Radiomics as a tool for prognostic prediction in transarterial chemoembolization for hepatocellular carcinoma: a systematic review and meta-analysis.

INTRODUCTION: Transarterial chemoembolization (TACE) is one of the predominant locoregional therapeutic modalities for addressing hepatocellular carcinoma (HCC). However, achieving precise prognostic predictions and effective patient selection remains a challenging pursuit. The primary objective of this systematic review and meta-analysis is to evaluate the efficacy of radiomics in forecasting the prognosis associated with TACE treatment. METHODS: A comprehensive exploration of pertinent original studies was undertaken, encompassing databases of PubMed, Web of Science and Embase. The studies' quality was meticulously evaluated employing the quality assessment of diagnostic accuracy studies 2 (QUADAS-2), the radiomics quality score (RQS) and the METhodological RadiomICs Score (METRICS). Pooled statistics, along with 95% confidence intervals (95% CI), were computed for sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR). Additionally, a summary receiver operating characteristic curve (sROC) was generated. To discern potential sources of heterogeneity, meta-regression and subgroup analyses were performed. RESULTS: The systematic review incorporated 29 studies, comprising a total of 5483 patients, with 14 studies involving 2691 patients qualifying for inclusion in the meta-analysis. The assessed studies exhibited commendable quality with regard to bias risk, with mean RQS of 12.90 ± 5.13 (35.82% ± 14.25%) and mean METRICS of 62.98% ± 14.58%. The pooled sensitivity was 0.83 (95% CI: 0.78-0.87), specificity was 0.86 (95% CI: 0.79-0.92), PLR was 6.13 (95% CI: 3.79-9.90), and NLR was 0.20 (95% CI: 0.15-0.27). The area under the sROC was 0.90 (95% CI: 0.87-0.93). Significant heterogeneity within all the included studies was observed, while meta-regression and subgroup analyses revealed homogeneous and promising findings in subgroups where principal methodological variables such as modeling algorithms, imaging modalities, and imaging phases were specified. CONCLUSION: Radiomics models have exhibited robust predictive capabilities concerning prognosis subsequent to TACE, thereby presenting promising prospects for clinical translation.

Liver fibrosis as a predictor of liver failure and outcome following ALPPS among patients with primary liver cancer.

The influence of liver fibrosis on the rate of liver regeneration and complications following ALPPS has yet to be fully understood. This study aimed to scrutinize the effects of liver fibrosis on the postoperative complications, and prognosis subsequent to ALPPS. Clinical data were collected from patients with primary liver cancer who underwent ALPPS at Peking Union Medical College Hospital between May 2014 and October 2022. The degree of liver fibrosis was assessed using haematoxylin-eosin staining and Sirius red staining. This study encompassed thirty patients who underwent ALPPS for primary liver cancer, and there were 23 patients with hepatocellular carcinoma, 5 with cholangiocarcinoma, and 2 with combined hepatocellular-cholangiocarcinoma. The impact of severe liver fibrosis on the rate of liver regeneration was not statistically significant (P = 0.892). All patients with severe complications belonged to the severe liver fibrosis group. Severe liver fibrosis exhibited a significant association with 90 days mortality (P = 0.014) and overall survival (P = 0.012). Severe liver fibrosis emerges as a crucial risk factor for liver failure and perioperative mortality following the second step of ALPPS. Preoperative liver function impairment is an important predictive factor for postoperative liver failure.

Endovascular Embolization for Idiopathic Serial Right Internal Mammary Artery Aneurysms.

CLINICAL IMAPCT: This article showed images of a patient with idiopathic serial right internal mammary artery true aneurysms treated by endovascular embolization, which is a rare diagnosis and should be considered in differential diagnosis of paratracheal mass.

LINC02257 regulates colorectal cancer liver metastases through JNK pathway.

Background: Long noncoding RNAs (lncRNAs) have emerged as critical regulators of colorectal cancer (CRC) progression, but their roles and underlying mechanisms in colorectal cancer liver metastases (CRLMs) remain poorly understood. Methods: To explore the expression patterns and functions of lncRNAs in CRLMs, we analyzed the expression profiles of lncRNAs in CRC tissues using the TCGA database and examined the expression patterns of lncRNAs in matched normal, CRC, and CRLM tissues using clinical samples. We further investigated the biological roles of LINC02257 in CRLM using in vitro and in vivo assays, and verified its therapeutic potential in a mouse model of CRLM. Results: Our findings showed that LINC02257 was highly expressed in metastatic CRC tissues and its expression was negatively associated with overall survival. Functionally, LINC02257 promoted CRC cell growth, migration, metastasis, and inhibited cell apoptosis in vitro, and enhanced liver metastasis in vivo. Mechanistically, LINC02257 up-regulated phosphorylated c-Jun N-terminal kinase (JNK) to promote CRLM. Conclusions: Our study revealed that LINC02257 played a key role in the proliferation and metastasis of CRC cells through the LINC02257/JNK axis. Targeting this axis may represent a promising therapeutic strategy for the treatment of liver metastases in patients with CRC.

