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Liver cancer remains a leading cause of mortality worldwide, and precise diagnostic tools are essential for effective treatment planning. Liver Tumors (LTs) vary significantly in size, shape, and location, and can present with tissues of similar intensities, making automatically segmenting and classifying LTs from abdominal tomography images crucial and challenging. This review examines recent advancements in Liver Segmentation (LS) and Tumor Segmentation (TS) algorithms, highlighting their strengths and limitations regarding precision, automation, and resilience. Performance metrics are utilized to assess key detection algorithms and analytical methods, emphasizing their effectiveness and relevance in clinical contexts. The review also addresses ongoing challenges in liver tumor segmentation and identification, such as managing high variability in patient data and ensuring robustness across different imaging conditions. It suggests directions for future research, with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular methods. This paper contributes to a comprehensive understanding of current liver tumor detection techniques, provides a roadmap for future innovations, and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

BackgroundUltrasonography (US) is widely used for the diagnosis of liver tumors. However, the accuracy of the diagnosis largely depends on the visual perception of humans. Hence, we aimed to construct artificial intelligence (AI) models for the diagnosis of liver tumors in US.MethodsWe constructed three AI models based on still B-mode images: model-1 using 24,675 images, model-2 using 57,145 images, and model-3 using 70,950 images. A convolutional neural network was used to train the US images. The four-class liver tumor discrimination by AI, namely, cysts, hemangiomas, hepatocellular carcinoma, and metastatic tumors, was examined. The accuracy of the AI diagnosis was evaluated using tenfold cross-validation. The diagnostic performances of the AI models and human experts were also compared using an independent test cohort of video images.ResultsThe diagnostic accuracies of model-1, model-2, and model-3 in the four tumor types are 86.8%, 91.0%, and 91.1%, whereas those for malignant tumor are 91.3%, 94.3%, and 94.3%, respectively. In the independent comparison of the AIs and physicians, the percentages of correct diagnoses (accuracies) by the AIs are 80.0%, 81.8%, and 89.1% in model-1, model-2, and model-3, respectively. Meanwhile, the median percentages of correct diagnoses are 67.3% (range 63.6%–69.1%) and 47.3% (45.5%–47.3%) by human experts and non-experts, respectively.Conclusion The performance of the AI models surpassed that of human experts in the four-class discrimination and benign and malignant discrimination of liver tumors. Thus, the AI models can help prevent human errors in US diagnosis.

Background The global cellular landscape of the tumor microenvironment (TME) combining primary and metastatic liver tumors has not been comprehensively characterized. Methods Based on the scRNA-seq and spatial transcriptomic data of non-tumor liver tissues (NTs), primary liver tumors (PTs) and metastatic liver tumors (MTs), we performed the tissue preference, trajectory reconstruction, transcription factor activity inference, cell–cell interaction and cellular deconvolution analyses to construct a comprehensive cellular landscape of liver tumors. Results Our analyses depicted the heterogeneous cellular ecosystems in NTs, PTs and MTs. The activated memory B cells and effector T cells were shown to gradually shift to inhibitory B cells, regulatory or exhausted T cells in liver tumors, especially in MTs. Among them, we characterized a unique group of TCF7+ CD8+ memory T cells specifically enriched in MTs that could differentiate into exhausted T cells likely driven by the p38 MAPK signaling. With regard to myeloid cells, the liver-resident macrophages and inflammatory monocyte/macrophages were markedly replaced by tumor-associated macrophages (TAMs), with TREM2+ and UBE2C+ TAMs enriched in PTs, while SPP1+ and WDR45B+ TAMs in MTs. We further showed that the newly identified WDR45B+ TAMs exhibit an M2-like polarization and are associated with adverse prognosis in patients with liver metastases. Additionally, we addressed that endothelial cells display higher immune tolerance and angiogenesis capacity, and provided evidence for the source of the mesenchymal transformation of fibroblasts in tumors. Finally, the malignant hepatocytes and fibroblasts were prioritized as the pivotal cell populations in shaping the microenvironments of PTs and MTs, respectively. Notably, validation analyses by using spatial or bulk transcriptomic data in clinical cohorts concordantly emphasized the clinical significance of these findings. Conclusions This study defines the ontological and functional heterogeneities in cellular ecosystems of primary and metastatic liver tumors, providing a foundation for future investigation of the underlying cellular mechanisms.

