Search Results (83)

Background: Liver resection of a caudate lobe is a challenging procedure in both open and minimally invasive approaches. The reason is mainly related to its anatomical position: segment 1 (S1) lies on the inferior vein cava, behind the main and the left portal veins, and below the hepatic veins. This meta-analysis aimed to assess the results of laparoscopic liver resection (LLR) versus open liver resection (OLR) for S1 resection. Methods: Available literature up to June 2024 was retrieved from the Medline and Embase databases. A systematic review with a meta-analysis was carried out to investigate the safety and efficacy of LLR for the S1 segment. Results: Six studies including 292 patients (LLR: n = 132; OLR: n = 160) were selected for the meta-analysis. The OLR cohort showed higher estimated blood loss (EBL) (MD: 140.1, 95% CI 49.3–130.8; p = 0.011) and longer length of hospital stay (MD: 3, 95% CI 1.8–4.2; p = 0.001). No differences in severe postoperative morbidity, overall morbidity, R1 resection rates, transfusion rates, operative time, and duration of Pringle maneuvers were shown. Conclusion: LLR for lesions located in S1 is safe and effective and may be associated with lower EBL and shorter length of stay than OLR. Further larger prospective studies are needed to confirm such results. Full article

Background: Single-port laparoscopic hepatectomy is a minimally invasive modality for the treatment of benign and malignant liver lesions. Due to the method’s technical challenges, it is suitable for experienced hepatobiliary surgeons and selected groups of patients. The aim of this study was to evaluate the results of a single Slovenian center performing single-port laparoscopic hepatectomy with a literature overview. Methods: A single-center retrospective consecutive case series of the twenty-six patients with liver disease operated with the single-port technique from January 2018 to July 2024 at the Department of Abdominal Surgery at the University Medical Centre, Ljubljana, was performed. Lesions were located in easy-to-treat segments. Operative time, conversion rate, length of hospital stay, and surgical complications were recorded and evaluated. Results: We performed twenty-six single-port laparoscopic liver resections (median age 63.5, range 31 to 79 years). The mean operative time was 92 ± 31 min. None of the cases were converted to multi-port laparoscopic or open surgery. Safe resection margins were obtained in cases of malignant disease. The mean hospital stay was 4 days. The post-operative complication rate involving intervention was 7% (2/26). The incisional hernia rate was 11.5% (3/26). No life-threatening surgical complications or morbidity were noted. Conclusions: Single-port laparoscopic hepatectomy is a safe and feasible technique for the resection of benign and malignant liver lesions in the hands of skilled and well-trained hepatobiliary surgeons. Full article

Automatic liver and tumor segmentation in contrast-enhanced magnetic resonance imaging (CE-MRI) images are of great value in clinical practice as they can reduce surgeons’ workload and increase the probability of success in surgery. However, this is still a challenging task due to the complex background, irregular shape, and low contrast between the organ and lesion. In addition, the size, number, shape, and spatial location of liver tumors vary from person to person, and existing automatic segmentation models are unable to achieve satisfactory results. In this work, drawing inspiration from self-attention mechanisms and multitask learning, we propose a segmentation network that leverages Swin-Transformer as the backbone, incorporating self-supervised learning strategies to enhance performance. In addition, accurately segmenting the boundaries and spatial location of liver tumors is the biggest challenge. To address this, we propose a multitask learning strategy based on segmentation and signed distance map (SDM), incorporating an attention gate into the skip connections. The strategy can perform liver tumor segmentation and SDM regression tasks simultaneously. The incorporation of the SDM regression branch effectively improves the detection and segmentation performance for small objects since it imposes additional shape and global constraints on the network. We performed comprehensive evaluations, both quantitative and qualitative, of our approach. The model we proposed outperforms existing state-of-the-art models in terms of DSC, 95HD, and ASD metrics. This research provides a valuable solution that lessens the burden on surgeons and improves the chances of successful surgeries. Full article

