Liver Segments Research Articles

Comparative analysis of the DCNN and HFCNN Based Computerized detection of liver cancer

Liver cancer detection is critically important in the discipline of biomedical image testing and diagnosis. Researchers have explored numerous machine learning (ML) techniques and deep learning (DL) approaches aimed at the automated recognition of liver disease by analysing computed tomography (CT) images. This study compares two frameworks, Deep Convolutional Neural Network (DCNN) and Hierarchical Fusion Convolutional Neural Networks (HFCNN), to assess their effectiveness in liver cancer segmentation. The contribution includes enhancing the edges and textures of CT images through filtering to achieve precise liver segmentation. Additionally, an existing DL framework was employed for liver cancer detection and segmentation. The strengths of this paper include a clear emphasis on the criticality of liver cancer detection in biomedical imaging and diagnostics. It also highlights the challenges associated with CT image detection and segmentation and provides a comprehensive summary of recent literature. However, certain difficulties arise during the detection process in CT images due to overlapping structures, such as bile ducts, blood vessels, image noise, textural changes, size and location variations, and inherent heterogeneity. These factors may lead to segmentation errors and subsequently different analyses. This research analysis compares two advanced methodologies, DCNN and HFCNN, for liver cancer detection. The evaluation of DCNN and HFCNN in liver cancer detection is conducted using multiple performance metrics, including precision, F1-score, recall, and accuracy. This comprehensive assessment provides a detailed evaluation of these models’ effectiveness compared to other state-of-the-art methods in identifying liver cancer.

Efficient Liver Segmentation using Advanced 3D-DCNN Algorithm on CT Images

According to the latest global cancer statistics for 2022, liver cancer ranks as the ninth most common disease in women. Segmenting the liver and distinguishing it from tumors within it pose a significant challenge due to the complex nature of liver imaging. Common imaging methods such as Magnetic Resonance Imaging (MRI), Computer Tomography (CT), and Ultrasound (US) are employed to distinguish liver tissue from liver tumors after collecting a sample. Attempting to partition the liver and tumor based on grayscale shades or shapes in abdominal CT images is not ideal because of the overlapping intensity levels and the variability in the location and shape of soft tissues. To address this issue, this study introduces an effective method for liver image segmentation using a 3D deep Convolutional Neural Network (3D-DCNN). The process involves several stages. First, liver images undergo preprocessing to enhance image quality, including median filtering, adaptive filtering, and converting them to grayscale. The feature extraction phase focuses on extracting four sets of features, such as the Local Binary Pattern (LBP) and the Gray-Level Co-occurrence Matrix (GLCM). Additionally, an Iterative Region Growing (IRG) technique is developed to improve the Dice Similarity Coefficient (DSC) prediction by enhancing the quality of the input images obtained from segmented images. This method enables the segmentation of the liver in abdominal CT image volumes and can subsequently be used to segment liver tumor images to evaluate the performance of the proposed 3D-DLNN approach. This method was implemented in MATLAB, and its performance was evaluated using various metrics. In experimental analysis, the proposed technique outperformed other methods, including Jaccard with JISTS-FCM, Fuzzy C-Means (FCM), and FCM with Cluster Size Adjustment (FCM-CSA).