Malignant liver tumors include primary malignant liver tumors and liver metastases. They are among the most common malignancies worldwide. The disease has a poor prognosis and poor overall survival, especially with liver metastases. Therefore, early detection and differentiation between malignant liver tumors are critical for patient treatment selection. The detection of cancer and the prediction of its origin is possible with a DNA methylation profile of the tumor DNA compared to that of normal cells, which reflects tissue differentiation and malignant transformation. New technologies enable the characterization of the tumor methylome in circulating tumor DNA (ctDNA), providing a variety of new ctDNA methylation biomarkers, which can provide additional information to clinical decision-making. Our review of the literature provides insight into methylation changes in ctDNA from patients with common malignant liver tumors and can serve as a starting point for further research.

Background Malignant liver tumor is one of the main causes of human death. In order to help physician better diagnose and make personalized treatment schemes, in clinical practice, it is often necessary to segment and visualize the liver tumor from abdominal computed tomography images. Due to the large number of slices in computed tomography sequence, developing an automatic and reliable segmentation method is very favored by physicians. However, because of the noise existed in the scan sequence and the similar pixel intensity of liver tumors with their surrounding tissues, besides, the size, position and shape of tumors also vary from one patient to another, automatic liver tumor segmentation is still a difficult task. Results We perform the proposed algorithm to the Liver Tumor Segmentation Challenge dataset and evaluate the segmentation results. Experimental results reveal that the proposed method achieved an average Dice score of 68.4% for tumor segmentation by using the designed network, and ASD, MSD, VOE and RVD improved from 27.8 to 21, 147 to 124, 0.52 to 0.46 and 0.69 to 0.73, respectively after performing adversarial training strategy, which proved the effectiveness of the proposed method. Conclusions The testing results show that the proposed method achieves improved performance, which corroborated the adversarial training based strategy can achieve more accurate and robustness results on liver tumor segmentation task.

Background The surgical evaluation of pediatric liver tumors is challenging, especially in difficult cases that require precise surgery. Three-dimensional visualization (3D) based on two-dimensional CT (2D) has been widely used. However, virtual surgical simulation (VS), which can provide more procedural details for specific patients, is worthy of further exploration. Methods Six pediatric liver tumor patients with the surgical difficulty increasing sequentially were selected. Recruited pediatric surgeons (15 junior and 15 senior) guided professional technicians in performing surgical evaluation using 2D, 3D, and VS sequentially. The patient-specific VS based on 3D can be constructed within the clinically permissible time window to meet clinical needs. Results For objective analysis, the scores in 3D Group and VS Group were significantly higher than those in 2D Group ( P  < 0.0001), and there was also a statistically significant difference between 3D Group and VS Group ( P  < 0.0001). As the difficulty increased, the 3D Group and VS Group consistently maintained higher scores. The scores of Junior Group using 2D were significantly lower than those of Senior Group ( P  < 0.0001), but there was no significant statistical difference in the scores of using 3D and VS. For subjective assessment, the scores of 3D Group and VS Group were significantly higher than those of 2D Group ( P  < 0.0001). VS was more aligned with clinical reality compared to 3D. Conclusion 3D and VS offer significant advantages in surgical evaluation compared with 2D, particularly for difficult cases and junior surgeons. The novel perspective and realistic experience provided by VS have attracted attention, and future research will further validate its potential clinical value in preoperative rehearsals and advanced training.

