This review addresses the peri-procedural bleeding risks in patients with cirrhosis, emphasizing the need for careful coagulation assessment and targeted correction strategies. Liver disease presents a unique hemostatic challenge, where traditional coagulation tests may not accurately predict bleeding risk, complicating the management of procedures like paracentesis, endoscopic therapy, and various interventional procedures. As such, this paper aims to provide a comprehensive analysis of current data, guidelines, and practices for managing coagulation in cirrhotic patients, with a focus on minimizing bleeding risk while avoiding unnecessary correction with blood products. The objectives of this review are threefold: first, to outline the existing evidence on bleeding risks associated with common invasive procedures in cirrhotic patients; second, to evaluate the efficacy and limitations of standard and advanced coagulation tests in predicting procedural bleeding; and third, to examine the role of blood product transfusions and other hemostatic interventions, considering potential risks and benefits in this delicate population. In doing so, this review highlights patient-specific and procedure-specific factors that influence bleeding risk and informs best practices to optimize patient outcomes. This review progresses through key procedures often performed in cirrhotic patients. The discussion begins with paracentesis, a low-risk procedure, followed by endoscopic therapy for varices, and concludes with high-risk interventions requiring advanced hemostatic considerations. Each chapter addresses procedural techniques, bleeding risk assessment, and evidence-based correction approaches. This comprehensive structure aims to guide clinicians in making informed, evidence-backed decisions in managing coagulation in cirrhosis, ultimately reducing procedural complications and improving care quality for this high-risk population.

Background/Objectives: Liver cancer is an exceedingly heterogeneous malignancy with high mortality rates, and despite extensive research, there have been no significant improvements in treatment outcomes. In the process of navigating the complex landscape of liver cancer, AI has arisen as the “knight in shining armour”, sparking hope and offering invaluable insight into early detection, diagnosis, staging, treatment selection, and post-treatment surveillance. By integrating imaging, clinical, pathological, and molecular data, AI emerges as a transformative tool that offers unique opportunities to enhance patient care. Methods: A comprehensive literature search of PubMed and Scopus, was conducted using the terms “artificial intelligence,” “machine learning,” “deep learning,” “radiomics,” and “liver cancer.” Eligible studies included peer-reviewed original research applying AI to detection, diagnosis, prognosis, treatment planning, or surveillance of liver cancer. Key findings are organized along the clinical continuum. Results: Imaging-based AI models for tumor detection were the most advanced, with several achieving diagnostic accuracy above 90% in retrospective studies. Applications for treatment decision-making are emerging, but most remain at proof-of-concept stages. Generally, few of these innovations have progressed to large-scale clinical trials or received regulatory approval, slowing their integration into clinical practice. Conclusions: This narrative review highlights AI’s potential to transform liver cancer management and addresses the ethical, regulatory, and logistical barriers to its clinical adoption, serving as a call to action for integrating AI into practice to improve patient outcomes.

The field of hepatocellular carcinoma (HCC) has faced significant change on multiple levels in the past few years. The increasing emphasis on the various HCC phenotypes and the emergence of novel, specific therapies have slowly paved the way for a personalized approach to primary liver cancer. In this light, the role of percutaneous liver biopsy of focal lesions has shifted from a purely confirmatory method to a technique capable of providing an in-depth characterization of any nodule. Cancer subtype, gene expression, the mutational profile, and tissue biomarkers might soon become widely available through biopsy. However, indications, expectations, and techniques might suffer changes as the aim of the biopsy evolves from providing minimal proof of the disease to high-quality specimens for extensive analysis. Consequently, a revamped position of tissue biopsy is expected in HCC, following the reign of non-invasive imaging-only diagnosis. Moreover, given the advances in techniques that have recently reached the spotlight, such as liquid biopsy, concomitant use of all the available methods might gather just enough data to improve therapy selection and, ultimately, outcomes. The current review aims to discuss the changing role of liver biopsy and provide an evidence-based rationale for its use in the era of precision medicine in HCC.

