Background: Indocyanine green (ICG) fluorescence has seen extensive application across medical and surgical fields, praised for its real-time navigation capabilities and low toxicity. Initially employed to assess liver function, ICG fluorescence is now integral to liver surgery, aiding in tumor detection, liver segmentation, and the visualization of bile leaks. This study reviews current protocols and ICG fluorescence applications in liver surgery, with a focus on optimizing timing and dosage based on clinical indications. Methods: Following PRISMA guidelines, we systematically reviewed the literature up to 27 January 2024, using PubMed and Medline to identify studies on ICG fluorescence used in liver surgery. A systematic review was performed to evaluate dosage and timing protocols for ICG administration. Results: Of 1093 initial articles, 140 studies, covering a total of 3739 patients, were included. The studies primarily addressed tumor detection (40%), liver segmentation (34.6%), and both (21.4%). The most common ICG fluorescence dose for tumor detection was 0.5 mg/kg, with administration occurring from days to weeks pre-surgery. Various near-infrared (NIR) camera systems were utilized, with the PINPOINT system most frequently cited. Tumor detection rates averaged 87.4%, with a 10.5% false-positive rate. Additional applications include the detection of bile leaks, lymph nodes, and vascular and biliary structures. Conclusions: ICG fluorescence imaging has emerged as a valuable tool in liver surgery, enhancing real-time navigation and improving clinical outcomes. Standardizing protocols could further enhance ICG fluorescence efficacy and reliability, benefitting patient care in hepatic surgeries.

Background: Post-hepatectomy liver failure (PHLF) is the most worrisome complication after a major hepatectomy and is the leading cause of postoperative mortality. The most important predictor of PHLF is the future liver remnant (FLR), the volume of the liver that will remain after the hepatectomy, representing a major concern for hepatobiliary surgeons, radiologists, and patients. Therefore, an accurate preoperative assessment of the FLR and the prediction of PHLF are crucial to minimize risks and enhance patient outcomes. Recent radiomics and deep learning models show potential in predicting PHLF and the FLR by integrating imaging and clinical data. However, most studies lack external validation and methodological homogeneity and rely on small, single-center cohorts. This review outlines current CT-based approaches for surgical risk stratification and key limitations hindering clinical translation. Methods: A literature analysis was performed on the PubMed Dataset. We reviewed original articles using the subsequent keywords: [(Artificial intelligence OR radiomics OR machine learning OR deep learning OR neural network OR texture analysis) AND liver resection AND CT]. Results: Of 153 pertinent papers found, we underlined papers about the prediction of PHLF and about the FLR. Models were built according to machine learning (ML) and deep learning (DL) automatic algorithms. Conclusions: Radiomics models seem reliable and applicable to clinical practice in the preoperative prediction of PHLF and the FLR in patients undergoing major liver surgery. Further studies are required to achieve larger validation cohorts.

Background: Acute liver injury occurs most frequently due to trauma, but it can also occur because of sepsis or drug-induced injury. This review aims to analyze artificial intelligence (AI)’s ability to detect and quantify liver injured areas in adults and pediatric patients. Methods: A literature analysis was performed on the PubMed Dataset. We selected original articles published from 2018 to 2023 and cohorts with ≥10 adults or pediatric patients. Results: Six studies counting 564 patients were collected, including 170 (30%) children and 394 adults. Four (66%) articles reported AI application after liver trauma, one (17%) after sepsis, and one (17%) due to chemotherapy. In five (83%) studies, Computed Tomography was performed, while in one (17%), FAST-UltraSound was performed. The studies reported a high diagnostic performance; in particular, three studies reported a specificity rate > 80%. Conclusions: Radiomics models seem reliable and applicable to clinical practice in patients affected by acute liver injury. Further studies are required to achieve larger validation cohorts.

Patient-specific three-dimensional (3D)-printed models are increasingly used to optimize preoperative planning in complex hepatopancreatobiliary (HPB) surgery. This single-center study evaluated their anatomical accuracy, clinical utility, cost efficiency, and educational value for surgical planning and training. Three patients with complex HPB lesions (pancreatic head adenocarcinoma, ampullary carcinoma, and giant hepatic hemangioma) were selected based on lesion size (> 3 cm) and suspected vascular or biliary involvement. Imaging data were segmented to produce multicolored 3D-printed models, which were used for preoperative simulation and surgical team training. An educational study was also conducted: 56 medical students were randomized to traditional learning (control) or to learning with a 3D-printed model (experimental) before completing an anatomy quiz. In parallel, a systematic review of the literature up to October 8, 2025, was conducted to evaluate the current evidence on the impact and applications of 3D printing in HPB surgery. All patient-specific 3D-printed models reproduced each patient’s anatomy, including tumors and their spatial relationships to vasculature and bile ducts. Model production required ~ 33 h on average, and material cost was ~ €55 per model. Students exposed to 3D-printed models scored higher on anatomy quizzes, indicating significantly better 3D spatial understanding than controls ( p < 0.001). The systematic review identified 14 studies (2014–2024; 218 patients), confirming that 3D printing improves anatomical understanding, surgical planning, and education while remaining cost-effective. Patient-specific 3D-printed models enhance surgeons’ understanding of complex anatomy, enable personalized operative planning, and advance surgical education in HPB surgery, offering a cost-effective, high-fidelity tool with promising clinical and educational impact.

The liver is the most common site of metastases from colorectal cancer (CRC), affecting up to half of patients throughout their disease course. Although contrast-enhanced computed tomography (CECT) is routinely used for staging and treatment monitoring, RECIST criteria poorly reflect biological heterogeneity and antiangiogenic therapy effects. Integrating radiological and radiomic biomarkers may enhance response evaluation and personalized treatment. This study aimed to evaluate portal-phase ring enhancement as a potential imaging biomarker of disease progression and prognosis in patients with colorectal liver metastases (CRLM) treated with bevacizumab-based chemotherapy and to explore its correlation with CT-derived radiomic features. Eighty consecutive patients with histologically confirmed CRLM treated with standard chemotherapy plus bevacizumab were retrospectively analyzed. Baseline and 3-month CECT scans were evaluated for the presence and evolution of portal-phase ring enhancement. Radiomic features were extracted and correlated with morphologic patterns, while survival outcomes were assessed using Kaplan–Meier and logistic regression analyses. Baseline portal-phase ring enhancement was observed in 32.5% of patients and was significantly associated with inferior overall survival ( p = 0.001), a finding confirmed on follow-up imaging ( p = 0.016). Among radiomic features, sphericity showed the strongest correlation with ring enhancement ( p = 0.003), yielding a modestly discriminative model. Portal-phase ring enhancement represents a reproducible imaging biomarker of poor prognosis in bevacizumab-treated CRLM. Its correlation with distinct radiomic signatures reinforces its biological plausibility as a marker of tumor aggressiveness. Integrating this feature with shape-based metrics into early imaging evaluation may refine risk stratification and personalized management.