Shams Iqbal

Primary liver malignancies, including hepatocellular carcinoma, intrahepatic cholangiocarcinoma, and combined hepatocellular cholangiocarcinoma, remain major causes of cancer-related morbidity and mortality worldwide. Although systemic therapies have advanced substantially, intrahepatic tumor progression, liver failure, and portal hypertension continue to drive adverse outcomes in many patients. Interventional radiology plays a central and expanding role in the multidisciplinary management of these tumors by providing image-guided locoregional therapies for local tumor control, downstaging, bridging transplantation or resection, palliation, and potential survival benefit. This narrative review summarizes current evidence and technical considerations for major locoregional approaches, including radiofrequency ablation, microwave ablation, cryoablation, transarterial chemoembolization, transarterial radioembolization, endobiliary therapies, stereotactic body radiation therapy, irreversible electroporation, histotripsy, high-intensity focused ultrasound, hepatic arterial infusion, and brachytherapy. Interventional radiology also contributes to preoperative liver optimization through portal vein embolization, liver venous deprivation, lobar radioembolization to induce contralateral hypertrophy, and, in some patients, portal decompression before hepatic resection. For hepatocellular carcinoma, ablation and transarterial therapies are integrated into stage-based treatment algorithms and may provide curative-intent treatment in some patients. In intrahepatic cholangiocarcinoma, locoregional therapies provide meaningful disease control and may prolong survival, particularly when combined with systemic therapy. In combined hepatocellular cholangiocarcinoma, treatment remains individualized because of limited prospective data and heterogeneous tumor biology. Beyond cytoreduction, locoregional therapies can modulate the tumor immune microenvironment through immunogenic cell death, antigen release, cytokine signaling, and vascular remodeling, providing a rationale for combination strategies with immune checkpoint inhibitors, anti-angiogenic agents, and targeted therapies. As treatment paradigms evolve, the future of interventional radiology in primary liver cancer will depend on appropriate patient selection, optimized dosimetry and technique, integration with molecular and immunologic biomarkers, and coordinated multidisciplinary care.

Purpose: We review burnout risk factors in interventional radiology (IR) and explore how artificial intelligence (AI) would address burnout from a workplace aspect. Materials and Methods: We performed a literature search on PubMed on risk factors for burnout in interventional radiology and AI tools to address burnout challenges. Results: IR specialists face burnout risk at personal, workplace and system levels. AI could identify burnout using demographic data and free text, alleviate administrative workload, and manage workflow. AI could also enhance procedural efficiency via automated navigation systems, reducing stress from radiation exposure. Future directions include enhanced burnout identification and medical coding for access to longitudinal data. Conclusion: AI may be a solution to addressing specific burnout risk factors in interventional radiology. Level of Evidence: No level of evidence. Review Article. Graphical

BACKGROUND The prolonged use of percutaneous cholecystostomy tubes (PCTs) in patients with acute cholecystitis, deemed inoperable, is fraught with complications. Transpapillary cholecystoduodenal stenting (TCDS) is an alternative technique that restores the physiologic outflow of bile, avoiding the need for an external drain. However, the long-term safety and efficacy of this approach remain unclear. We sought to prospectively assess the safety and efficacy of this procedure, performed via percutaneous or endoscopic approach, in high-risk patients presenting with acute cholecystitis. METHODS This prospective study included consecutive patients with acute cholecystitis and long-lasting, prohibitive surgical risk, in whom TCDS was offered at two partnering tertiary care centers between August 1, 2018, and December 31, 2022. Patients with a need for endoscopic retrograde cholangiopancreatography (ERCP) underwent ERCP-guided TCDS. In patients without a need for ERCP, a temporary PCT was followed by fluoroscopic-guided TCDS 4 weeks to 6 weeks later. Interval cholecystectomy was performed in patients who became surgical candidates later. All patients were followed up until January 1, 2023. RESULTS Transpapillary cholecystoduodenal stenting was successful in 67 (percutaneous in 45/50; endoscopic in 22/23) of 73 patients (92%) attempted. Over a median follow-up period of 17 months (7, 26), 10 patients (15%) developed stent blockage or migration; all but two had their stent successfully replaced. Five patients (7%) developed mild, self-limited pancreatitis. Five (7%) patients underwent interval cholecystectomy at a median time of 7 months. CONCLUSION Transpapillary cholecystoduodenal stenting is a safe and promising definitive alternative to chronic PCT in high-risk patients with acute cholecystitis that eliminates the discomfort and complications of long-term external drainage. LEVEL OF EVIDENCE Prognostic and Epidemiologic; Level V. Export

Liver cancer is a leading cause of cancer-related death worldwide, and its early detection and treatment are crucial for improving morbidity and mortality. Biomarkers have the potential to facilitate the early diagnosis and management of liver cancer, but identifying and implementing effective biomarkers remains a major challenge. In recent years, artificial intelligence has emerged as a promising tool in the cancer sphere, and recent literature suggests that it is very promising in facilitating biomarker use in liver cancer. This review provides an overview of the status of AI-based biomarker research in liver cancer, with a focus on the detection and implementation of biomarkers for risk prediction, diagnosis, staging, prognostication, prediction of treatment response, and recurrence of liver cancers.

Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in ”radiomics”, a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities.

Median arcuate ligament syndrome (Dunbar syndrome) Shams Iqbal, Mahesh Chaudhary