Precise Liver Research Articles

Hepatocyte-derived extracellular vesicles regulate liver regeneration through a negative feedback mechanism.

While significant progress has been made in understanding various aspects of liver regeneration, the molecular mechanisms responsible for the initiation and termination of cell proliferation in the liver following massive tissue loss or injury of liver remain unknown. As it was previously shown, the loss of liver mass affects putative hepatocyte-specific mitogenic inhibitors in the blood. Although the presence of these putative inhibitors regulating precise liver regeneration has been described in numerous publications, they have never been identified. Extracellular vesicles (EVs) are nano-sized, membrane-limited structures secreted by cells into the extracellular space. Their proposed role is stable intercellular carriers of proteins and RNAs, predominantly micro-RNA, from secreted to recipient cells. Upon uptake by the recipient cells, EVs can significantly modulate their biological functions. In the present study, using in vivo and in vitro models, we demonstrate that hepatocyte proliferation and liver regeneration are regulated by EVs secreted by hepatocytes into the bloodstream. This regulation occurs through a negative feedback mechanism, which explains the precise regeneration of liver tissue after massive damage. We also demonstrate that an essential component of this mechanism is RNA carried by hepatocyte-derived EVs. Our findings open up a new and unexplored area of liver biology regarding the mechanisms involved in the precise regulation of liver regeneration after a massive tissue loss or injury. Further study of this mechanism will have a great influence on the development of new approaches to liver transplantation, various liver pathologies, and hepatic tumors.

Association between the paracaval branches of the caudate lobe and the three major hepatic veins in liver casts: Locating the cranial boundary of the caudate lobe.

According to Couinaud's definition, the cranial boundary of the caudate lobe is delineated by the three major hepatic veins. However, many branches of the caudate lobe go through the ceiling that is composed of these hepatic veins. The cranial boundary of the caudate lobe should be determined by employing the portal segmentation. We conducted a study based on the dissection of 37 colored resin liver casts to reveal the caudate branches of the liver. The paracaval portal vein branches (PCPvs) were defined as cranial portal branches from the main trunk or first-order branch of the portal vein distributed in front of the inferior vena cava, according to Kumon's classification. The PCVs were traced to reveal the cranial boundary of the caudate lobe. Results showed that in 18 cases (49%), the PCPvs reached the liver surface through the gap between the right and middle hepatic veins (type RM, n = 11), between the tiny branches of the middle hepatic vein (type M, n = 4), and between the middle and left hepatic veins (type ML, n = 3). The PCPvs did not reach the liver surface in 19 cases (type 0). No PCPvs reached the hepatic surface behind the right hepatic vein. Half of the PCPvs in the liver reached the hepatic surface beyond the boundary composed of the three major hepatic veins. Recognition of the PCPvs in the liver is indispensable to perform anatomically precise liver resections involving the major hepatic veins.

Ex-vivo Liver Resection and Autotransplantation for Liver Malignancy : A Large Volume Retrospective Clinical Study.

To assess the effectiveness of optimized ex-vivo liver resection and autotransplantation (ELRA) for treating liver malignancies. ELRA is a promising surgery for radical resection of conventionally unresectable tumors, despite the disappointing long-term prognosis during its developmental stages. A recent multicenter study reported 5-year overall and disease-free survival rates of 28% and 20.8%, respectively. We retrospectively analyzed data of patients who underwent ELRA for advanced liver cancers between 2009 and 2022. We applied ELRA via our novel surgical indication classification system where the surgical risk with curative intent for advanced liver malignancy was controllable using the ex-vivo approach. The ELRA was optimized for determinacy, predictability, and controllability via the precision liver surgery paradigm. Thirty-seven cases with liver malignancies were enrolled. The operative time and anhepatic phase duration were 649.6±200.0 and 261.2±74.5 minutes, respectively, while the intraoperative blood loss was 1902±1192mL. Negative resection margins were achieved in all patients, and the 90-day morbidity at Clavien-Dindo IIIa/IIIb and mortality rates were 27.0% and 24.3%. Post-ELRA 1-, 3-, and 5-year actual overall survival rates were 62.2%, 37.8%, and 35.1%, respectively, and 1-, 3-, and 5-year actual disease-free survival rates were 43.2%, 24.3%, and 18.9%, respectively. Long-term outcomes of ELRA under precision liver surgery for advanced liver malignancy were favorable. Appropriate criteria for disease selection and surgical indications and optimized procedures together can improve surgical treatment and patient prognosis.