Background and Objectives: Current guidelines recommend contrast-enhanced CT/MRI as confirmatory imaging tests for diagnosing hepatocellular carcinoma (HCC). However, these modalities are not always able to differentiate HCC from benign/dysplastic nodules that are commonly observed in cirrhotic livers. Consequently, many lesions require either pathological confirmation via invasive biopsy or surveillance imaging after 3–6 months, which results in delayed diagnosis and treatment. We aimed to develop noninvasive imaging biomarkers of liver cell size and cellularity, using magnetic resonance imaging (MRI), and to assess their utility in identifying HCC. Methods: MR cytometry combines measurements of water diffusion rates over different times corresponding to probing cellular microstructure at different spatial scales. Maps of microstructural properties, such as cell size and cellularity, are derived by fitting voxel values in multiple diffusion-weighted images to a three-compartment (blood, intra-, and extracellular water) model of the MRI signal. This method was validated in two phases: (1) histology-driven simulations, utilizing segmented histological images of different liver pathologies, and (2) ex vivo MR cytometry performed on fixed human liver specimens. Results: Both simulations and ex vivo MR cytometry of fixed human liver specimens demonstrated that HCC exhibits significantly smaller cell sizes and higher cellularities compared to normal liver and cirrhotic regenerative nodules. Conclusion: This study highlights the potential of MR cytometry to differentiate HCC from non-HCC lesions by quantifying cell size and cellularity in liver tissues. Our findings provide a strong foundation for further research into the role of MR cytometry in the noninvasive early diagnosis of HCC. Full article

Mucinous cystic neoplasm of the liver (MCN-L) is a rare benign tumor accounting for less than 5% of all liver cysts, with MCN-L in the hilar region being exceptionally uncommon and often misdiagnosed due to its complex presentation. A 48-year-old woman presented with obstructive jaundice following initial laparoscopic drainage of hepatic cysts, where pathology initially indicated benign cystic lesions. Months later, imaging revealed an enlarged cystic lesion in the left liver lobe with intrahepatic bile duct dilation. Further evaluations, including ultrasound, enhanced CT, and MRI, confirmed a large cystic lesion compressing the intrahepatic bile ducts. After a multidisciplinary discussion, hepatic cyst puncture and drainage were performed, temporarily alleviating jaundice. However, she returned with yellowish-brown drainage fluid and worsening jaundice, prompting cyst wall resection. Postoperative pathology confirmed MCN-L. Three months later, jaundice subsided, and a hepatic resection of segment 4 was performed, with pathology confirming low-grade MCN-L. At a 12-month follow-up, the patient showed no abnormalities. This case highlights the diagnostic and therapeutic challenges of MCN-L in the hilar region, as it can easily be mistaken for other liver cystic lesions on imaging. Pathologic examination is essential for definitive diagnosis, and early radical surgical resection is critical to improve prognosis and reduce the risk of malignancy and recurrence. Full article

Liver cancer is a major global health challenge, significantly contributing to mortality rates. The accurate segmentation of liver and tumors from clinical CT images plays a crucial role in selecting therapeutic strategies for liver disease and treatment monitoring but remains challenging due to liver shape variability, proximity to other organs, low contrast between tumors and healthy tissues, and unclear lesion boundaries. To address these challenges, we propose the Deep Residual Dual-Attention Network (DRDA-Net), a novel model for end-to-end liver and tumor segmentation. DRDA-Net integrates a Residual UNet architecture with dual-attention mechanisms, multi-scale tile and patch extraction, and an Ensemble method. The dual-attention mechanisms enhance focus on key regions, addressing variations in size, shape, and intensity, while the multi-scale approach captures fine details and broader contexts. Additionally, we introduce a unique pre-processing pipeline employing a two-channel denoising technique using convolutional neural networks (CNNs) and stationary wavelet transforms (SWTs) to reduce noise while preserving structural details. Evaluated on the 3DIRCADb dataset, DRDA-Net achieved Dice scores of 97.03% and 75.4% for liver and tumor segmentation, respectively, outperforming state-of-the-art methods. These results demonstrate the model’s effectiveness in overcoming segmentation challenges and highlight its potential to improve liver cancer diagnostics and treatment planning. Full article