O-01 A CASE OF LANGERHANS CELL HISTIOCYTOSIS WITH INVOLVEMENT OF THE HYPOTHALAMUS, BONE, AND LIVER

Abstract Introduction Langerhans cell histiocytosis (LCH) is a neoplastic disorder characterized by the abnormal infiltration of Langerhans cells, affecting multiple systems. Here, we report a rare case of LCH with involvement of the hypothalamus, liver, and bone. Clinical Case A 36-year-old female patient presented to the psychiatry department with complaints of anxiety, including sudden behavioral changes, headaches, increased forgetfulness, hypersomnia, and excessive thirst excessive eating, and memory lapses that persisted for three months. A contrast-enhanced pituitary MRI taken revealed a lobulated mass lesion, measuring 15x9x17 mm, extending into the interpeduncular cistern and anteriorly toward the optic chiasm, with cystic necrotic areas located at the base of the third ventricle (Figure 1A). Laboratory examinations indicated panhypopituitarism and central diabetes insipidus. Abdominal ultrasound revealed a hypoechoic area in liver segment 8.PET-CT showed a region of intense metabolic activity in the posterior section of the left mastoid bone (SUVmax: 12.4). In addition, a 14-mm focal area with increased fluorodeoxyglucose uptake was observed in liver segment 8 (SUVmax: 8.4) (Figure 1B).The temporal MRI showed a mass lesion extending to the petrous apex, filling the mastoid cells on the left side, with signal characteristics similar to the hypothalamic lesion (Figure 1C).Biopsy was performed on identified lesions. Both biopsy results were consistent with LCH. She was planned to administer two courses of cladribine therapy. Follow-up PET-CT after chemotherapy indicated that the liver and mastoid bone involvement had resolved. However, the patient’s neuropsychiatric symptoms did not improve following chemotherapy. Paraneoplastic autoimmune encephalitis was primarily suspected, and antibody testing was performed. Due to the progressive nature of the patient’s condition, intravenous immunoglobulin therapy was administered. On the fourth day of this treatment, the patient experienced increased agitation and delusions, and developed disorientation.50 mg of quetiapine was prescribed. Three days after starting quetiapine, the patient developed fever, tachypnea, muscle rigidity, increased confusion, and an acute increase in serum creatine kinase levels (29,000 U/L). The condition was evaluated as a neuroleptic malignant syndrome leading to the discontinuation of quetiapine. The patient was transferred to the intensive care unit (ICU).On the first day of ICU admission, the patient was intubated. Despite cardiac support, the patient died on the third day of ICU admission. Conclusion LCH is a rare but multisystemic inflammatory neoplasm. The absence of specific clinical features can lead to delays in diagnosis. Although LCH most commonly presents with bone and skin lesions, a multidisciplinary approach is crucial for an accurate diagnosis. Albeit rare, LCH should be considered in the differential diagnosis of patients presenting with hypothalamic masses.Figure 1:Magnetic resonance image of the hypothalamic mass (A), Magnetic resonance images revealing a mass lesion in the left mastoid bone (B) and a subcapsular lesion in liver segment 8 (C)

Case report: Chemotherapy plus sintilimab for the treatment of gastroesophageal junction hepatoid adenocarcinoma with liver metastasis: a case study with literature review

ObjectiveTo elucidate the clinicopathological features and treatment of metastatic gastroesophageal junction hepatoid adenocarcinoma (GEJ HAC)using a case study and literature review.MethodsClinical presentation, results of histology and immunohistochemistry, and next-generation sequencing(NGS) in a patient with GEJ HAC metastasizing to the liver were reviewed. Chemotherapy (SOX or S-1) plus sintilimab was administered.ResultsA 65-year-old male patient with a history of hypertension was admitted to the hospital due to a one-week increase in serum AFP levels. There was a small intraluminal mass at the GEJ and a metastatic lesion in liver segment VIII, as well as enlarged perigastric and retroperitoneal lymph nodes. Tumor cells in both the GEJ and liver tissue exhibited a glandular shape with a nest-like adenoid structure. Immunohistochemical (IHC) analysis of the GEJ tissue showed positivity for AFP, CA19-9, CK7, CK20, MUC-1, P53 (wild type), Glypican-3, and HepPar-1, and negativity for Arginase-1, CD10, and Her-2. In the metastatic liver tissue, IHC testing demonstrated positivity for AFP, CD10, CK19, CK20, HepPar-1, MUC-1, Ki-67, and P53 (wild type), while CK7 was negative. The NGS report of GEJ mass indicated that the JAK2 and TP53 genes harbored missense mutations, while the MLH1, MSH2, MSH6, PMS2, ERBB2, EGFR, PIK3CA, APC, CTNNB1, CDH1, and DPYD genes were normal. The patient’s serum levels of CEA, CA19-9, and AFP were sharply decreased. The patient achieved a major pathological response (MPR) and remains in a progression-free stage.ConclusionsSintilimab-based chemotherapy has proven efficacy in achieving a MPR and maintaining a progression-free state for a patient with GEJ HAC that has metastasized to the liver.

Application of DynaCT biliary soft tissue reconstruction technology in diagnosis and treatment of hepatolithiasis.