Research on tumors segmentation based on image enhancement method

One of the most effective ways to treat liver cancer is to perform precise liver resection surgery, the key step of which includes precise digital image segmentation of the liver and its tumor. However, traditional liver parenchymal segmentation techniques often face several challenges in performing liver segmentation: lack of precision, slow processing speed, and computational burden. These shortcomings limit the efficiency of surgical planning and execution. In this work, the model initially describes in detail a new image enhancement algorithm that enhances the key features of an image by adaptively adjusting the contrast and brightness of the image. Then, a deep learning-based segmentation network was introduced, which was specially trained on the enhanced images to optimize the detection accuracy of tumor regions. In addition, multi-scale analysis techniques have been incorporated into the study, allowing the model to analyze images at different resolutions to capture more nuanced tumor features. In the presentation of the experimental results, the study used the 3Dircadb dataset to test the effectiveness of the proposed method. The experimental results show that compared with the traditional image segmentation method, the new method using image enhancement technology has significantly improved the accuracy and recall rate of tumor identification.

Precise liver tumor ablation: the clinical potential of US-US overlay fusion guidance.

Image-guided thermal ablation is a minimally invasive option for patients with early-stage hepatocellular carcinoma (HCC). However, the risk of local recurrence remains substantial because ultrasound (US) artifacts have a negative impact on the assessment of ablative margins during and immediately after ablation. Precise, real-time assessment of the ablation zone is key to reducing the risk of local tumor progression. With the advent of US image fusion technology, ablative margins can now be assessed three-dimensionally with greater accuracy. Therefore, US-US overlay fusion guidance has the potential to improve the local controllability of ablation in patients with HCC. This review discusses the US-US fusion guidance technique and its current clinical applications for hepatic interventions, with descriptions of its concept, methodology, and efficacy.

Semi-supervised segmentation of abdominal organs and liver tumor: uncertainty rectified curriculum labeling meets X-fuse

Precise liver tumors and associated organ segmentation hold immense value for surgical and radiological intervention, enabling anatomical localization for pre-operative planning and intra-operative guidance. Modern deep learning models for medical image segmentation have evolved from convolution neural networks to transformer architectures, significantly boosting global context understanding. However, accurate delineation especially of hepatic lesions remains an enduring challenge due to models’ predominant focus solely on spatial feature extraction failing to adequately characterize complex medical anatomies. Moreover, the relative paucity of expertly annotated medical imaging data restricts model exposure to diverse pathological presentations. In this paper, we present a three-phrased cascaded segmentation framework featuring an X-Fuse model that synergistically integrates spatial and frequency domain’s complementary information in dual encoders to enrich latent feature representation. To enhance model generalizability, building upon X-Fuse topology and taking advantage of additional unlabeled pathological data, our proposed integration of curriculum pseudo-labeling with Jensen–Shannon variance-based uncertainty rectification promotes optimized pseudo supervision in the context of semi-supervised learning. We further introduce a tumor-focus augmentation technique including training-free copy-paste and knowledge-based synthesis that show efficacy in simplicity, contributing to the substantial elevation of model adaptability on diverse lesional morphologies. Extensive experiments and modular evaluations on a holdout test set demonstrate that our methods significantly outperform existing state-of-the-art segmentation models in both supervised and semi-supervised settings, as measured by the Dice similarity coefficient, achieving superior delineation of bones (95.42%), liver (96.26%), and liver tumors (89.53%) with 16.41% increase comparing to V-Net on supervised-only and augmented-absent scenario. Our method marks a significant step toward the realization of more reliable and robust AI-assisted diagnostic tools for liver tumor intervention. We have made the codes publicly available [https://github.com/lyupengju/X-Fuse].

Benefits of a modified local precision liver resection using intraoperative laparoscopic ultrasound in the treatment and prognosis of patients with liver cancer.

Benefits of a modified local precision liver resection using intraoperative laparoscopic ultrasound in the treatment and prognosis of patients with liver cancer.

Facile engineering of aptamer-coupled silk fibroin encapsulated myogenic gold nanocomposites: investigation of antiproliferative activity and apoptosis induction.

Nanocomposites selectively induce cancer cell death, holding potential for precise liver cancer treatment breakthroughs. This study assessed the cytotoxicity of gold nanocomposites (Au NCs) enclosed within silk fibroin (SF), aptamer (Ap), and the myogenic Talaromyces purpureogenus (TP) against a human liver cancer cell (HepG2). The ultimate product, Ap-SF-TP@Au NCs, results from a three-step process. This process involves the myogenic synthesis of TP@Au NCs derived from TP mycelial extract, encapsulation of SF on TP@Au NCs (SF-TP@Au NCs), and the conjugation of Ap within SF-TP@Au NCs. The synthesized NCs are analyzed by various characteristic techniques. Ap-SF-TP@Au NCs induced potential cell death in HepG2 cells but exhibited no cytotoxicity in non-cancerous cells (NIH3T3). The morphological changes in cells were examined through various biochemical staining methods. Thus, Ap-SF-TP@Au NCs emerge as a promising nanocomposite for treating diverse cancer cells.

