Complete tumor resection is crucial in oncological liver surgery, and the evaluation of intraoperative resection margins is essential to prove R0 resection. This can be challenging for hepatocellular carcinoma (HCC) due to the heterogeneity of both the tumor and background liver tissue. Label-free multiphoton microscopy (MPM) enables tissue analysis based on endogenous optical signals, and has the potential for intraoperative real-time assessment of resection planes. Matched samples of human HCC and background liver tissue from 76 patients were imaged using a multimodal approach, including coherent anti-Stokes Raman scattering, two-photon autofluorescence, and second harmonic generation. The morphological information contained in each channel was reduced to 17 texture parameters that were used for classification. A neural network model was trained on approximately 25,000 images (35 patients) and used to classify a test set of approximately 27,000 images (38 patients) as well as create maps showing the tumor border (3 patients). Label-free MPM revealed HCC growth patterns as well as steatotic and desmoplastic features. Accurate tumor recognition was achieved on low-lateral-resolution MPM images, mimicking the use of endoscopes. The model achieved a test set correct rate of 97.3% (98.2% for liver and 96.5% for tumor). Analysis of the contribution of the different nonlinear signals to the classification showed that autofluorescence plays a key role in discriminating between neoplastic and non-neoplastic tissue. In conclusion, label-free intraoperative optical histopathology of HCC has the potential to improve tumor resection margins. By implementation in endoscopes, MPM may enable on-site tissue analysis for optimization of tumor identification or characterization of liver tissue.

BACKGROUND:The oncologic value of lymphadenectomy (LND) in biliary tract cancers (BTC) remains controversial. While guidelines recommend retrieval of ≥ 6 lymph nodes to ensure accurate staging, evidence for a therapeutic survival benefit is limited. PATIENTS AND METHODS:We retrospectively analyzed 253 consecutive resections for intrahepatic cholangiocarcinoma (iCCA), perihilar cholangiocarcinoma (pCCA), and gallbladder carcinoma (GBC) at a high‑volume hepatobiliary center from a prospectively maintained database (2013-2023). Patients were stratified into no (0 nodes), limited (1-5 nodes), and extended (≥ 6 nodes) LND. Postoperative morbidity, recurrence-free survival (RFS), and overall survival (OS) were assessed with uni- and multivariable models. RESULTS:LND was performed in 47% of patients and extended LND in 52.9%. Clavien-Dindo grade ≥ III complications occurred in 69.8% with LND ≥ 6 compared with 46.4% with LND1-5 and 41.8% with no LND (p < 0.001), with longer ICU and hospital stays and more septic and pulmonary events. On multivariable analysis, LND ≥ 6 was not an independent predictor of morbidity in the overall cohort, but in the subgroup of major resections (OR 2.79, 95% CI 1.121-6.955, p = 0.027). LND extent had no independent impact on OS or RFS. CONCLUSIONS:Extended LND was associated with a higher rate of postoperative complications and was an independent risk factor in patients undergoing major hepatectomy. However, no clear survival benefit was observed. These findings may suggest that the role of LND in BTC may be primarily diagnostic and that more selective, biology-driven approaches should be considered. Prospective studies are needed for validation.

Non-rigid registration is essential for augmented reality-guided laparoscopic liver surgery, as it enables the fusion of preoperative information such as tumor location and vascular structures into the limited intraoperative view, thereby enhancing surgical navigation. A prerequisite is the accurate prediction of intraoperative liver deformation, which remains highly challenging due to factors such as large deformation caused by pneumoperitoneum, respiration and tool interaction as well as noisy intraoperative data, and limited field of view due to occlusion and constrained camera movement. To address these challenges, we introduce PIVOTS, a Preoperative to Intraoperative VOlume-To-Surface registration neural network that directly takes point clouds as input for deformation prediction. The geometric feature extraction encoder allows multi-resolution feature extraction, and the decoder, comprising inter-modality cross attention modules, enables information exchange between pre- and intraoperative features and accurate multi-level displacement prediction. We train the neural network on a large synthetic dataset created using a biomechanical simulation pipeline that explicitly targets the mentioned intraoperative challenges and validate its performance on both synthetic and real datasets. Results demonstrate superior registration performance of our method compared to baseline methods, exhibiting strong robustness against high amounts of noise, large deformation, and various levels of intraoperative visibility. The network is fast enough to run multiple times per second and directly generalizes to new patients without retraining. We publish training and test sets as evaluation benchmarks in an effort to contribute to the development of more robust liver registration methods based on volume-to-surface data. Code, docker container and datasets are available athttps://github.com/pengliu-nct/PIVOTS.