Hepatobiliary stone disease involves an intrahepatic bile duct stone that occurs above the confluence of the right and left hepatic ducts. One-step percutaneous transhepatic cholangioscopic lithotripsy (PTCSL) using the percutaneous transhepatic one-step biliary fistulation (PTOBF) technique enables the clearing of intrahepatic stones and the resolution of strictures. However, hepatolithiasis with associated strictures still has high residual and recurrence rates after one-step PTCSL. DynaCT can achieve synchronized acquisition with a flat-panel detector during C-arm rotation. The technical approach and application value of DynaCT biliary soft tissue reconstruction technology for the diagnosis and treatment of hepatolithiasis with bile duct stenosis were evaluated in this study. To explore the value of DynaCT biliary soft tissue reconstruction technology for the diagnosis and treatment of hepatolithiasis with bile duct stenosis, and to assess the feasibility and effectiveness of the PTOBF technique guided by DynaCT biliary soft tissue reconstruction technology. The clinical data of 140 patients with complex biliary stenosis disease combined with bile duct stenosis who received PTOBF and were admitted to the First Affiliated Hospital of Guangzhou Medical University from January 2020 to December 2024 were collected. The patients were divided into two groups: DynaCT-PTOBF group (70 patients) and conventional PTOBF group (70 patients). These groups were compared in terms of the preoperative bile duct stenosis, location of the liver segment where the stone was located, intraoperative operative time, immediate stone retrieval rate, successful stenosis dilatation rate, postoperative complication rate, postoperative reoperation rate, stone recurrence rate, and stenosis recurrence rate. DynaCT biliary soft tissue reconstruction technology was successfully performed in 70 patients. The DynaCT-PTOBF group had a higher detection rate of target bile ducts where bile duct stones and biliary strictures were located than the PTOBF group. Compared with the PTOBF group, the DynaCT-PTOBF group was characterized by a significantly greater immediate stone removal rate (68.6% vs 50.0%, P = 0.025), greater immediate stenosis dilatation success rate (72.9% vs 55.7%, P = 0.034), greater final stenosis release rate (91.4% vs 75.7%, P = 0.012), shorter duration of intraoperative hemorrhage (3.14 ± 2.00 vs 26.5 ± 52.1, P = 0.039), and lower incidence of distant cholangitis (2.9% vs 11.4%, P = 0.49). There were no significant differences between the two groups in terms of the final stone removal rate, reoperation rate, or long-term complication incidence rate. DynaCT biliary soft tissue reconstruction technology guiding the PTOBF technique in patients with hepatolithiasis with bile duct stenosis is feasible and accurate. It may be beneficial for optimizing the preoperative evaluation of the PTOBF technique.

Opportunistic assessment of steatotic liver disease in lung cancer screening eligible individuals.

Steatotic liver disease (SLD) is a potentially reversible condition but often goes unnoticed with the risk for end-stage liver disease. To opportunistically estimate SLD on lung screening chest computed tomography (CT) and investigate its prognostic value in heavy smokers participating in the National Lung Screening Trial (NLST). We used a deep learning model to segment the liver on non-contrast-enhanced chest CT scans of 19,774 NLST participants (age 61.4±5.0 years; 41.2% female) at baseline and on the 1-year follow-up scan if no cancer was detected. SLD was defined as hepatic fat fraction (HFF) ≥5% derived from Hounsfield unit measures of the segmented liver. Participants with SLD were categorized as lean (body mass index [BMI]<25kg/m2) and overweight (BMI≥25kg/m2). The primary outcome was all-cause mortality. Cox proportional hazard regression assessed the association between (1) SLD and mortality at baseline and (2) the association between a change in HFF and mortality within 1 year. There were 5.1% (1000/19,760) all-cause deaths over a median follow-up of 6 (range, 0.8-6) years. At baseline, SLD was associated with increased mortality in lean but not in overweight/obese participants as compared to participants without SLD (hazard ratio [HR] adjusted for risk factors: 1.93 [95% confidence interval 1.52-2.45]; p=0.001). Individuals with an increase in HFF within 1 year had a significantly worse outcome than participants with stable HFF (HR adjusted for risk factors: 1.29 [1.01-1.65]; p=0.04). SLD is an independent predictor for long-term mortality in heavy smokers beyond known clinical risk factors.

Comprehensive evaluation of the ramification patterns of hepatic vascular anatomy based on three-dimensional visualization technology.