HCCDB v2.0: Decompose Expression Variations by Single-cell RNA-seq and Spatial Transcriptomics in HCC.

Large-scale transcriptomic data are crucial for understanding the molecular features of hepatocellular carcinoma (HCC). Integrated 15 transcriptomic datasets of HCC clinical samples, the first version of HCC database (HCCDB v1.0) was released in 2018. Through the meta-analysis of differentially expressed genes and prognosis-related genes across multiple datasets, it provides a systematic view of the altered biological processes and the inter-patient heterogeneities of HCC with high reproducibility and robustness. With four years having passed, the database now needs integration of recently published datasets. Furthermore, the latest single-cell and spatial transcriptomics have provided a great opportunity to decipher complex gene expression variations at the cellular level with spatial architecture. Here, we present HCCDB v2.0, an updated version that combines bulk, single-cell, and spatial transcriptomic data of HCC clinical samples. It dramatically expands the bulk sample size by adding 1656 new samples from 11 datasets to the existing 3917 samples, thereby enhancing the reliability of transcriptomic meta-analysis. A total of 182,832 cells and 69,352 spatial spots are added to the single-cell and spatial transcriptomics sections, respectively. A novel single-cell level and 2-dimension (sc-2D) metric is proposed as well to summarize cell type-specific and dysregulated gene expression patterns. Results are all graphically visualized in our online portal, allowing users to easily retrieve data through a user-friendly interface and navigate between different views. With extensive clinical phenotypes and transcriptomic data in the database, we show two applications for identifying prognosis-associated cells and tumor microenvironment. HCCDB v2.0 is available at http://lifeome.net/database/hccdb2.

R-MFE-TCN: A correlation prediction model between body surface and tumor during respiratory movement.

BACKGROUND: 2D CT image-guided radiofrequency ablation (RFA) is an exciting minimally invasive treatment that can destroy liver tumors without removing them. However, CT images can only provide limited static information, and the tumor will move with the patient's respiratory movement. Therefore, how to accurately locate tumors under free conditions is an urgent problem to be solved at present. PURPOSE: The purpose of this study is to propose a respiratory correlation prediction model for mixed reality surgical assistance system, Riemannian and Multivariate Feature Enhanced Temporal Convolutional Network (R-MFE-TCN), and to achieve accurate respiratory correlation prediction. METHODS: The model adopts a respiration-oriented Riemannian information enhancement strategy to expand the diversity of the dataset. A new Multivariate Feature Enhancement module (MFE) is proposed to retain respiratory data information, so that the network can fully explore the correlation of internal and external data information, the dual-channel is used to retain multivariate respiratory feature, and the Multi-headed Self-attention obtains respiratory peak-to-valley value periodic information. This information significantly improves the prediction performance of the network. At the same time, the PSO algorithm is used for hyperparameter optimization. In the experiment, a total of seven patients' internal and external respiratory motion trajectories were obtained from the dataset, and the first six patients were selected as the training set. The respiratory signal collection frequency was 21 Hz. RESULTS: A large number of experiments on the dataset prove the good performance of this method, which improves the prediction accuracy while also having strong robustness. This method can reduce the delay deviation under long window prediction and achieve good performance. In the case of 400 ms, the average RMSE and MAE are 0.0453  and 0.0361 mm, respectively, which is better than other research methods. CONCLUSION: The R-MFE-TCN can be extended to respiratory correlation prediction in different clinical situations, meeting the accuracy requirements for respiratory delay prediction in surgical assistance.

Urinary biomarkers for hepatocellular carcinoma: current knowledge for clinicians.

Hepatocellular carcinoma (HCC) is the most predominant primary liver cancer, causing many illnesses and deaths worldwide. The insidious clinical presentation, difficulty in early diagnosis, and the highly malignant nature make the prognosis of HCC extremely poor. The complex and heterogeneous pathogenesis of HCC poses significant challenges to developing therapies. Urine-based biomarkers for HCC, including diagnostic, prognostic, and monitoring markers, may be valuable supplements to current tools such as serum α-fetoprotein (AFP) and seem promising for progress in precision medicine. Herein, we reviewed the major urinary biomarkers for HCC and assessed their potential for clinical application. Molecular types, testing platforms, and methods for building multimolecule models in the included studies have shown great diversity, thus providing abundant novel tools for future clinical transformation and applications.