Postoperative pulmonary complications (PPCs) following liver surgery are associated with considerable morbidity and mortality. Nevertheless, data regarding the effectiveness of short-term, self-conducted preoperative respiratory training on pulmonary function and its influence on postoperative recovery are limited. Patients scheduled for liver surgery at the University Hospital Dresden, were screened for eligibility and randomized 1:1 to the intervention or control group. The intervention consisted of self-conducted respiratory training with an incentive spirometer for 14 consecutive days before surgery. Pulmonary function was assessed using longitudinal bodyplethysmography. The feasibility of the study design and clinical outcomes were evaluated. Sixty-two patients were screened, 50 participants (81

Tumor recognition was demonstrated on samples of 150 patients. Neural network classification of ninety-thousand images of colorectal liver metastases, hepatocellular carcinoma and matched liver tissue achieved a sensitivity and a specificity of 91% and 96%. (c) 2025 The Author(s)

Liver cancer, including hepatocellular carcinoma (HCC), cholangiocellular carcinoma (CCC), and metastases, presents diagnostic challenges during surgery due to its infiltrative nature. Accurate intraoperative classification and margin assessment are crucial for improving outcomes. Current methods, like frozen section analysis, are time-consuming and subjective, necessitating rapid, objective alternatives. This study assessed fiber-based attenuated total reflection infrared (ATR IR) spectroscopy combined with supervised machine learning for intraoperative liver tumor classification based on a holistic biochemical signature approach. Fresh liver tissue from 69 surgical patients was analyzed using a probe consisting of Ge ATR crystal and silver halide fibers. Supervised algorithms reliably classified normal tissue and tumor subtypes (HCC, CCC, metastases) using cross-validation and independent test sets. Normal liver tissue was distinguished primarily by differences in glycogen content and structural compactness of tumor tissue. Normal and tumor tissues were differentiated with a sensitivity of 0.89 and a specificity of 0.92. The accuracy of spectroscopic classification is 0.90. The three-group classification of tumor subtypes also yielded an average accuracy of 0.90. HCC is characterized by a higher glycogen content compared to CCC and metastases and can be identified spectroscopically with high reliability. CCC showed distinct protein-associated spectral signatures, while metastases exhibited unique profiles reflecting their different origins. In a minority of cases, misclassifications occurred, indicating potential for further refinement. Fiber-based ATR IR spectroscopy in combination with machine learning provides a rapid, objective, and highly accurate intraoperative tool for liver tumor classification. This label-free biochemical approach may enhance surgical precision and reduce recurrence risks across the full range of solid tumor entities.

Low pneumoperitoneum pressure (LPP) lowers the incidence of CO2 embolisms in minimally invasive liver resections (MILR), while higher pneumoperitoneum pressure (HPP) reduces intraoperative blood loss. This contradiction necessitates careful pressure management especially in major liver resections where intraoperative blood loss greatly impacts postoperative outcome. In this randomized non-inferiority trial, adults undergoing elective MILR for any indication will be recruited in alignment with inclusion and exclusion criteria. After given informed consent, eligible patients will be randomized to either low (≤10 mmHg) or high (≥14 mmHg) pneumoperitoneum pressure during parenchymal transection. Blood, peritoneal biopsies, and liver tissue will be sampled to evaluate intraoperative tissue damage. Sample size (n = 66 patients per group) is calculated based on the current literature. The primary study endpoint is intraoperative blood loss during the parenchymal transection phase. Secondary endpoints include CO2 embolisms, intraoperative tissue damage, operation time, morbidity, mortality, and duration of hospitalization. Minimizing intraoperative blood loss in MILR is a clinically relevant problem, which greatly impacts the procedure’s safety and influences the patient’s morbidity and mortality. HPP, exerting counter pressure to the vascular pressure, serves for bleeding control in MILR. The risk of CO2 embolism, arising from the combination of high intra-abdominal pressure and low central venous pressure, favors the use of LPP. The proposed trial aims to assess the non-inferiority of LPP compared to HPP during the parenchymal transection phase of MILR. ClinicalTrials.gov NCT06770803. First Submitted: 2024-12-30, First Submitted that Met QC Criteria: 2025-01-07, First Posted: 2025-01-13.

