options, for optimizing outcomes for this growing patient population. CONCLUSIONS: Formalizing the definition of resectability in pNETs through multidisciplinary collaborative research will be important for standardizing the indications for multimodality treatment and aggressive surgical approaches for patients.