The liver segmentation method proposed by Couinaud is widely accepted by surgeons because of its convenience and practicality. However, this conventional eight-segment classification does not reflect realistic details of the liver and thus requires further adjustments to promote improvements in surgical strategies. This study aimed to explore the ramification patterns of the hepatic vasculature comprehensively. A total of 197 eligible patients meeting the study criteria were enrolled for three-dimensional reconstruction analysis. In the left hemiliver, the portal vein bifurcated into P2 and umbilical portion (UP) in 172 (98.3%) patients. The P4b of 103 patients (103/172, 59.9%) whose P4b branched from the right horn of the left portal vein (LPV) diverged from the main trunk of the UP. In the right paramedian sector (RPMS), the entire portal trunk directly bifurcates into P8vent and P8dor. Simple branching of P5 off the trunk of the RPMS was observed in 78 patients (78/130, 60%). The anterior fissure vein (AFV) was identified in 86 (86/148, 58.1%) patients. V8d entered the right hepatic vein (RHV) in all the patients. In 75.3% (113/150) of all the patients, V5d joined the RHV. In the right lateral sector (RLS), more than half (71/133, 53.4%) of the patients had an arch-like type. We summarize different patterns of liver vascular branches, providing a valuable reference for clinical surgery and liver transplantation. Cranio-caudal segmentation is more common than ventral-dorsal segmentation. The AFV can be regarded as a reliable anatomical landmark for subsegmentation in segment 8. In addition, the absence of AFV was associated with the P8 pattern.

Liver resection in stage 0-A HCC in segments 7/8: a propensity-matched analysis comparing open, laparoscopic, and robotic approach.

Both laparoscopic hepatectomy (LH) and robotic hepatectomy (RH) have been performed for tumors in nearly all liver segments. However, few studies have compared the outcomes of patients who underwent open hepatectomy (OH), LH and RH for the treatment of Barcelona Clinic Liver Cancer (BCLC) stage 0-A HCC in S7/8. The clinical data of patients who underwent S7/8 resection for the treatment of BCLC stage 0-A HCC in the First Affiliated Hospital of Guangxi Medical University from July 2017 to July 2023 were retrospectively collected. To minimize selection bias, propensity score matching (PSM) analysis was performed using American Society of Anesthesiology (ASA), tumor size, body mass index (BMI), alpha-fetoprotein (AFP), tumor location, age, number of tumors, platelet (PLT), and Viral hepatitis. A total of 401 patients met the study criteria.After PSM, 61 OH (28.6%), 74 LH (34.8%), and 78 RH (36.6%) were included. RH group had the least blood loss among the three groups (OH, 300 vs. LH, 215 vs. RH, 100mL,P < 0.001). Conversion rate was significantly lower in RH group compared to LH group [LH, 10 (13.5%) vs. RH, 1 (1.3%),P = 0.003]. Although minimally invasive group (RH + LH) took slightly longeroperativetime (OH, 233 vs. LH, 255.5 vs. RH, 257min,P = 0.068), there was no statistical difference. The minimally invasive group had fewer postoperative hospital stay (OH, 8 vs. LH, 6 vs. RH, 6days,P < 0.001). The minimally invasive group had lower rates of surgical complications (OH, 37.7% vs. LH, 20.3% vs. RH, 11.5%). However, there were no statistically significant variations observed in the disease-free survival or overall survival rates among the three groups. RH showed advantage over the OH and LH in short-term outcomes, and non-inferiority in survival outcomes for the treatment of BCLC stage 0-A HCC patients.

G-UNETR++: A Gradient-Enhanced Network for Accurate and Robust Liver Segmentation from Computed Tomography Images

G-UNETR++: A Gradient-Enhanced Network for Accurate and Robust Liver Segmentation from Computed Tomography Images

Extracting organs of interest from medical images based on convolutional neural network with auxiliary and refined constraints

Accurately extracting organs from medical images provides radiologist with more comprehensive evidences to clinical diagnose, which offers up a higher accuracy and efficiency. However, the key to achieving accurate segmentation lies in abundant clues for contour distinction, which has a high demand for the network architecture design and its practical training status. To this end, we design auxiliary and refined constraints to optimize the energy function by supplying additional guidance in training procedure, thus promoting model’s ability to capture information. Specifically, for the auxiliary constraint, a set of convolutional structures are involved into a conventional network to act as a discriminator, then adversarial network is established. Based on the obtained architecture, we further build adversarial mechanism by introducing a second discriminator into segmentor for refinement. The involvement of refined constraint contributes to ameliorate training situation, optimize model performance, and boost its ability of collecting information for segmentation. We evaluate the proposed framework on two public databases (NIH Pancreas-CT and MICCAI Sliver07). Experimental results show that the proposed network achieves comparable performance to current pancreas segmentation algorithms and outperforms most state-of-the-art liver segmentation methods. The obtained results on public datasets sufficiently demonstrate the effectiveness of the proposed model for organ segmentation.

