Imaging Diagnosis of Hepatocellular Carcinoma: A State-of-the-Art Review

A Bi-modal Temporal Segmentation Network for Automated Segmentation of Focal Liver Lesions in Dynamic Contrast-enhanced Ultrasound

OBJECTIVE

To develop and validate an automated deep learning-based model for focal liver lesion (FLL) segmentation in a dynamic contrast-enhanced ultrasound (CEUS) video.

METHODS

In this multi-center and retrospective study, patients with FLL who underwent dynamic CEUS exam were included from September 2021 to December 2021 (model development and internal test sets), and from March 2023 to May 2023 (external test sets). A bi-modal temporal segmentation network (BTS-Net) was developed and its performance was evaluated using Dice score, intersection over union (IoU) and Hausdorff distance, and compared against several segmentation methods. Time-intensity curves (TICs) were obtained automatically from BTS-Net and manually de-lineated by an experienced radiologist, and evaluated by intra-class correlation and Pearson correlation co-efficients. Multiple characteristics were analyzed to evaluate the influencing factors of BTS-Net.

RESULTS

A total of 232 patients (160 men, median age 56 y) with single FLL were enrolled. BTS-Net achieved mean Dice scores of 0.78, 0.74 and 0.80, mean IoUs of 0.67, 0.62 and 0.68, and mean Hausdorff distances of 15.83, 16.01 and 15.04 in the internal test set and two external test sets, respectively. The mean intra-class correlation and Pearson correlation co-efficients of TIC were 0.89, 0.92 and 0.98, and 0.91, 0.93 and 0.99, respectively. BTS-Net demonstrated a significantly higher mean Dice score and IoU in large (0.82, 0.72), homogeneous positive enhanced (0.81, 0.70) or stable (0.81, 0.70) lesions in pooled test sets.

CONCLUSION

Our study proposed BTS-Net for automated FLL segmentation of dynamic CEUS video, achieving favorable performance in the test sets. Downstream TIC generation based on BTS-Net performed well, demonstrating its potential as an effective segmentation tool in clinical practice.

Artificial Intelligence and Machine Learning Predicting Transarterial Chemoembolization Outcomes: A Systematic Review

BACKGROUND

Major society guidelines recommend transarterial chemoembolization (TACE) as the standard of care for intermediate-stage hepatocellular carcinoma (HCC) patients. However, predicting treatment response remains challenging.

AIMS

As artificial intelligence (AI) may predict therapeutic responses, this systematic review aims to assess the performance and effectiveness of radiomics and AI-based models in predicting TACE outcomes in patients with HCC.

METHODS

A systemic search was conducted on Medline and Embase databases from inception to 7th April 2024. Included studies generated a predictive model for TACE response and evaluated its performance by area under the curve (AUC), specificity, or sensitivity analysis. Systematic reviews, meta-analyses, case series and reports, pediatric, and animal studies were excluded. Secondary search of references of included articles ensured comprehensiveness.

RESULTS

64 articles, with 13,412 TACE-treated patients, were included. AI in pre-treatment CT scans provided value in predicting the efficacy of TACE in HCC treatment. A positive association was observed for AI in pre-treatment MRI scans. Models incorporating radiomics had numerically better performance than those incorporating manual measured radiological variables. 39 studies demonstrated that combined predictive models had numerically better performance than models based solely on imaging or non-imaging features.

CONCLUSION

A combined predictive model incorporating clinical features, laboratory investigations, and radiological characteristics may effectively predict response to TACE treatment for HCC.