BACKGROUND:Posthepatectomy liver failure (PHLF) is the most fatal complication after liver resection, particularly in patients with comorbidities. This study aimed to assess the effect of long-term medication on PHLF incidence after open liver resections. METHODS:A retrospective analysis of 682 patients who underwent elective open hepatectomies between 2008 and 2015 at 2 academic centers was performed. Preoperative, intraoperative, and postoperative data were collected, including long-term medication. The risk factors for the development of PHLF and other postoperative complications were evaluated using univariate and multivariate logistic regression analyses. RESULTS:PHLF occurred in 81 patients (11.9%), with a higher incidence in patients taking diuretics as long-term medication than in those not taking diuretics (17.7% vs 5.3%, respectively; P <.001). Diuretic use was identified as a strong independent risk factor for PHLF (odds ratio [OR], 3.8 [95% CI, 2.1-7.0]; P <.001), alongside liver cirrhosis (OR, 3.8 [95% CI, 1.9-7.6]; P <.001), primary liver malignancies (OR, 3.8 [95% CI, 1.6-9.3]; P <.001), major hepatectomies (OR, 3.1 [95% CI, 1.7-5.7]; P <.001), and long operating time (OR, 4.2 [95% CI, 2.4-7.2]; P <.001). Patients with long-term diuretic intake were older, had higher body mass indices, and had more comorbidities, including liver cirrhosis. CONCLUSION:Long-term diuretic use is associated with a significantly increased risk of PHLF after open hepatectomy.

BACKGROUND:Successful liver resection in oncologic surgery depends on safety, precision, and efficacy, all of which require a thorough understanding of liver anatomy. Contrast-enhanced computed tomography (CT)-generated three-dimensional (3D) models have been proposed as a valuable tool to enhance this understanding. However, a systematic comparison of different display modalities across professional groups has not yet been performed. METHODS:In this prospective, monocentric randomized trial, we compared high-resolution two-dimensional (2D) CT images of liver malignancies with their corresponding standardized, non-colored 3D virtual and printed models in facilitating anatomical and spatial understanding as well as surgical decision-making. A total of 91 participants, including 40 surgeons, 10 radiologists, and 41 students, evaluated six clinical cases (three centrally and three peripherally located liver malignancies). Each participant assessed one central and one peripheral case per display modality, presented in a random order. RESULTS:Compared to 2D CT images, both 3D virtual and printed models significantly improved the identification of tumor location (P < 0.001), enhanced the comprehension of spatial relationships with adjacent liver and portal veins (P < 0.001 and P = 0.019, respectively), and facilitated clinical decision-making (P < 0.001). No significant difference was observed between virtual and printed models in terms of effectiveness. Within the different groups, surgeons and students, but not radiologists, more accurately identified tumor location and spatial relationships with adjacent liver and portal veins using 3D models. Subjectively, most surgeons and students preferred 3D printed models over virtual models and 2D CT images. CONCLUSIONS:This study demonstrated that standardized, non-colored 3D virtual and printed models equally help preoperative anatomical understanding and decision-making, particularly for surgeons and students. By isolating the influence of display modality, our findings clarify prior inconsistent results and support the integration of cost-effective 3D visualization by applying virtual models into surgical planning and education. Preference for printed models despite comparable efficacy highlights the importance of user-centered implementation strategies.