Background/Objectives: Radiomic features exhibit a correlation with tumor size on pretreatment images. However, on post-treatment images, this association is influenced by treatment efficacy and varies between responders and non-responders. This study introduces a novel model, called baseline-referenced Delta radiomics, which integrates the association between radiomic features and tumor size into Delta radiomics to predict chemotherapy response in liver metastases from breast cancer (BC) and colorectal cancer (CRC). Materials and Methods: A retrospective study analyzed contrast-enhanced computed tomography (CT) scans of 83 BC patients and 84 CRC patients. Among these, 57 BC patients with 106 liver lesions and 37 CRC patients with 109 lesions underwent post-treatment imaging after systemic chemotherapy. Radiomic features were extracted from up to three lesions per patient following manual segmentation. Tumor response was assessed by measuring the longest diameter and classified according to RECIST 1.1 criteria as progressive disease (PD), partial response (PR), or stable disease (SD). Classification models were developed to predict chemotherapy response using pretreatment data only, Delta radiomics, and baseline-referenced Delta radiomics. Model performance was evaluated using confusion matrix metrics. Results: Baseline-referenced Delta radiomics performed comparably or better than established radiomics models in predicting tumor response in chemotherapy-treated patients with liver metastases. The sensitivity, specificity, and balanced accuracy in predicting response ranged from 0.66 to 0.97, 0.81 to 0.97, and 80% to 90%, respectively. Conclusions: By integrating the relationship between radiomic features and tumor size into Delta radiomics, baseline-referenced Delta radiomics offers a promising approach for predicting chemotherapy response in liver metastases from breast and colorectal cancer. Full article

The liver is a frequent focus in radiology due to its diverse pathology, and artificial intelligence (AI) could improve diagnosis and management. This systematic review aimed to assess and categorize research studies on AI applications in liver radiology from 2018 to 2024, classifying them according to areas of interest (AOIs), AI task and imaging modality used. We excluded reviews and non-liver and non-radiology studies. Using the PRISMA guidelines, we identified 6680 articles from the PubMed/Medline, Scopus and Web of Science databases; 1232 were found to be eligible. A further analysis of a subgroup of 329 studies focused on detection and/or segmentation tasks was performed. Liver lesions were the main AOI and CT was the most popular modality, while classification was the predominant AI task. Most detection and/or segmentation studies (48.02%) used only public datasets, and 27.65% used only one public dataset. Code sharing was practiced by 10.94% of these articles. This review highlights the predominance of classification tasks, especially applied to liver lesion imaging, most often using CT imaging. Detection and/or segmentation tasks relied mostly on public datasets, while external testing and code sharing were lacking. Future research should explore multi-task models and improve dataset availability to enhance AI’s clinical impact in liver imaging. Full article

Background: Medullary carcinoma of the small intestine is an exceptionally rare subtype of gastrointestinal cancer, characterized by its solid growth pattern and lack of glandular structures, which complicate timely diagnosis. During the COVID-19 pandemic, diagnostic delays for rare cancers became increasingly common due to the prioritization of COVID-related cases and patient reluctance to seek medical attention. Methods and Result: We present the case of a 70-year-old male initially misdiagnosed with COVID-19, whose persistent symptoms led to the eventual discovery of medullary carcinoma. Imaging studies revealed focal lesions in the liver, spleen, and thickened small intestinal walls, prompting surgical resection of a 16 cm intestinal segment. Histopathological examination confirmed medullary carcinoma with lymph node and liver metastases, supported by immunohistochemistry, which showed positive markers (calretinin, pancytokeratin, cytokeratin 7) and excluded other malignancies. Conclusions: The diagnostic delay, exacerbated by the pandemic, highlights the challenges of distinguishing rare cancers from more common conditions during global health crises. This case underscores the importance of advanced diagnostic techniques, such as immunohistochemistry, for accurate identification. Maintaining robust cancer diagnostic pathways during emergencies is crucial to avoid delays in treatment. Future research should focus on improving screening methods for rare cancers and developing resilient healthcare systems to mitigate similar challenges in future crises. Full article