Relationship between Bladder Cancer, Nutritional Supply, and Treatment Strategies: A Comprehensive Review.

Bladder cancer (BC) is the predominant neoplasm affecting the urinary system and ranks among the most widespread malignancies globally. The causes of bladder cancer include genetic factors; age; sex; and lifestyle factors, such as imbalanced nutrition, obesity, and metabolic disorders. The lack of proper nutrient intake leads to the development of bladder cancer because insufficient nutrients are consumed to prevent this disease. The purpose of this review was to analyze the nutrients closely linked to the onset and advancement of bladder cancer and to explore the relationship between dietary nutrients and bladder cancer. Particular emphasis was placed on nutrients that are frequently ingested in daily life, including sugar, fat, protein, and others. The focus of this research was to analyze how nutritional intake before and after surgery affects the recovery process of patients who have been diagnosed with bladder cancer. This article seeks to increase awareness among both society and the medical community about the significance of implementing appropriate dietary nutrition to reduce the chances of developing bladder cancer, enhance perioperative care for patients with bladder cancer, and aid in their recuperation.

Analysis of the metastatic mechanism and progress in the treatment of breast cancer liver metastasis: a narrative review.

Background and Objective: Breast cancer is the most common malignancy in women, and metastasis to other target organs is one of the main causes of death. Breast cancer liver metastasis (BCLM) has long been a research focus. Enhancing therapeutic effects, optimizing treatment plans and improving the prognosis of patients are major clinical challenges at present. Methods: We performed a comprehensive, nonsystematic review of the latest literature to define the current metastatic mechanism and related treatment advances of BCLM. Key Content and Findings: Due to the lack of research on the mechanism of BCLM, present treatment programs still have limited benefits, and the prognosis of patients is generally poor. New research directions and treatment ideas for BCLM are urgently needed. In this article, we indicated the specific procedures of the BCLM mechanism from the microenvironment to metastasis formation and progress in treatment, including drug therapies such as targeted therapy, surgery, intervention therapy and radiotherapy. Research on the molecular mechanism plays a crucial role in the development of BCLM-related therapies. Based on the metastasis process, we are able to propel new findings and further progression of antineoplastic drugs. Conclusions: The process of BCLM is multistep, and various factors are involved in it, which provides a powerful theoretical basis for the development of therapeutic methods for treatment of this disease. Further understanding of the mechanism of BCLM is essential to guide clinical management.

Extrachromosomal DNA (ecDNA) in cancer: mechanisms, functions, and clinical implications.

Extrachromosomal DNA (ecDNA) is circular DNA that plays an important role in the development and heterogeneity of cancer. The rapid evolution of methods to detect ecDNA, including microscopic and sequencing approaches, has greatly enhanced our knowledge of the role of ecDNA in cancer development and evolution. Here, we review the molecular characteristics, functions, mechanisms of formation, and detection methods of ecDNA, with a focus on the potential clinical implications of ecDNA in cancer. Specifically, we consider the role of ecDNA in acquired drug resistance, as a diagnostic and prognostic biomarker, and as a therapeutic target in the context of cancer. As the pathological and clinical significance of ecDNA continues to be explored, it is anticipated that ecDNA will have broad applications in the diagnosis, prognosis, and treatment of patients with cancer.

Recognition of liver tumors by predicted hyperspectral features based on patient's computed tomography radiomics features.

BACKGROUND: Primary liver tumors can be a serious threat to life and health. Early diagnosis may be life saving. Therefore, enhancing the accuracy of non-invasive early detection of liver tumors is imperative. METHODS: Firstly, image enhancement was applied to augment the dataset, resulting in a total of 464 samples after employing seven data augmentation methods. Subsequently, the XGBoost model was utilized to construct and learn the mapping relationship between Computed Tomography (CT) and corresponding hyperspectral imaging (HSI) data. This model enables the prediction of HSI features corresponding to CT features, thereby enriching CT with more comprehensive hyperspectral information. RESULTS: Four classifiers were employed to discern the presence of tumors in patients. The results demonstrated exceptional performance, with a classification accuracy exceeding 90%. CONCLUSIONS: This study proposes an artificial intelligence-based methodology that utilizes early CT radiomics features to predict HSI features. Subsequently, the results are utilized for non-invasive tumor prediction and early screening, thereby enhancing the accuracy of non-invasive liver tumor detection.

