Impact of neoadjuvant chemotherapy on the difficulty and outcomes of laparoscopic and robotic major liver resections for colorectal liver metastases: A propensity-score and coarsened exact-matched controlled study

Navigating Complexity in Liver Resection: A Narrative Review of Factors Influencing Intraoperative Difficulty

Background: Liver resection remains the cornerstone for curative management in primary liver malignancies. Liver surgery ranges from simple wedge resections to complex hepatectomies involving vascular or biliary reconstructions. The anatomical complexity of the liver and these varied surgical approaches create challenges in assessing operative difficulty. This literature review explores the key factors influencing operative difficulty in liver resection for primary liver malignancy across surgical techniques. Methods: A broad literature review was conducted to determine the factors that were associated with increased operative difficulty in liver resection using the Embase, PubMed, and Cochrane databases for studies published between 2000 and 2025. Results: This review identifies several patient, tumor, and surgical factors that influence operative difficulty in liver resection. Numerous difficult scoring systems were identified, yet their applicability across different operative approaches remains uncertain. Across open and minimally invasive techniques, tumor size and location are commonly used to determine complexity. However, debate remains regarding the optimal cutoff for tumor diameter. Other identified factors include the extent of resection, patient-specific variables (e.g., cirrhosis, body mass index, previous surgeries), and surgical technique. In addition, liver resection procedures classified based on the 2000 Brisbane terminology have been stratified into three groups of increasing difficulty. Conclusion: The ability to predict operative difficulty is useful for case selection, surgical planning, and risk stratification for meaningful shared decision-making. Future research should focus on refining predictive models by integrating composite measures, including patient-reported outcomes and long-term survival. A unified, validated scoring system applicable across surgical techniques could enhance consistency in clinical practice and research to improve outcomes.

Explainable artificial intelligence prediction-based model in laparoscopic liver surgery for segments 7 and 8: an international multicenter study

BACKGROUND

Artificial intelligence (AI) is becoming more useful as a decision-making and outcomes predictor tool. We have developed AI models to predict surgical complexity and the postoperative course in laparoscopic liver surgery for segments 7 and 8.

METHODS

We included patients with lesions located in segments 7 and 8 operated by minimally invasive liver surgery from an international multi-institutional database. We have employed AI models to predict surgical complexity and postoperative outcomes. Furthermore, we have applied SHapley Additive exPlanations (SHAP) to make the AI models interpretable. Finally, we analyzed the surgeries not converted to open versus those converted to open.

RESULTS

Overall, 585 patients and 22 variables were included. Multi-layer Perceptron (MLP) showed the highest performance for predicting surgery complexity and Random Forest (RF) for predicting postoperative outcomes. SHAP detected that MLP and RF gave the highest relevance to the variables "resection type" and "largest tumor size" for predicting surgery complexity and postoperative outcomes. In addition, we explored between surgeries converted to open and non-converted, finding statistically significant differences in the variables "tumor location," "blood loss," "complications," and "operation time."

CONCLUSION

We have observed how the application of SHAP allows us to understand the predictions of AI models in surgical complexity and the postoperative outcomes of laparoscopic liver surgery in segments 7 and 8.