RATIONALE AND OBJECTIVES:To investigate factors influencing the success and complication rate of percutaneous transhepatic biliary drainage (PTBD). MATERIALS AND METHODS:PTBD procedures between 2017 and 2022 were enrolled retrospectively. Success rate, complications, and technical considerations were compared using Mann-Whitney U-, X2-, and Fishers exact tests. The influence of the study center's experience (training-effect) on success and complication rates was assessed by linear regression categorized by year. RESULTS:In 398 patients, 499 PTBD procedures (337 men; mean age 66.2 +/- 12.5 years) were carried out, with a success rate of 83.2% (415/499). PTBD in dilated bile ducts was more successful than in non-dilated bile ducts (90.0%; 316/340 vs. 68.6%; 109/159; p < 0.001), with e.g. lower radiation doses (2787.52 +/- 4012.72 cGy*cm2 vs. 4679.25 +/- 4663.55 cGy*cm2; p < 0.001), and shorter total procedure time (33.42 +/- 24.03 min vs. 41.09 +/- 27.21 min; p < 0.001). Complications occurred in 34/499 (6.8%) procedures (major complications n = 25/34) with no significant difference in bile duct width. Right-sided PTBD revealed more complications (9.0%; 30/332 vs. 2.4%; 4/166; p = 0.006) and higher radiation doses (3679.47 +/- 4571.71 cGy*cm2 vs. 2819.01 +/- 3724.92 cGy*cm2; p = 0.001) than left-sided approaches. Linear regression showed a significant continuous increase in the technical success rate of 3.0% per year (2017-2022; 72.5%; 78.5%; 82.2%; 85.0%; 89.0%; 87.5%; p = 0.005), while the overall complication rate remained unaffected (p = 0.364). CONCLUSION:Medical centers adopting PTBD procedures can potentially increase their success rate significantly within a short period of time. PTBD is a safe procedure, with left-sided approaches showing lower complication rates and radiation exposure, underscoring their often-underestimated advantages in clinical practice.

Malignome des oberen Gastrointestinaltrakts im UICC-Stadium IV werden gemäß Leitlinien aufgrund der schlechten Prognose meist als palliativ eingestuft, daher erhalten die betroffenen Patienten keine chirurgische Therapie. Das UICC-Stadium IV (M1) umfasst jedoch sowohl eine ausgedehnte Fernmetastasierung als auch eine limitierte, sog. Oligometastasierung. Jedoch unterscheiden sich diese beiden Patientengruppen hinsichtlich ihrer onkologischen Prognose signifikant. In Studien der letzten Dekade konnte gezeigt werden, dass auch Patienten mit Oligometastasierung bei Ösophagus- und Magenkarzinomen bei guter Selektion durch verbesserte multimodale Therapiekonzepte einer Konversionschirurgie von Primärtumor und Fernmetastasen zugeführt werden können und sich hierdurch die Prognose signifikant verbessern lässt. Aufgrund der konstanten Weiterentwicklung der Systemtherapien hat sich die Rate von Konversionschemotherapien in den letzten Jahren kontinuierlich erhöht. Um den Patienten die entsprechenden Therapien anbieten zu können, ist eine sorgfältige Indikationsstellung und Patientenselektion mit regelmäßiger Evaluation essenziell. Somit ist eine Vorstellung von Patienten mit Oligometastasierung an einem großen Zentrum unerlässlich, um den Patienten die optimale Therapie und auch eine passende Studienteilnahme zu ermöglichen.

BackgroundMicrovascular invasion is a major histopathological risk factor of postoperative recurrence in patients with hepatocellular carcinoma. This study aimed to develop and validate a digital biopsy model using imaging features to predict microvascular invasion before hepatectomy.MethodsA total of 217 consecutive patients who underwent hepatectomy for resectable hepatocellular carcinoma were enrolled at two tertiary-care reference centers. An imaging-based digital biopsy model was developed and internally validated using logistic regression analysis with adjustments for age, sex, etiology of disease, size and number of lesions.ResultsThree imaging features, i.e., non-smoothness of lesion margin (OR = 16.40), ill-defined pseudocapsula (OR = 4.93), and persistence of intratumoral internal artery (OR = 10.50), were independently associated with microvascular invasion and incorporated into a prediction model. A scoring system with 0 - 3 points was established for the prediction model. Internal validation confirmed an excellent calibration of the model. A cutoff of 2 points indicates a high risk of microvascular invasion (area under the curve 0.87). The overall survival and recurrence-free survival stratified by the risk model was significantly shorter in patients with high risk features of microvascular invasion compared to those patients with low risk of microvascular invasion (overall survival: median 35 vs. 75 months, P = 0.027; recurrence-free survival: median 17 vs. 38 months, P < 0.001)).ConclusionA preoperative assessment of microvascular invasion by digital biopsy is reliable, easily applicable, and might facilitate personalized treatment strategies.