(1) Background: Cirrhosis is associated with frequent alterations in standard coagulation tests that do not adequately reflect hemostasis. Thromboelastography provides a global assessment of coagulation and evaluates the functional status of clotting factors, fibrinogen, platelets, and fibrinolysis. The study aimed to assess whether liver disease severity leads to progressive alterations in the thromboelastography-based assessment of coagulation. (2) Methods: Consecutive patients with cirrhosis and abnormal standard coagulation tests (at least one of International Normalized Ratio > 2, platelet count < 50 × 10³/µL, fibrinogen < 200 mg/dL) were analyzed using native thromboelastography. (3) Results: A total of 106 patients were included, of whom 69 (65.1%) had a normal thromboelastography. While the standard coagulation tests were significantly worse in patients in the Child C group (n = 62, 58.5%) than in patients staged in Child A and B, no significant differences existed between any of the thromboelastography variables. Of the 50 patients (47.1%) with an International Normalized Ratio > 2, only two patients (4%) had features of hypocoagulation, while 26% had features of hypercoagulability on thromboelastography. Patients with a platelet count < 50 × 10³/µL had significantly lower platelet function as assessed by thromboelastography, yet only eight patients (20%) met the criteria for platelet transfusion. A thromboelastography-based transfusion protocol might lead to a 94.6% reduction in blood product transfusion indications in a simulation where the included patients would require interventional procedures. (4) Conclusion: Standard coagulation tests showed a poor correlation with thromboelastography. Based on thromboelastography, patients with severe, decompensated liver disease have a preserved hemostasis balance despite abnormal standard coagulation tests. Therefore, standard coagulation tests should not be used to guide the administration of blood products in patients with cirrhosis.

The current book chapter delves into the intricacies of ultrasonographic (US) assessment in patients with advanced liver disease and portal hypertension, offering a comprehensive, clinically oriented exploration of the most frequently employed tools in this domain. The chapter seeks to familiarize readers with a standardized US approach to advanced liver disease, encompassing standard B-mode imaging, Doppler US, elastography, and contrast-enhanced US (CEUS). Beyond the fundamental techniques, the chapter discusses specific clinical scenarios, providing valuable insights into transjugular intrahepatic portosystemic shunt (TIPS) assessment, post-transplantation follow-up protocols, the nuanced evaluation of vascular liver disease, and the management of incidental and treated liver nodules. This thorough exploration caters to the needs of clinicians and researchers and serves as a practical guide for medical practitioners involved in the day-to-day care of patients with complex liver conditions. The chapter aspires to be a useful resource in the field, offering an up-to-date and insightful overview that bridges the gap between theoretical knowledge and its practical application in the dynamic landscape of liver disease assessment using ultrasonography.

Background/Objectives: Treatment allocation for hepatocellular carcinoma (HCC) is a highly complex task that requires multidisciplinary tumor board (MDT) input; however, its accessibility and reliability can vary across the healthcare system. Large language models (LLMs) have emerged as potential clinical decision-making tools to aid MDTs. Our aim was to compare different LLM-generated treatment recommendations for HCC cases with MDT decisions. Methods: We retrospectively analyzed 100 HCC cases discussed during MDT meetings in a tertiary-care hospital in Cluj-Napoca, Romania. Identical prompts and structured clinical information were offered to four different LLMs (ChatGPT-5, a customized Tumor Board ChatGPT, Gemini 2.5 Flash, and Gemini 2.5 Pro), each of which was required to provide treatment recommendations. Concordance with the MDT decisions was assessed across the first, second, and third treatment options. Inter-model differences were evaluated using Cochran’s Q and Holm-adjusted exact McNemar tests. Chance-corrected agreement was assessed using Cohen’s kappa (κ), and generalized estimating equations evaluated associations between concordance and clinical complexity. Results: First-recommendation concordance was 81% (95% CI 72.2–87.5) for GPT-5, 80% (71.1–86.7) for Gemini 2.5 Pro, 78% (68.9–85.0) for TumorBoard ChatGPT, and 63% (53.2–71.8) for Gemini 2.5 Flash; cumulative top-3 concordance reached 95%, 92%, 95%, and 86%, respectively. GPT-5 (κ = 0.754) and Gemini 2.5 Pro (κ = 0.743) showed substantial chance-corrected agreement. Gemini 2.5 Flash showed significantly lower concordance, including after adjustment for BCLC stage, Child–Pugh class, tumor burden, and treatment category (p = 0.006), while these clinical complexity variables were not significantly associated with concordance. Conclusions: LLMs showed moderate-to-substantial agreement with MDT decisions, with meaningful inter-model differences, for a complex disease, such as HCC. Our study highlights that, while the need for human oversight is essential, LLMs could serve as supportive tools for expert guidance in some clinical settings and underscore AI’s potential in enhancing liver cancer care.