Automated ultrasonography of hepatocellular carcinoma using discrete wavelet transform based deep-learning neural network

This study introduces HCC-Net, a novel wavelet-based approach for the accurate diagnosis of hepatocellular carcinoma (HCC) from abdominal ultrasound (US) images using artificial neural networks. The HCC-Net integrates the discrete wavelet transform (DWT) to decompose US images into four sub-band images, a lesion detector for hierarchical lesion localization, and a pattern-augmented classifier for generating pattern-enhanced lesion images and subsequent classification. The lesion detection uses a hierarchical coarse-to-fine approach to minimize missed lesions. CoarseNet performs initial lesion localization, while FineNet identifies any lesions that were missed. In the classification phase, the wavelet components of detected lesions are synthesized to create pattern-augmented images that enhance feature distinction, resulting in highly accurate classifications. These augmented images are classified into 'Normal,' 'Benign,' or 'Malignant' categories according to their morphologic features on sonography. The experimental results demonstrate the significant effectiveness of the proposed coarse-to-fine detection framework and pattern-augmented classifier in lesion detection and classification. We achieved an accuracy of 96.2 %, a sensitivity of 97.6 %, and a specificity of 98.1 % on the Samsung Medical Center dataset, indicating HCC-Net's potential as a reliable tool for liver cancer screening.

Prediction of early postoperative recurrence of hepatocellular carcinoma by habitat analysis based on different sequence of contrast-enhanced CT

Aim

To develop a habitat imaging method for preoperative prediction of early postoperative recurrence of hepatocellular carcinoma.

Methods

A retrospective cohort study was conducted to collect data on 344 patients who underwent liver resection for HCC. The internal subregion of the tumor was objectively delineated and the clinical features were also analyzed to construct clinical models. Radiomics feature extraction was performed on tumor subregions of arterial and portal venous phase images. Machine learning classification models were constructed as a fusion model combining the three different models, and the models were assessed.

Results

A comprehensive retrospective analysis was conducted on a cohort of 344 patients who underwent hepatic cancer resection at one of the two centers. it was found that the combined SVM model yielded superior results after comparing various metrics, such as the AUC, accuracy, sensitivity, specificity, and DCA.

Conclusions

Habitat analysis of sequential CT images can delineate distinct subregions within a tumor, offering valuable insights for early prediction of postoperative HCC recurrence.

In Vivo Time-Resolved Fluorescence Detection of Liver Cancer Supported by Machine Learning

OBJECTIVES

One of the widely used optical biopsy methods for monitoring cellular and tissue metabolism is time-resolved fluorescence. The use of this method in optical liver biopsy has a high potential for studying the shift in energy-type production from oxidative phosphorylation to glycolysis and changes in the antioxidant defense of malignant cells. On the other hand, machine learning methods have proven to be an excellent solution to classification problems in medical practice, including biomedical optics. We aim to combine time-resolved fluorescence measurements and machine learning to automate the division of liver parenchyma and tumors (primary malignant, metastases and benign tumors) into classes.

MATERIALS AND METHODS

An optical biopsy was performed using a developed setup with a fine-needle optical probe in clinical conditions under ultrasound control. Fluorescence decays were recorded in a conditionally healthy liver and lesions during percutaneous needle biopsy. The labeled data set was created on the basis of the recorded fluorescence results and the histopathological classification of the biopsies obtained. Several machine learning methods were trained using different separation strategies of the training test set, and their respective accuracy was compared.

RESULTS

Our results show that each of the tumor types had its own characteristic metabolic shifts recorded by the time-resolved fluorescence spectroscopy. The application of machine learning demonstrates a reliable separation of the liver and all tumor types into cancer and noncancer classes with sensitivity, specificity and corresponding accuracy greater than 0.91, 0.79 and 0.90, using the random forest method. We also show that our method is capable of giving a preliminary diagnosis of the type of liver tumor (primary malignant, metastases and benign tumors) with a sensitivity, specificity and accuracy of at least 0.80, 0.95 and 0.90.

CONCLUSIONS

These promising results highlight its potential as a key tool in the future development of diagnostic and therapeutic strategies for liver cancers. Lasers Surg. Med. 00:00-00, 2024. 2024 Wiley Periodicals LLC.