Introduction CRC with liver metastases is a major contributor to cancer-related mortality. Despite advancements in liver resection techniques, patient survival remains a concern due to high recurrence rates. This study seeks to uncover prognostic biomarkers that predict overall survival in patients undergoing curative hepatic resection for CRC liver metastases. Methods Prospectively collected serum samples from a cohort of 49 patients who received curative hepatic resection for CRC liver metastases were studied. The patients are part of a cohort, previously analyzed for perioperative complications (see methods). Various preoperative serum markers, clinical characteristics, and factors were analyzed. Univariate and multivariate Cox regression analyses were conducted to determine associations between these variables and disease-free survival as well as overall survival. Results For disease-free survival, univariate analysis highlighted the correlation between poor outcomes and advanced primary tumor stage, high ASA score, and synchronous liver metastases. Multivariate analysis identified nodal-positive primary tumors and synchronous metastases as independent risk factors for disease-free survival. Regarding overall survival, univariate analysis demonstrated significant links between poor survival and high preoperative IL-8 levels, elevated neutrophil–lymphocyte ratio (NLR), and presence of metastases in other organs. Multivariate analysis confirmed preoperative IL-8 and having three or more liver metastases as independent risk factors for overall survival. The impact of IL-8 on survival was particularly noteworthy, surpassing the influence of established clinical factors. Conclusion This study establishes preoperative IL-8 levels as a potential prognostic biomarker for overall survival in patients undergoing curative liver resection for CRC liver metastases. This study underscores the importance of incorporating IL-8 and other biomarkers into clinical decision-making, facilitating improved patient stratification and tailored treatment approaches. Further research and validation studies are needed to solidify the clinical utility of IL-8 as a prognostic marker.

In laparoscopic liver surgery, augmented reality can assist surgeons in locating structures of interest, which are invisible in the laparoscopic view. This requires an accurate deformable registration of preoperative patient data with the intraoperative liver model based on established correspondences. Finding these automatically is extremely difficult, due to the two distinct modalities and different noise sources, which lead to very different geometries. This can cause modern neural-network-based registration algorithms to produce imperfect alignments. We aim to alleviate this issue by incorporating additional expert knowledge as input to these networks. We propose Cue-Net for non-rigid registration, and modify it in such a way that surgeons can steer and correct its behavior. This is achieved by incorporating an interaction step in which users mark matching cues, allowing them to pass on their anatomical knowledge to the otherwise automatic system. We evaluated the performance of Cue-Net with user inputs on different datasets from the global and local points of view. The distribution of the matching cues on the operating surface is observed to be the most dominant factor for obtaining an improvement regarding registration accuracy, compared to the morphology of matching cues. Additionally, we performed a user study to present the usability of the system. Code is available at: https://gitlab.com/nct_tso_public/cue-net.

BACKGROUND:Liver surgery remains a cornerstone of potentially curative multimodal treatments for primary malignancies of the liver and hepatic metastases. Improving perioperative safety is a prerequisite in this context. Perioperative blood transfusions negatively influence postoperative recovery. This study aimed to identify risk factors for perioperative packed red blood cell (PRBC) transfusion and to elucidate its effect on postoperative outcomes. METHODS:This was an observational study of a prospective data collection. A monocentric, retrospective analysis of 1118 hepatectomies at the University Hospital Carl Gustav Carus between 2013 and 2020 was conducted to compare postoperative short- and long-term outcomes in patients undergoing curative intended liver resection of hepatic primary or secondary malignancies. The outcomes were compared between 356 patients (31.8%) who received PRBC transfusions during surgery or within 7 days after surgery and 762 patients (68.2%) who did not receive PRBC transfusions. RESULTS:Preoperative anemia could be observed in 45.0% of the whole cohort: 65.7% in the PRBC transfusion group and 35.3% in the nontransfused group. Postoperative complications were significantly more common in the PRBC transfusion group in association with prolonged lengths of hospital stay and increased 30-day mortality than in the nontransfused group. After adjustment for possible confounders, preexisting kidney failure, preoperative hemoglobin and albumin levels outside of the reference range, intraoperative plasma transfusions, and overall surgery time were recognized as negative predictors for perioperative PRBC transfusions. PRBC transfusion increased the risk of death by approximately 38.8% (hazard ratio, 1.388; 95% CI, 1.027-1.876; P = .033), whereas no influence on recurrence-free survival (RFS) was observed. CONCLUSION:PRBC transfusions were associated with postoperative morbidity and mortality after curative-intended surgery for liver cancers and represented an independent poor prognostic indicator for overall survival but not for RFS.