ResNet50-Boosted UNet for Improved Liver Segmentation Accuracy

Segmentation of the liver from abdominal CT images is difficult due to changes in form, density, and the presence of malignancies. This research describes a novel strategy to improve segmentation accuracy that uses UNet as a foundation architecture and ResNet50 as a backbone architecture. This integrated design automates feature selection and spatial awareness, overcoming limitations in previous models. Experimental evaluations using the LiTS dataset show higher performance. Specifically, using the LiTS dataset, our algorithm achieves a remarkable foreground accuracy of 99.81% in liver segmentation. These results outperform existing approaches, demonstrating UNet and ResNet50's potential as valuable tools for precise liver segmentation in clinical situations. The suggested system shows promise for application in diverse medical imaging tasks other than liver segmentation, demonstrating its versatility and effectiveness in enhancing machine-assisted medical diagnostics and decision-making processes.

Artificial intelligence in liver imaging: methods and applications.

Liver disease is regarded as one of the major health threats to humans. Radiographic assessments hold promise in terms of addressing the current demands for precisely diagnosing and treating liver diseases, and artificial intelligence (AI), which excels at automatically making quantitative assessments of complex medical image characteristics, has made great strides regarding the qualitative interpretation of medical imaging by clinicians. Here, we review the current state of medical-imaging-based AI methodologies and their applications concerning the management of liver diseases. We summarize the representative AI methodologies in liver imaging with focusing on deep learning, and illustrate their promising clinical applications across the spectrum of precise liver disease detection, diagnosis and treatment. We also address the current challenges and future perspectives of AI in liver imaging, with an emphasis on feature interpretability, multimodal data integration and multicenter study. Taken together, it is revealed that AI methodologies, together with the large volume of available medical image data, might impact the future of liver disease care.

Palm-Sized Wireless Transient Elastography System with Real-Time B-Mode Ultrasound Imaging Guidance: Toward Point-of-Care Liver Fibrosis Assessment.

Transient elastography (TE), recommended by the WHO, is an established method for characterizing liver fibrosis via liver stiffness measurement (LSM). However, technical barriers remain towards point-of-care application, as conventional TE requires wired connections, possesses a bulky size, and lacks adequate imaging guidance for precise liver localization. In this work, we report the design, phantom validation, and clinical evaluation of a palm-sized TE system that enables simultaneous B-mode imaging and LSM. The performance of this system was validated experimentally using tissue-equivalent reference phantoms (1.45-75 kPa). Comparative studies against other liver elastography techniques, including conventional TE and two-dimensional shear wave elastography (2D-SWE), were performed to evaluate its reliability and validity in adults with various chronic liver diseases. Intra- and inter-operator reliability of LSM were established by an elastography expert and a novice. A good agreement was observed between the Young's modulus reported by the phantom manufacturer and this system (bias: 1.1-8.6%). Among 121 patients, liver stiffness measured by this system and conventional TE were highly correlated (r = 0.975) and strongly agreed with each other (mean difference: -0.77 kPa). Inter-correlation of this system with conventional TE and 2D-SWE was observed. Excellent-to-good operator reliability was demonstrated in 60 patients (ICCs: 0.824-0.913). We demonstrated the feasibility of employing a fully integrated phased array probe for reliable and valid LSM, guided by real-time B-mode imaging of liver anatomy. This system represents the first technical advancement toward point-of-care liver fibrosis assessment. Its small footprint, along with B-mode guidance capability, improves examination efficiency and scales up screening for liver fibrosis.

Clinical efficacy of precision liver resection for primary liver cancer.

Precision liver resection is considered the gold standard in liver surgery. Therefore, optimizing the resection of lesions and minimizing unnecessary time of liver ischemia and hypoxia have become focal points. A total of 96 patients with primary liver cancer admitted to Cangzhou People's Hospital from January 2017 and December 2019 were included in this retrospective study, and divided into two groups according to the different surgical treatment, with 50 cases in the control group (conventional hepatic resection) and 46 cases in the observation group (precision liver resection). The surgical indicators, liver function, alpha-fetoprotein (AFP), complications, and three-year follow-up results were analyzed in the two groups. The operation time, intraoperative bleeding, hospital stay, and time of anal venting in the observation group were shorter than those in the control group (P<0.05). One week after surgery, AST, TBiL, ALT, and γ-GT levels decreased in both groups, with more significant decreases in the observation group than those in the control group (P<0.05). PCT and hs-CRP levels in the observation group were significantly lower than those in the control group (P<0.05) observation. The incidences of pleural effusion, bile leak, abdominal infection, pulmonary infection, as well as the total complication rates in the observation group were lower in the observation group than those in the control group (P<0.05). The follow-up data revealed that the observation group exhibited a lower recurrence rate observationand higher survival rate than the control group within 3 years, but these differences were not significant (P>0.05). Precision liver resection can effectively treat primary liver cancer, reduce the incidence of complications, and promote patient recovery after surgery.