Background/Objectives: Recent advancements in artificial intelligence (AI) have spurred interest in developing computer-assisted analysis for imaging examinations. However, the lack of high-quality datasets remains a significant bottleneck. Labeling instructions are critical for improving dataset quality but are often lacking. This study aimed to establish a liver MRI segmentation protocol and assess its impact on annotation quality and inter-reader agreement. Methods: This retrospective study included 20 patients with chronic liver disease. Manual liver segmentations were performed by a radiologist in training and a radiology technician on T2-weighted imaging (wi) and T1wi at the portal venous phase. Based on the inter-reader discrepancies identified after the first segmentation round, a segmentation protocol was established, guiding the second round of segmentation, resulting in a total of 160 segmentations. The Dice Similarity Coefficient (DSC) assessed inter-reader agreement pre- and post-protocol, with a Wilcoxon signed-rank test for per-volume analysis and an Aligned-Rank Transform (ART) for repeated measures analyses of variance (ANOVA) for per-slice analysis. Slice selection at extreme cranial or caudal liver positions was evaluated using the McNemar test. Results: The per-volume DSC significantly increased after protocol implementation for both T2wi (p < 0.001) and T1wi (p = 0.03). Per-slice DSC also improved significantly for both T2wi and T1wi (p < 0.001). The protocol reduced the number of liver segmentations with a non-annotated slice on T1wi (p = 0.04), but the change was not significant on T2wi (p = 0.16). Conclusions: Establishing a liver MRI segmentation protocol improves annotation robustness and reproducibility, paving the way for advanced computer-assisted analysis. Moreover, segmentation protocols could be extended to other organs and lesions and incorporated into guidelines, thereby expanding the potential applications of AI in daily clinical practice. Full article

Bacterial liver parenchymal infections in dogs are rarely documented, and their imaging characteristics are scarce in the veterinary literature, especially in Computed Tomography (CT). This retrospective multicentric study aimed to describe the CT characteristics of parenchymal bacterial liver infection and abscessation in dogs and compare them with the human literature. Twenty dogs met the inclusion criteria. All dogs, except one, showed discrete hepatic lesions consistent with pyogenic liver abscess (19/20). A single case showed diffuse liver changes, which was diagnosed with granulomatous bacterial hepatitis (1/20). Multifocal lesions were associated with the presence of abdominal pain (p = 0.023). CT characteristics of pyogenic liver abscesses in our study resemble those described in the human literature, with multifocal (14/19) or single (5/19), round or ovoid (19/19), hypoattenuating hepatic lesions, which are better visualised in post-contrast images. Pyogenic liver abscesses can also show features such as the “cluster sign” (8/19), transient arterial segmental enhancement (6/10), rim enhancement (6/19), and intralesional gas (4/19). Additional CT findings, such as local lymphadenomegaly (18/20), peritoneal fat stranding (14/20), and peritoneal fluid (13/20), are also commonly observed. Full article

The growth rate of in situ-induced hepatic lesions in an Oncopig large animal model is quantitatively assessed. Oncopigs (n = 9) received baseline triple-phase CT scans prior to lesion induction. Lesions were subsequently induced by delivering the Ad-Cre vector to four locations in the liver. Triple-phase CT scans were obtained weekly to track the growth of the lesions. Animals were sacrificed at 14, 21, or 28 days (n = 3 in each group). The overall success rate of lesion generation was ~78%. Histopathology sections consistently revealed lesions that were highly inflammatory and consisted of a large leukocyte population without clear evidence of carcinomas. Lesions presented within imaging as hypovascular, low attenuating masses with slight contrast enhancement around the margins but little to no enhancement within the lesions themselves. The observed lesions were manually segmented on the venous phase image. Segmentation volumes were fitted to a logistic growth and decay model. Several lesions observed at earlier time points in the 28-day group had fully regressed by the time of the necropsy. The overall trend of rapid growth for the first 21 days, with spontaneous regression of the lesions being observed from day 21 to 28, suggests that the optimal window for experimental studies may be from days 14 to 21. The data and mathematical models generated from this study may be used for future computational models; however, the current model presented has moderate clinical relevance because many induced tumors resolved spontaneously within a few weeks. Awareness and careful consideration of the modest relevance and limitations of the model are advisable for each specific use case. Full article