To examine the comparative robustness of computed tomography (CT)-based conventional radiomics and deep-learning convolutional neural networks (CNN) to predict overall survival (OS) in HCC patients. Retrospectively, 114 HCC patients with pretherapeutic CT of the liver were randomized into a development (n = 85) and a validation (n = 29) cohort, including patients of all tumor stages and several applied therapies. In addition to clinical parameters, image annotations of the liver parenchyma and of tumor findings on CT were available. Cox-regression based on radiomics features and CNN models were established and combined with clinical parameters to predict OS. Model performance was assessed using the concordance index (C-index). Log-rank tests were used to test model-based patient stratification into high/low-risk groups. The clinical Cox-regression model achieved the best validation performance for OS (C-index [95% confidence interval (CI)] 0.74 [0.57–0.86]) with a significant difference between the risk groups (p = 0.03). In image analysis, the CNN models (lowest C-index [CI] 0.63 [0.39–0.83]; highest C-index [CI] 0.71 [0.49–0.88]) were superior to the corresponding radiomics models (lowest C-index [CI] 0.51 [0.30–0.73]; highest C-index [CI] 0.66 [0.48–0.79]). A significant risk stratification was not possible (p > 0.05). Under clinical conditions, CNN-algorithms demonstrate superior prognostic potential to predict OS in HCC patients compared to conventional radiomics approaches and could therefore provide important information in the clinical setting, especially when clinical data is limited.

A major challenge in image-guided laparoscopic surgery is that structures of interest often deform and go, even if only momentarily, out of view. Methods which rely on having an up-to-date impression of those structures, such as registration or localisation, are undermined in these circumstances. This is particularly true for soft-tissue structures that continually change shape - in registration, they must often be re-mapped. Furthermore, methods which require 'revisiting' of previously seen areas cannot in principle function reliably in dynamic contexts, drastically weakening their uptake in the operating room. We present a novel approach for learning to estimate the deformed states of previously seen soft tissue surfaces from currently observable regions, using a combined approach that includes a Graph Neural Network (GNN). The training data is based on stereo laparoscopic surgery videos, generated semi-automatically with minimal labelling effort. Trackable segments are first identified using a feature detection algorithm, from which surface meshes are produced using depth estimation and delaunay triangulation. We show the method can predict the displacements of previously visible soft tissue structures connected to currently visible regions with observed displacements, both on patient data and porcine data. Our innovative approach learns to compensate non-rigidity in abdominal endoscopic scenes directly from stereo laparoscopic videos through targeting a new problem formulation, and stands to benefit a variety of target applications in dynamic environments.

Background The liver is the main destination for distant metastases of gastrointestinal as well as other malignancies. Consequently, the therapy of liver metastases plays a key role in oncological treatment strategies.Objectives In this review, the current treatment options and multimodal therapy strategies for liver metastases are presented.Materials and methods Current literature and national/international consensus guidelines are reviewed and general treatment options for liver metastases are summarized and then presented for the respective malignancies.Results According to treatment strategy and prognosis, liver metastases are divided into three categories: colorectal liver metastases (CRLM), neuroendocrine liver metastases, and non-colorectal non-neuroendocrine liver metastases which include liver metastases of all other malignancies. Due to their highest incidence, CRLM are of major interest. The individual treatment is chosen according to patient's oncological situation, liver function, and comorbidities.Conclusions Surgery is the best curative treatment option. Thus, all patients should be evaluated for surgery in specialized oncological boards. Despite higher local recurrence, local ablation methods are good alternative options for small and limited metastases. Systemic treatments and radiation are implemented in palliative settings as well as multimodal treatment strategies with the goal of cure.