Relationship between iron overload caused by abnormal hepcidin expression and liver disease: A review.

Iron is essential to organisms, the liver plays a vital role in its storage. Under pathological conditions, iron uptake by the intestine or hepatocytes increases, allowing excess iron to accumulate in liver cells. When the expression of hepcidin is abnormal, iron homeostasis in humans cannot be regulated, and resulting in iron overload. Hepcidin also regulates the release of iron from siderophores, thereby regulating the concentration of iron in plasma. Important factors related to hepcidin and systemic iron homeostasis include plasma iron concentration, body iron storage, infection, inflammation, and erythropoietin. This review summarizes the mechanism and regulation of iron overload caused by hepcidin, as well as related liver diseases caused by iron overload and treatment.

Classification of metastatic hepatic carcinoma and hepatocellular carcinoma lesions using contrast-enhanced CT based on EI-CNNet.

BACKGROUND: For hepatocellular carcinoma and metastatic hepatic carcinoma, imaging is one of the main diagnostic methods. In clinical practice, diagnosis mainly relied on experienced imaging physicians, which was inefficient and cannot met the demand for rapid and accurate diagnosis. Therefore, how to efficiently and accurately classify the two types of liver cancer based on imaging is an urgent problem to be solved at present. PURPOSE: The purpose of this study was to use the deep learning classification model to help radiologists classify the single metastatic hepatic carcinoma and hepatocellular carcinoma based on the enhanced features of enhanced CT (Computer Tomography) portal phase images of the liver site. METHODS: In this retrospective study, 52 patients with metastatic hepatic carcinoma and 50 patients with hepatocellular carcinoma were among the patients who underwent preoperative enhanced CT examinations from 2017-2020. A total of 565 CT slices from these patients were used to train and validate the classification network (EI-CNNet, training/validation: 452/113). First, the EI block was used to extract edge information from CT slices to enrich fine-grained information and classify them. Then, ROC (Receiver Operating Characteristic) curve was used to evaluate the performance, accuracy, and recall of the EI-CNNet. Finally, the classification results of EI-CNNet were compared with popular classification models. RESULTS: By utilizing 80% data for model training and 20% data for model validation, the average accuracy of this experiment was 98.2% ± 0.62 (mean ± standard deviation (SD)), the recall rate was 97.23% ± 2.77, the precision rate was 98.02% ± 2.07, the network parameters were 11.83 MB, and the validation time was 9.83 s/sample. The classification accuracy was improved by 20.98% compared to the base CNN network and the validation time was 10.38 s/sample. Compared with other classification networks, the InceptionV3 network showed improved classification results, but the number of parameters was increased and the validation time was 33 s/sample, and the classification accuracy was improved by 6.51% using this method. CONCLUSION: EI-CNNet demonstrated promised diagnostic performance and has potential to reduce the workload of radiologists and may help distinguish whether the tumor is primary or metastatic in time; otherwise, it may be missed or misjudged.

Exosomal circ-PTPN22 and circ-ADAMTS6 mark T cell exhaustion and neutrophil extracellular traps in Asian intrahepatic cholangiocarcinoma.

Intrahepatic cholangiocarcinoma (ICC) is a liver tumor featured by challenges of non-invasive early diagnosis and a higher prevalence rate in Asian countries. These characteristics necessitate the development of liquid biopsy and immunotherapy methods to improve the prognosis of patients with ICC. Herein, we conducted a pilot study on the transcriptome of tumor tissues, adjacent normal tissues, and plasma exosomes of Asian patients with ICC from northern and southern China. We identified a subgroup of immunogenic Asian ICC, which is different from Caucasian ICC and is characterized by T cell exhaustion and neutrophil extracellular traps. The levels of circ-PTPN22 (hsa_circ_0110529) and circ-ADAMTS6 (hsa_circ_0072688), potential circRNA biomarkers, were elevated in the ICC tumor tissues and plasma exosomes of this subgroup than in the other subgroups and normal controls. These circRNAs were derived from post-transcriptional backsplicing of PTPN22 and ADAMTS6 that were expressed in T cells and endothelial cells, respectively, in the ICC microenvironment. Our results revealed a subgroup of Asian ICC characterized by T cell exhaustion and neutrophil extracellular traps and marked by elevated levels of circ-PTPN22 and circ-ADAMTS6 in tumor tissues and plasma exosomes. This subgroup is potentially detectable by plasma exosomal circRNAs and treatable with immune checkpoint blockade.