Employing a Hybrid Convolutional Neural Network and Extreme Learning Machine for Precision Liver Disease Forecasting

Employing a Hybrid Convolutional Neural Network and Extreme Learning Machine for Precision Liver Disease Forecasting

Liver volumetric and anatomic assessment in living donor liver transplantation: The role of modern imaging and artificial intelligence.

The shortage of deceased donor organs has prompted the development of alternative liver grafts for transplantation. Living-donor liver transplantation (LDLT) has emerged as a viable option, expanding the donor pool and enabling timely transplantation with favorable graft function and improved long-term outcomes. An accurate evaluation of the donor liver's volumetry (LV) and anatomical study is crucial to ensure adequate future liver remnant, graft volume and precise liver resection. Thus, ensuring donor safety and an appropriate graft-to-recipient weight ratio. Manual LV (MLV) using computed tomography has traditionally been considered the gold standard for assessing liver volume. However, the method has been limited by cost, subjectivity, and variability. Automated LV techniques employing advanced segmentation algorithms offer improved reproducibility, reduced variability, and enhanced efficiency compared to manual measurements. However, the accuracy of automated LV requires further investigation. The study provides a comprehensive review of traditional and emerging LV methods, including semi-automated image processing, automated LV techniques, and machine learning-based approaches. Additionally, the study discusses the respective strengths and weaknesses of each of the aforementioned techniques. The use of artificial intelligence (AI) technologies, including machine learning and deep learning, is expected to become a routine part of surgical planning in the near future. The implementation of AI is expected to enable faster and more accurate image study interpretations, improve workflow efficiency, and enhance the safety, speed, and cost-effectiveness of the procedures. Accurate preoperative assessment of the liver plays a crucial role in ensuring safe donor selection and improved outcomes in LDLT. MLV has inherent limitations that have led to the adoption of semi-automated and automated software solutions. Moreover, AI has tremendous potential for LV and segmentation; however, its widespread use is hindered by cost and availability. Therefore, the integration of multiple specialties is necessary to embrace technology and explore its possibilities, ranging from patient counseling to intraoperative decision-making through automation and AI.

Beyond radiologist-level liver lesion detection on multi-phase contrast-enhanced CT images by deep learning

Accurate detection of liver lesions from multi-phase contrast-enhanced CT (CECT) scans is a fundamental step for precise liver diagnosis and treatment. However, the analysis of multi-phase contexts is heavily challenged by the misalignment caused by respiration coupled with the movement of organs. Here, we proposed an AI system for multi-phase liver lesion segmentation (named MULLET) for precise and fully automatic segmentation of real-patient CECT images. MULLET enables effectively embedding the important ROIs of CECT images and exploring multi-phase contexts by introducing a transformer-based attention mechanism. Evaluated on 1,229 CECT scans from 1,197 patients, MULLET demonstrated significant performance gains in terms of Dice, Recall, and F2 score, which are 5.80%, 6.57%, and 5.87% higher than state of the arts, respectively. MULLET has been successfully deployed in real-world settings. The deployed AI web server provides a powerful system to boost clinical workflows of liver lesion diagnosis and could be straightforwardly extended to general CECT analyses.

Two-step artificial intelligence algorithm for liver segmentation automates anatomic virtual hepatectomy.

Anatomic virtual hepatectomy with precise liver segmentation for hemilivers, sectors, or Couinaud's segments using conventional three-dimensional simulation is not automated and artificial intelligence (AI)-based algorithms have not yet been applied. Computed tomography data of 174 living-donor candidates for liver transplantation (training data) were used for developing a new two-step AI algorithm to automate liver segmentation that was validated in another 51 donors (validation data). The Pure-AI (no human intervention) and ground truth (GT, full human intervention) data groups were compared. In the Pure-AI group, the median Dice coefficients of the right and left hemilivers were highly similar, 0.95 and 0.92, respectively; sectors, posterior to lateral: 0.86-0.92, and Couinaud's segments 1-8: 0.71-0.89. Labeling of the first-order branch as hemiliver, right or left portal vein perfectly matched; 92.8% of the second-order (sectors); 91.6% of third-order (segments) matched between the Pure-AI and GT data. The two-step AI algorithm for liver segmentation automates anatomic virtual hepatectomy. The AI-based algorithm correctly divided all hemilivers, and more than 90% of the sectors and segments.