Liver T1 mapping in Fontan patients and patients with biventricular congenital heart disease - insights into the effects of venous congestions on diffuse liver disease.

T1 relaxation time quantification on parametric maps is routinely used in cardiac imaging and may serve as a non-invasive biomarker for diffuse liver disease. In this study, we aimed to investigate the relationship between liver T1 values and cardiac function in patients with congenital heart disease (CHD) and compared patients with a biventricular circulation (BVC) to those with a Fontan circulation (FC). Magnetic resonance images from patients with CHD, obtained between June and December 2023 on a 1.5 T machine, were retrospectively reviewed. The examinations included cardiac cine sequences to assess ventricular mass and volumes, along with liver T1 mapping. T1 values were measured in eight liver segments and were compared with ventricular mass and volumes in patients with BVC and FC. In total, 104 patients (75 with BVC and 29 with FC) were included. T1 values varied significantly among the eight liver segments in both patient groups. In an age-matched comparison, patients with FC had significantly higher T1 values in all liver segments. In patients with BVC and right ventricular (RV) enlargement, a positive correlation between RV volume and T1 values in the right liver lobe was found (R > 0.504, p < 0.033). In patients with FC, the T1 values did not differ between patients with an extracardiac conduit or a lateral tunnel. Liver T1 mapping suggests more severe liver affection in patients with FC compared to those with BVC. It seems a valuable addition to cardiovascular magnetic resonance for patients who are at risk of systemic venous congestion.

Adaptive Evolutionary Optimization of Deep Learning Architectures for Focused Liver Ultrasound Image Segmentation.

Background: Liver ultrasound segmentation is challenging due to low image quality and variability. While deep learning (DL) models have been widely applied for medical segmentation, generic pre-configured models may not meet the specific requirements for targeted areas in liver ultrasound. Quantitative ultrasound (QUS) is emerging as a promising tool for liver fat measurement; however, accurately segmenting regions of interest within liver ultrasound images remains a challenge. Methods: We introduce a generalizable framework using an adaptive evolutionary genetic algorithm to optimize deep learning models, specifically U-Net, for focused liver segmentation. The algorithm simultaneously adjusts the depth (number of layers) and width (neurons per layer) of the network, dropout, and skip connections. Various architecture configurations are evaluated based on segmentation performance to find the optimal model for liver ultrasound images. Results: The model with a depth of 4 and filter sizes of [16, 64, 128, 256] achieved the highest mean adjusted Dice score of 0.921, outperforming the other configurations, using three-fold cross-validation with early stoppage. Conclusions: Adaptive evolutionary optimization enhances the deep learning architecture for liver ultrasound segmentation. Future work may extend this optimization to other imaging modalities and deep learning architectures.

TransRAUNet: A Deep Neural Network with Reverse Attention Module Using HU Windowing Augmentation for Robust Liver Vessel Segmentation in Full Resolution of CT Images.

Background: Liver cancer has a high mortality rate worldwide, and clinicians segment liver vessels in CT images before surgical procedures. However, liver vessels have a complex structure, and the segmentation process is conducted manually, so it is time-consuming and labor-intensive. Consequently, it would be extremely useful to develop a deep learning-based automatic liver vessel segmentation method. Method: As a segmentation method, UNet is widely used as a baseline, and a multi-scale block or attention module has been introduced to extract context information. In recent machine learning efforts, not only has the global context extraction been improved by introducing Transformer, but a method to reinforce the edge area has been proposed. However, the data preprocessing step still commonly uses general augmentation methods, such as flip, rotation, and mirroring, so it does not perform robustly on images of varying brightness or contrast levels. We propose a method of applying image augmentation with different HU windowing values. In addition, to minimize the false negative area, we propose TransRAUNet, which introduces a reverse attention module (RAM) that can focus edge information to the baseline TransUNet. The proposed architecture solves context loss for small vessels by applying edge module (RAM) in the upsampling phase. It can also generate semantic feature maps that allows it to learn edge, global context, and detail location by combining high-level edge and low-level context features. Results: In the 3Dricadb dataset, the proposed model achieved a DSC of 0.948 and a sensitivity of 0.944 in liver vessel segmentation. This study demonstrated that the proposed augmentation method is effective and robust by comparisons with the model without augmentation and with the general augmentation method. Additionally, an ablation study showed that RAM has improved segmentation performance compared to TransUNet. Compared to prevailing state-of-the-art methods, the proposed model showed the best performance for liver vessel segmentation. Conclusions: TransRAUnet is expected to serve as a navigation aid for liver resection surgery through accurate liver vessel and tumor segmentation.