Decoding the Role of O-GlcNAcylation in Hepatocellular Carcinoma

Post-translational modifications (PTMs) influence protein functionality by modulating protein stability, localization, and interactions with other molecules, thereby controlling various cellular processes. Common PTMs include phosphorylation, acetylation, ubiquitination, glycosylation, SUMOylation, methylation, sulfation, and nitrosylation. Among these modifications, O-GlcNAcylation has been shown to play a critical role in cancer development and progression, especially in hepatocellular carcinoma (HCC). This review outlines the role of O-GlcNAcylation in the development and progression of HCC. Moreover, we delve into the underlying mechanisms of O-GlcNAcylation in HCC and highlight compounds that target O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA) to improve treatment outcomes. Understanding the role of O-GlcNAcylation in HCC will offer insights into potential therapeutic strategies targeting OGT and OGA, which could improve treatment for patients with HCC.

Prognostic Impact of Metabolic Syndrome and Steatotic Liver Disease in Hepatocellular Carcinoma Using Machine Learning Techniques

Metabolic dysfunction-associated steatotic liver disease (MASLD) currently represents the predominant cause of chronic liver disease and is closely linked to a significant increase in the risk of hepatocellular carcinoma (HCC), even in the absence of liver cirrhosis. In this retrospective multicenter study, machine learning (ML) methods were employed to investigate the relationship between metabolic profile and prognosis at diagnosis in a total of 219 HCC patients. The eXtreme Gradient Boosting (XGB) method demonstrated superiority in identifying mortality predictors in our patients. Etiology was the most determining prognostic factor followed by Barcelona Clinic Liver Cancer (BCLC) and Eastern Cooperative Oncology Group (ECOG) classifications. Variables related to the development of hepatic steatosis and metabolic syndrome, such as elevated levels of alkaline phosphatase (ALP), uric acid, obesity, alcohol consumption, and high blood pressure (HBP), had a significant impact on mortality prediction. This study underscores the importance of metabolic syndrome as a determining factor in the progression of HCC secondary to MASLD. The use of ML techniques provides an effective tool to improve risk stratification and individualized therapeutic management in these patients.

Biosensing of Alpha-Fetoprotein: A Key Direction toward the Early Detection and Management of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is currently one of the most prevalent cancers worldwide. Associated risk factors include, but are not limited to, cirrhosis and underlying liver diseases, including chronic hepatitis B or C infections, excessive alcohol consumption, nonalcoholic fatty liver disease (NAFLD), and exposure to chemical carcinogens. It is crucial to detect this disease early on before it metastasizes to adjoining parts of the body, worsening the prognosis. Serum biomarkers have proven to be a more accurate diagnostic tool compared to imaging. Among various markers such as nucleic acids, circulating genetic material, proteins, enzymes, and other metabolites, alpha-fetoprotein (AFP) is a protein marker primarily used to diagnose HCC. However, current methods need a large sample and carry a high cost, among other challenges, which can be improved using biosensing technology. Early and accurate detection of AFP can prevent severe progression of the disease and ensure better management of HCC patients. This review sheds light on HCC development in the human body. Afterward, we outline various types of biosensors (optical, electrochemical, and mass-based), as well as the most relevant studies of biosensing modalities for non-invasive monitoring of AFP. The review also explains these sensing platforms, detection substrates, surface modification agents, and fluorescent probes used to develop such biosensors. Finally, the challenges and future trends in routine clinical analysis are discussed to motivate further developments.

Recent Advances in Liver Tissue Engineering as an Alternative and Complementary Approach for Liver Transplantation

Acute and chronic liver diseases cause significant morbidity and mortality worldwide, affecting millions of people. Liver transplantation is the primary intervention method, replacing a non-functional liver with a functional one. However, the field of liver transplantation faces challenges such as donor shortage, postoperative complications, immune rejection, and ethical problems. Consequently, there is an urgent need for alternative therapies that can complement traditional transplantation or serve as an alternative method. In this review, we explore the potential of liver tissue engineering as a supplementary approach to liver transplantation, offering benefits to patients with severe liver dysfunctions.