For both women and men over 60, liver cancer is the primary cause of cancer-related deaths. To help physicians to diagnose patients more accurately, computer-assisted imaging techniques have become increasingly important in recent years. Recent, deep Convolutional Neural Network (CNN) research has produced amazing improvements in image segmentation and classification. The same issue of diagnosing liver nodules in computed tomography (CT) scans is addressed in this research by introducing a novel Computer-Aided Detection (CAD) system that makes use of an Efficient Network (EfficientNet) image classification algorithm. Unlike CNN, which adjusts its network parameters arbitrarily, a set of predetermined scaling coefficients is used in the EfficientNet scaling technique to reliably scale the network’s breadth, depth, and resolution. Here the EfficientNet models are assessed by varying the input dimensions of the CT scans from The Liver Tumor Segmentation (LiTs) dataset. Finally, the performance evaluation shows that the input dimension 224×224 effectively classified the images and is superior to the other models evaluated with 0.991 AUC and 99.37% F1-Score, precision 99.44%, recall 99.30%, specificity 99.43%, and accuracy 99.36% for Kaggle datasets.

Background Hepatic myopericytoma (MPC) is an extremely rare pathological entity in the liver. Conversely, cystic hepatic lesions are a group of heterogeneous lesions encountered commonly in daily practice. Here, we report a unique case of the coexistence of primary hepatic MPC and multiple cystic hepatic lesions along with our perceptions on its diagnosis and treatment. Case presentation A 56-year-old female patient was found to have a left liver mass during a routine physical examination. Computer tomography (CT) and magnetic resonance imaging (MRI) confirmed the existence of a left hepatic neoplasm along with multiple hepatic cysts but could not exclude the possible malignant nature of the neoplasm. Computer tomography (CT) also identified an enlarged mediastinal lymph node with a maximum diameter of 4.3 cm, which further underwent core needle biopsy under CT guidance. A histopathological examination was performed to rule out malignancy. Afterwards, the patient underwent left hemihepatectomy to resect a solid tumor of 5.5 cm × 5 cm × 4.7 cm with multiple cystic lesions which were histopathologically examined to establish the diagnosis of myopericytoma with hepatic cysts. Postoperatively, the patient recovered from the surgery quickly without significant adverse events and was not found to have a reoccurrence of the primary pathological entity. Conclusions This is the first reported case of a patient with the co-existence of primary hepatic myopericytoma and multiple cystic hepatic lesions undergoing surgical treatment with eventual recovery.

Background: Thermal ablation of centrally located liver tumors carries an increased risk of bile duct injury due to their proximity to the biliary tree. We aim to evaluate whether biliary cooling using a nasobiliary tube can effectively mitigate bile duct injury during the ablation process. Methods: We retrospectively analyzed the data of 322 patients who underwent thermal ablation at Dalin Tzu Chi Hospital from July 2020 to June 2023 and identified those who received prophylactic biliary cooling during thermal ablation for central liver tumors. Data including demographics, tumor characteristics, procedural details, and clinical outcomes were analyzed. Results: Among the 322 patients who underwent thermal ablation, 9 with central liver tumors received prophylactic biliary cooling. The median distance between the tumor and the central bile duct was 1 mm (range: 0–4 mm), the temperature of the cold normal saline was 4 °C, and the mean volume of normal saline infused was 150 mL (range: 100–200 mL). Complete ablation was achieved in all patients in a single session without any biliary injury. One patient developed acute cholangitis after ENBD placement, which resolved with antibiotic therapy. Conclusions: Biliary cooling with 4 °C cold saline through a nasobiliary tube can improve the safety and effectiveness of thermal ablation for central liver tumors.

Malignant liver tumors, including primary malignant liver tumors and liver metastases, are among the most frequent malignancies worldwide. The disease carries a poor prognosis and poor overall survival, particularly in cases involving liver metastases. Consequently, the early detection and precise differentiation of malignant liver tumors are of paramount importance for making informed decisions regarding patient treatment. Significant research efforts are currently directed towards the development of diagnostic tools for different types of cancer using minimally invasive techniques. A prominent area of focus within this research is the evaluation of circulating microRNA, for which dysregulated expression is well documented in different cancers. Combining microRNAs in panels using serum or plasma samples derived from blood holds great promise for better sensitivity and specificity for detection of certain types of cancer.