VSNet: Vessel Structure-aware Network for hepatic and portal vein segmentation

VSNet: Vessel Structure-aware Network for hepatic and portal vein segmentation

Ruptured Hepatic Subcapsular Hematoma as a Complication of Endoscopic Retrograde Cholangiopancreatography. A Case Report and Literature Review

This report describes the unusual case of a 59-year-old woman who presented a subcapsular hepatic hematoma (HH) affecting liver segments VII and VIII as a complication of Endoscopic Retrograde Cholangiopancreatography (ERCP). Due to hemodynamic instability, urgent surgical hemostasis and evacuation of the hematoma were performed. The most important clinical manifestations were acute abdominal pain and progressive anemia. The diagnosis is based on clinical findings and images, being computed tomography (CT) the gold standard for the definitive diagnosis. The patient was successfully treated and was discharged home on the 75th hospital day. During the six-month follow-up, no clinical or biochemical abnormalities were observed, and the imaging studies showed a progressive reduction in the size of the injuries. As a potentially life-threatening complication, subcapsular hepatic hematoma after ERCP must be considered in the differential diagnosis of symptomatic cases in the early period after ERCP.

An Optimized CNN Architecture for Accurate 3D Liver Segmentation in Medical Images

An Optimized CNN Architecture for Accurate 3D Liver Segmentation in Medical Images

8 years with laparoscopic liver surgery: a referral center experience

Objective: Laparoscopic liver surgery (LLR) was first performed in 1992 and LLR began to be performed for many benign and malignant etiologies, especially hepatocellular carcinoma (HCC) and colorectal cancer metastases (CRC). Recent studies have shown that LLR has less bleeding, shorter hospital stay (LOS), faster recovery, and similar long-term oncologic outcomes compared to open surgery. This study aims to examine the results of LLR, which has been performed for 8 years in our institution, which is one of the referal centers in the field of hepatopancreaticobiliary surgery. Methods: Twenty-nine patients who underwent LLR for malignant and benign reasons in our hospital between January 2016 and March 2024 were included in the study, and 416 patients who underwent open surgery, and laparoscopic ablation. Data were obtained retrospectively from the hospital registry system. Results: 18 (62.1%) of the patients were male and the median age was 57 (41-62.5). 23 (79.31%) of the patients underwent surgery for malignant reasons. The most common indications for surgery were HCC (24.14%) and CRC (20.69%). Median blood loss was 200 (100-375) ml. Median LOS was three (3-5) days, and 30-day readmission rate was 3.45%. Clavian-Dindo ≥3 complication grade was seen in 13.79% of patients and no mortality was observed in any patient. R0 resection was achieved in 73.91% of patients. Disease recurrence developed in 56.52% of patients at a median follow-up of 22.1 (9.9-48.5) months. Of the patients who developed recurrence in the liver, recurrence developed at the surgical margin in 13.04%, and in other liver segments in 30.43%. Median overall survival was 48.5 months, and median recurrence-free survival was 21 months. 1-, 3-, and 5-year overall survival were 85%, 76%, and 48%, respectively, while 1- and 3-year recurrence-free survival were 71% and 34%, respectively. Conclusion: In this study, it was shown that LLR is a safe alternative to traditional open surgery in terms of length of hospital stay, blood loss, recurrence rates, and survival rates in parallel with the literature, and that although the surgical margin was positive, recurrence developed mostly in other liver segments, and in some patients, no recurrence was observed despite positive margins.

