Objective and Background: Clinically significant posthepatectomy liver failure (PHLF B+C) remains the main cause of mortality after major hepatic resection. This study aimed to establish an aspartate aminotransferase to platelet ratio combined with an albumin-bilirubin grade (APRI+ALBI), based multivariable model (MVM) to predict PHLF and compare its performance to indocyanine green clearance (ICG-R15 or ICG-PDR) and albumin-ICG evaluation (ALICE). Methods: A total of 12,056 patients from the National Surgical Quality Improvement Program database were used to generate a MVM to predict PHLF B+C. The model was determined using stepwise backwards elimination. The performance of the model was tested using receiver operating characteristic curve analysis and validated in an international cohort of 2525 patients. In 620 patients, the APRI+ALBI MVM, trained in the National Surgical Quality Improvement Program cohort, was compared with the MVM’s based on other liver function tests (ICG clearance, ALICE) by comparing the areas under the curve (AUC). Results: A MVM including APRI+ALBI, age, sex, tumor type, and extent of resection was found to predict PHLF B+C with an AUC of 0.77, with comparable performance in the validation cohort (AUC: 0.74). In direct comparison with other MVM’s based on more expensive and time-consuming liver function tests (ICG clearance, ALICE), the APRI+ALBI MVM demonstrated equal predictive potential for PHLF B+C. A smartphone application for the calculation of the APRI+ALBI MVM was designed. Conclusion: Risk assessment through the APRI+ALBI MVM for PHLF B+C increases preoperative predictive accuracy and represents a universally available and cost-effective risk assessment before hepatectomy, facilitated by a freely available smartphone app.

Summary: Background: Despite the remarkable advances that have been made in the management of colorectal cancer over recent years, the optimal treatment for colorectal liver metastases (CRLM) remains a controversial matter. Undoubtedly, such a complex disease requires a multidisciplinary approach, in which close collaboration between all specialists involved in its management is of utmost importance. Methods: A literature search was conducted in PubMed. There was no limit set to the date of publication. The main focus of the literature review is to provide a comprehensive summary of the current multidisciplinary management of CRLM while highlighting the surgical approach. Results: Assessment of resectability, evaluation of the patient’s fitness, and discussion of clinicopathological features all play a vital role in finding the most suitable treatment strategy for CRLM patients, who should all be timely discussed in the multidisciplinary tumor board, in order to decide upon the optimal therapy sequence, operative time window, and postoperative treatment. Conclusion: Although hepatic resection remains the only potentially curative treatment strategy for patients with CRLM, a multidisciplinary approach is essential for optimal treatment. A clear definition of treatment goal (curative vs. palliative) at the time of disease diagnosis determines the further therapeutic course. Preoperative estimation of liver functional reserve is a key factor in the decision-making process for CRLM resectability.

Background and Aims: The optimal treatment strategy for de novo perihilar cholangiocarcinoma (pCCA) remains debated. This study compares outcomes between liver transplantation following neoadjuvant chemoradiation (RT+LT) and liver resection (LR), with (LR+VR) and without vascular resection (LR w/o VR). Approach and Results: This single-center, retrospective study included patients with de novo perihilar cholangiocarcinoma treated at the Mayo Clinic Rochester (1993–2023) with curative-intent surgery. Patients underwent either (1) LR, classified as LR+VR or LR w/o VR, or (2) RT+LT following the transplant protocol. Overall survival (OS) and recurrence-free survival were analyzed using as-treated and intention-to-treat approaches, incorporating competing risk analysis and direct matching. In the as-treated analysis, RT+LT showed superior median OS compared with LR w/o VR (78.0 vs. 58.2 mo, p =0.03) and LR+VR (25.8 mo, p <0.001). Considering dropout rates (RT+LT: 41%, LR: 28%), intention-to-treat analysis showed no significant OS difference between LR and RT + LT (31.7 vs. 38.5 mo, p =0.19). In matched as-treated analysis, RT + LT had no significant survival benefit over LR w/o VR (50.6 vs. 140.6 mo, p =0.08) or LR+VR (25.8 mo, p =0.11). Perioperative mortality was 4% (RT+LT), 7% (LR w/o VR), and 8% (LR + VR). Conclusions: Both LR and RT+LT achieve excellent oncological outcomes in selected patients with de novo perihilar cholangiocarcinoma. Key challenges remain dropouts during neoadjuvant therapy in patients planned for LT and high perioperative mortality for patients undergoing LR. OS doubles in patients undergoing LR+VR compared with dropouts, supporting LR+VR as a viable option for patients who are ineligible for LT. Appropriate patient selection is crucial, as those not undergoing surgery suffer from a dismal prognosis.

Cholangiocarcinoma (CCA) represents a biologically and clinically heterogeneous group of biliary tract malignancies. Growing evidence underscores that the 3 major anatomic subtypes—intrahepatic (iCCA), perihilar (pCCA), and distal (dCCA)—are distinct tumor entities rather than a single disease. Each subtype differs in embryologic origin, carcinogenesis mechanisms, molecular landscape, and immunogenic profile, all of which contribute to variability in diagnosis, treatment responsiveness, and prognosis. The importance of understanding these distinctions is reinforced by the rising global incidence of CCA, which highlights the need for precision medicine approaches based on these subtypes. Recent advances have deepened our insight into subtype-specific risk factors, including chronic biliary diseases and metabolic conditions, while artificial intelligence–driven tools are improving diagnostic accuracy and risk stratification. Surgical innovations now allow for more tailored resections and perioperative care, and therapeutic strategies are increasingly guided by molecular and immune profiling. Emerging strategies for early diagnosis and chemoprevention in high-risk populations further emphasize the significance of recognizing the unique biology of each subtype. In this review, we provide a comprehensive overview of the distinct anatomic subsets of CCA, integrating their molecular and biological foundations with clinical management to inform practice and guide future research.

Portal hypertension is a key factor in posthepatectomy liver failure (PHLF). While preoperative liver function tests like APRI+ALBI assess liver function, they only partially reflect portal hypertension severity. Elevated von Willebrand factor antigen (vWF-Ag) indicates endothelial dysregulation and correlates with portal hypertension. Combining vWF-Ag with APRI+ALBI may enhance PHLF prediction. A total of 534 patients who underwent liver resection at Mayo Clinic Rochester (2020–2024) were analyzed for PHLF incidence, postoperative morbidity, and 90-day mortality. Predictive probability for PHLF was assessed using receiver operating characteristic analysis and validated in an external Austrian cohort of 283 patients (2008–2017). vWF-Ag was then integrated into the existing APRI+ALBI multivariable model. PHLF grade B/C was reported in 56 patients (10.5%). The 90-day mortality rate was 0.9% (n=5). Using 2 cutoffs (182%, 240%), vWF-Ag was found to be an independent predictive factor for PHLF grade B/C that remained statistically significant upon multivariable analysis. The combination of preoperative APRI+ALBI and vWF-Ag increased the positive predictive value to 27.9% for PHLF grade B/C in the APRI+ALBI high and vWF-Ag high group. Incorporation of vWF-Ag into the established APRI+ALBI-based multivariable model revealed a superior AUC of 0.772 for PHLF risk stratification and was successfully validated in an independent cohort (AUC=0.834). The combination of vWF-Ag with APRI+ALBI demonstrates a favorable predictive potential for PHLF risk assessment. We provide 2 pathways for clinical assessment: (1) a cutoff-based system and (2) a more complex multivariable model that can be calculated in a specifically designed smartphone application.