Abdominal CT images are important clues for diagnosing liver cancer lesions. However, liver cancer presents challenges such as significant differences in tumor size, shape, and location, which can affect segmentation accuracy. To address these challenges, we propose an end-to-end 3D segmentation algorithm, RMCNet. In the shallow encoding part of RMCNet, we incorporated a 3D multiscale convolution (3D-Multiscale Convolution) module to more effectively extract tumors of varying sizes. Moreover, the convolutional block attention module (CBAM) is used in the encoding part to help the model focus on both the shape and location of tumors. Additionally, a residual path is introduced in each encoding layer to further enrich the extracted feature maps. Our method achieved DSC scores of 76.56% and 72.96%, JCC scores of 75.82% and 71.25%, HD values of 11.07 mm and 17.06 mm, and ASD values of 2.54 mm and 10.51 mm on the MICCAI 2017 Liver Tumor Segmentation public dataset and the 3Dircadb-01 public dataset, respectively. Compared to other methods, RMCNet demonstrates superior segmentation performance and stronger generalization capability. Full article

Introduction: A rare case of atraumatic liver laceration associated with hemoperitoneum is presented in a patient with amyloidosis who came to the hospital for abdominal pain. Case Presentation: The imaging findings reveal significant hepatomegaly with finely heterogeneous hepatic density and subcapsular hypo-dense streaks in segments VI and VII, likely representing lesions. Post-contrast enhancement shows a punctiform contrast medium extravasation within the subhepatic fluid collection, visible from the arterial phase and intensifying in subsequent study phases. Discussion: These imaging findings suggest an atraumatic hepatic laceration, a diagnosis confirmed by the presence of hemoperitoneum distributed bilaterally under the diaphragm, in the paracolic gutters, along the mesentery root, and predominantly in the peri-hepatic region. Conclusion: The detailed imaging analysis provided critical insights into the diagnosis and management of this rare clinical presentation. Full article

Accurate analysis of Fluorodeoxyglucose (FDG)-Positron Emission Tomography (PET) images is crucial for the diagnosis, treatment assessment, and monitoring of patients suffering from various cancer types. FDG-PET images provide valuable insights by revealing regions where FDG, a glucose analog, accumulates within the body. While regions of high FDG uptake include suspicious tumor lesions, FDG also accumulates in non-tumor-specific regions and organs. Identifying these regions is crucial for excluding them from certain measurements, or calculating useful parameters, for example, the mean standardized uptake value (SUV) to assess the metabolic activity of the liver. Manual organ delineation from FDG-PET by clinicians demands significant effort and time, which is often not feasible in real clinical workflows with high patient loads. For this reason, this study focuses on automatically identifying key organs with high FDG uptake, namely the brain, left cardiac ventricle, kidneys, liver, and bladder. To this end, an ensemble approach is adopted, where a three-dimensional Attention U-Net (3D AU-Net) is employed for robust three-dimensional analysis, while a two-dimensional U-Net (2D U-Net) is utilized for analysis in the coronal plane. The 3D AU-Net demonstrates highly detailed organ segmentations, but also includes many false positive regions. In contrast, 2D U-Net achieves higher reliability with minimal false positive regions, but lacks the 3D details. Experiments conducted on a subset of the public AutoPET dataset with 60 PET scans demonstrate that the proposed ensemble model achieves high accuracy in segmenting the required organs, surpassing current state-of-the-art techniques, and supporting the potential utilization of the proposed methodology in accelerating and enhancing the clinical workflow of cancer patients. Full article