Super‐stable homogeneous embolic agents advance the treatment of hepatocellular carcinoma

Super‐stable homogeneous embolic agents advance the treatment of hepatocellular carcinoma

SPECT and CT misregistration reduction in [99mTc]Tc-MAA SPECT/CT for precision liver radioembolization treatment planning.

Respiration and body movement induce misregistration between static [99mTc]Tc-MAA SPECT and CT, causing lung shunting fraction (LSF) and tumor-to-normal liver ratio (TNR) errors for 90Y radioembolization planning. We aim to alleviate the misregistration between [99mTc]Tc-MAA SPECT and CT using two registration schemes on simulation and clinical data. In the simulation study, 70 XCAT phantoms were modeled. The SIMIND Monte Carlo program and OS-EM algorithm were used for projection generation and reconstruction, respectively. Low-dose CT (LDCT) at end-inspiration was simulated for attenuation correction (AC), lungs and liver segmentation, while contrast-enhanced CT (CECT) was simulated for tumor and perfused liver segmentation. In the clinical study, 16 patient data including [99mTc]Tc-MAA SPECT/LDCT and CECT with observed SPECT and CT mismatch were analyzed. Two liver-based registration schemes were studied: SPECT registered to LDCT/CECT and vice versa. Mean count density (MCD) of different volumes-of-interest (VOIs), normalized mutual information (NMI), LSF, TNR, and maximum injected activity (MIA) based on the partition model before and after registration were compared. Wilcoxon signed-rank test was performed. In the simulation study, compared to before registration, registrations significantly reduced estimation errors of MCD of all VOIs, LSF (Scheme 1: - 100.28%, Scheme 2: - 101.59%), and TNR (Scheme 1: - 7.00%, Scheme 2: - 5.67%), as well as MIA (Scheme 1: - 3.22%, Scheme 2: - 2.40%). In the clinical study, Scheme 1 reduced 33.68% LSF and increased 14.75% TNR, while Scheme 2 reduced 38.88% LSF and increased 6.28% TNR compared to before registration. One patient may change from 90Y radioembolization untreatable to treatable and other patients may change the MIA up to 25% after registration. NMI between SPECT and CT was significantly increased after registrations in both studies. Registration between static [99mTc]Tc-MAA SPECT and corresponding CTs is feasible to reduce their spatial mismatch and improve dosimetric estimation. The improvement of LSF is larger than TNR. Our method can potentially improve patient selection and personalized treatment planning for liver radioembolization.

The evolution of anatomical hepatectomy: Past, present, and future

The liver contains a complex structure of blood vessels and bile ducts, and the vascular structure is highly variable. The anatomical segmentation of the liver is still controversial, and the Couinaud segmentation method based on the portal vein is more widely used in clinical practice. The treatment of liver tumors and other lesions is closely related to the liver anatomy. The mechanism of liver tumor invasion and metastasis is complex, and it is currently believed that tumor invasion mainly spreads along the portal vein. Anatomic liver resection is an important surgical method for liver diseases, especially liver tumors. This article reviews the vascular structure of the liver, the development of anatomical hepatectomy, blood flow control, surgical planning, intraoperative navigation, minimally invasive surgery, and precise hepatectomy. Anatomic liver resection is a part of precision liver surgery, which is becoming increasingly more precise in terms of surgical evaluation, surgical planning, and surgical operation. New technologies will facilitate precision surgery with less trauma and greater benefits for patients. With the development and advancement of technology, image-based surgical planning and intraoperative surgical navigation will become more widely used in precision liver surgery.

Controlled fabrication of functional liver spheroids with microfluidic flow cytometric printing

Multicellular liver spheroids are 3D culture models useful in the development of therapies for liver fibrosis. While these models can recapitulate fibrotic disease, current methods for generating them via random aggregation are uncontrolled, yielding spheroids of variable size, function, and utility. Here, we report fabrication of precision liver spheroids with microfluidic flow cytometric printing. Our approach fabricates spheroids cell-by-cell, yielding structures with exact numbers of different cell types. Because spheroid function depends on composition, our precision spheroids have superior functional uniformity, allowing more accurate and statistically significant screens compared to randomly generated spheroids. The approach produces thousands of spheroids per hour, and thus affords a scalable platform by which to manufacture single-cell precision spheroids for disease modeling and high throughput drug testing.