Hepatocellular carcinoma (HCC) is an increasingly common malignancy worldwide. Transarterial chemoembolization (TACE) is a procedure that was developed to treat HCC patients deemed unsuitable candidates for surgical resection or transplantation and has shown a survival benefit for patients with unresectable HCC. Similarly, radiofrequency ablation (RFA) and microwave ablation (MWA) have been shown to treat HCC successfully, although the rates of success are size-dependent. Head-to-head studies of TACE vs. RFA have shown some superiority of ablation vs. TACE in prolonging survival, with the greatest survival benefit conferred to individuals undergoing dual TACE and ablation therapy. The chapter will discuss the merits of combination treatment vs. monotherapy.

(1) Background: Pancreatic ductal adenocarcinoma remains one of the most lethal malignancies due to late diagnosis, aggressive tumor biology, and limited therapeutic options. Artificial intelligence has emerged as a promising tool to improve detection, risk stratification, and treatment planning. This study aims to review the current clinical applications of artificial intelligence in the management of pancreatic cancer and evaluate its translational potential. (2) Methods: A comprehensive literature search was conducted across major databases (PubMed/MEDLINE, Scopus, Web of science and Cochrane) for studies published between 2015 and 2026. Eligible studies included clinical investigations and systematic reviews reporting quantifiable outcomes related to diagnosis, staging, prognostication, and treatment response using artificial intelligence methods. (3) Results: A total of 24 studies were included, most of which were retrospective and utilized imaging, histopathology, and clinical datasets. Artificial intelligence demonstrated high diagnostic performance, particularly in imaging-based detection and lesion characterization, with several models achieving excellent accuracy. Applications in staging, surgical planning, and prognostication also showed promising results, although external validation and prospective data were limited. (4) Conclusions: Artificial intelligence has significant potential to enhance the management of pancreatic cancer, particularly as a decision-support tool. However, further prospective validation and integration into clinical workflows are required before widespread adoption.

Hepatocellular carcinoma (HCC), remains one of the most common causes of cancer-related death globally. HCC typically arises in the setting of chronic liver disease and cirrhosis and as such, treatment must be balanced between the biology of the tumor, underlying liver function and performance status of the patient. Hepatic resection is the procedure of choice in patients with high-performance status who harbor a solitary mass (regardless of size). Before the first laparoscopic hepatectomy (LH) was described as early as 1991, open hepatectomy (OH) was the only choice for surgical treatment of liver tumors. LH indications were initially based solely on tumor location, size, and type and was only used for partial resection of the anterolateral segments. Since then, LH has been shown to share the benefits of other laparoscopic procedures, such as earlier recovery and discharge, and reduced postoperative pain; these are obtained with no differences in oncologic outcomes compared to open resection. Specific to liver resection, LH can limit the volume of intraoperative blood loss, shorten portal clamp time and decrease overall and liver-specific complications. This chapter will offer an overview of standard steps are in pursuing laparoscopic liver resection, be it for a minor segmentectomy or a lobectomy.

Despite gaining traction around the world, laparoscopic approach to major hepatectomy, defined as resection of at least two neighboring hepatic segments, has not been fully adopted in Greece. We sought to describe our growing experience. Data on six patients who underwent major hepatic resection in the last year in two major urban hospitals. The predominant indication for hepatectomy in these patients were metastatic disease to the liver. Three patients had colorectal metastases, one patient had metastatic melanoma, and two patients had benign pathology. There were two males and four females, with an average age of 59 (range: 52-71 years). Four patients had a solitary lesion and two had two lesions. Four patients had received pre-hepatectomy disease-specific chemotherapy or immunotherapy. The median size of the lesion was 5 cm (range: 3.5-12 cm). Two patients underwent a right hepatic lobectomy, one a left hepatic lobectomy together with a right colectomy, and three patients underwent right anterior sectorectomy. All operations were done purely laparoscopic except one, where a hand port was employed due to the size of the lesion. Median operative time was 190 minutes and median stay at the hospital was 2 nights. No patient required intensive care unit monitoring. There was no 30-day mortality. Major laparoscopic hepatic resection can be accomplished safely in well selected patients in the current Greek health system. Patients who have a laparoscopic hepatectomy benefit from a short hospital stay, without jeopardizing the safety of the operation.

Background and Objectives: Posthepatectomy liver failure (PHLF) remains a major cause of morbidity and mortality for patients undergoing major liver resections. Recent research highlights the expanding role of machine learning (ML), a crucial subfield of artificial intelligence (AI), in optimizing risk stratification. The aim of the current study was to review, elaborate on and critically analyze the available literature regarding the use of ML-driven risk prediction models for posthepatectomy liver failure. Materials and Methods: A systematic search was conducted in the PubMed/MEDLINE, Scopus and Web of Science databases. Fifteen studies that trained and validated ML models for prediction of PHLF were further included and analyzed. Results: The available literature supports the value of ML-derived models for PHLF prediction. Perioperative clinical, laboratory and imaging features have been combined in a variety of different algorithms to provided interpretable and accurate models for identifying patients at risk of PHLF. The ML-based algorithms have consistently demonstrated high area under the curve and sensitivity values, surpassing traditionally used risk scores in predictive performance. Limitations include the small sample sizes, heterogeneity in populations included, lack of external validation and a reported poor ability to distinguish between true positive and false positive cases in several studies. Conclusions: Despite the constraints, ML-driven tools, in combination with traditional scoring systems and clinical insight, may enable early and accurate PHLF risk detection, personalized surgical planning and optimization of postoperative outcomes in liver surgery.