Robotic Real Anatomical Right Hepatectomy Preserving the Caudate Lobe: Separate Dissection of the Right Anterior and Posterior Glissonean Pedicles, Combined with the Use of ICG Fluorescent Imaging (with Video).

Conventional right hepatectomy typically involves resection of the right hemiliver, often including partial removal of the caudate lobe. However, recent advancements, particularly in indocyanine green (ICG) fluorescence imaging, have allowed for more accurate identification of anatomical boundaries between liver segments. In this context, we present a refined technique for real anatomical right hepatectomy that preserves the caudate lobe, offering enhanced surgical precision and several distinct advantages over traditional methods. This study involved a right hepatectomy using the Da Vinci Xi robotic system, employing ICG fluorescence imaging for parenchymal transection. Key steps included the dissection of the right anterior and posterior Glissonean pedicles and the preservation of the caudate lobe. ICG was administered to visualize the boundary between the caudate lobe and the right liver, guiding a precise resection. The procedure lasted 320min with 150mL blood loss. Pathology showed a 6.7cm × 5.5cm hepatocellular carcinoma with a 2.1cm margin. No bile leakage or significant complications occurred, and the patient was discharged on day 6 without issues. This technique, underpinned by a deep understanding of liver anatomy, the Glissonean approach, and advanced ICG fluorescence imaging, allows for greater precision in defining anatomical boundaries and preserving the caudate lobe. We propose that real anatomical right hepatectomy offers a promising alternative for surgical practice, with the potential for improved outcomes in patients with liver malignancies.

Novel techniques of liver segmental and subsegmental pedicle anatomy from segment 1 to segment 8.

Laparoscopic anatomical liver resection has become more challenging because some subsegmental Glissonean pedicles are hard to dissect. Here, we introduce how to dissect every (sub) segmental Glissonean pedicle from the first porta hepatis and perform standardized (sub) segmentectomy [from segment 1 (S1) to S8]. To summarize our methods of laparoscopic anatomical segmental and subsegmental liver resection. The Glisson sheath and liver capsule were separated along the Laennec membrane. The Glissonean pedicle could be isolated and transected with little or no parenchymal damage through this extra-Glissonean dissection approach. The basin of the (sub) segment was determined by the ischemia demarcation line or indocyanine green staining. The hepatic vein or intersegmental vein was also used to guide the plane of parenchymal transection. All segmental or subsegmental pedicles or even the pedicle of the cone unit could be dissected along the Laennec membrane using our novel technique through the first porta hepatis. The dorsal branches of S8, the branches of S4a and the paracaval portion branches (b/c vein) of the caudate lobe were the most difficult to dissect. The novel techniques of liver segmental and subsegmental pedicle anatomy is feasible for laparoscopic liver resection and can help accurately guide (sub) segmentectomy from S1 to S8.

Novel Technique for Liver Segmentation by Fluorescence Intensity Gradient Using Two Different Doses of Indocyanine Green.

Techniques involving dye injection or regional ischemia are commonly used for the precise identification of liver regions during hepatectomy.1,2 The visualization of regions with indocyanine green (ICG) has been widely used for liver segmentation.3 ICG is typically administered only once during each hepatectomy. We developed a threshold-adjustable Medical Imaging Projection System (MIPS) that projects ICG fluorescent images directly onto a patient's organ, which shows potential for real-time navigation.4,5 We report a case in which a fluorescence intensity gradient using two different doses of ICG was effective during anatomical right anterior sectionectomy. A 73-year-old man underwent one radiation treatment and three radiofrequency ablations for hepatocellular carcinoma over the past 4 years. A follow-up computed tomography scan revealed a low-density lesion in segment 8 with a portal vein tumor thrombus (PVTT), and we planned a right anterior sectionectomy. A direct approach to the pedicle with the PVTT was not recommended as portal patency in the anterior section was unknown; therefore, we identified the three liver regions using a fluorescence intensity gradient. The ICG fluorescence intensity gradient was established using two doses of ICG, which effectively identified the right posterior and anterior sections and the left lobe. MIPS clearly projected each section boundary onto the liver surface. Right anterior sectionectomy was successfully performed with no postoperative complications. Pathological examination revealed a negative surgical margin. Utilizing multiple ICG dosages for boundary identification of liver regions has potential use for anatomic hepatectomy.