Delta-radiomics in cancer immunotherapy response prediction: A systematic review

Background

The new immunotherapies have not only changed the oncological therapeutic approach but have also made it necessary to develop new imaging methods for assessing the response to treatment. Delta radiomics consists of the analysis of radiomic features variation between different medical images, usually before and after therapy.

Purpose

This review aims to evaluate the role of delta radiomics in the immunotherapy response assessment.

Methods

A systematic search was performed in PubMed, Scopus, and Web Of Science using "delta radiomics AND immunotherapy" as search terms. The included articles' methodological quality was measured using the Radiomics Quality Score (RQS) tool.

Results

Thirteen articles were finally included in the systematic review. Overall, the RQS of the included studies ranged from 4 to 17, with a mean RQS total of 11,15 ± 4,18 with a corresponding percentage of 30.98 ± 11.61 %. Eleven articles out of 13 performed imaging at multiple time points. All the included articles performed feature reduction. No study carried out prospective validation, decision curve analysis, or cost-effectiveness analysis.

Conclusions

Delta radiomics has been demonstrated useful in evaluating the response in oncologic patients undergoing immunotherapy. The overall quality was found law, due to the lack of prospective design and external validation. Thus, further efforts are needed to bring delta radiomics a step closer to clinical implementation.

Imaging Investigation of Hepatocellular Carcinoma Progress via Monitoring γ-Glutamyltranspeptidase Level with a Near-Infrared Fluorescence/Photoacoustic Bimodal Probe

Hepatocellular carcinoma (HCC) is one of the main principal causes of cancer death, and the late definite diagnosis limits therapeutic approaches in time. The early diagnosis of HCC is essential, and the previous investigations on the biomarkers inferred that the γ-glutamyltranspeptidase (GGT) level could indicate the HCC process. Herein, a near-infrared fluorescence/photoacoustic (NIRF/PA) bimodal probe, CySO3-GGT, was developed for monitoring the GGT level and thus to image the HCC process. After the in-solution tests, the bimodal response was convinced. The various HCC processes were imaged by CySO3-GGT at the cellular level. Then, the CCl4-induced HCC (both induction and treatment) and the subcutaneous and orthotopic xenograft mice models were selected. All throughout the tests, CySO3-GGT achieved NIRF and PA bimodal imaging of the HCC process. In particular, CySO3-GGT could effectively realize 3D imaging of the HCC nodule by visualizing the boundary between the tumor and the normal tissue. The information here might offer significant guidance for the dynamic monitoring of HCC in the near future.

Radiomics Applications in Spleen Imaging: A Systematic Review and Methodological Quality Assessment

The spleen, often referred to as the "forgotten organ", plays numerous important roles in various diseases. Recently, there has been an increased interest in the application of radiomics in different areas of medical imaging. This systematic review aims to assess the current state of the art and evaluate the methodological quality of radiomics applications in spleen imaging. A systematic search was conducted on PubMed, Scopus, and Web of Science. All the studies were analyzed, and several characteristics, such as year of publication, research objectives, and number of patients, were collected. The methodological quality was evaluated using the radiomics quality score (RQS). Fourteen articles were ultimately included in this review. The majority of these articles were published in non-radiological journals (78%), utilized computed tomography (CT) for extracting radiomic features (71%), and involved not only the spleen but also other organs for feature extraction (71%). Overall, the included papers achieved an average RQS total score of 9.71 ± 6.37, corresponding to an RQS percentage of 27.77 ± 16.04. In conclusion, radiomics applications in spleen imaging demonstrate promising results in various clinical scenarios. However, despite all the included papers reporting positive outcomes, there is a lack of consistency in the methodological approaches employed.

The role of routine biopsy of the background liver in the management of hepatocellular carcinoma

We sought to determine the influence of background liver biopsies on hepatocellular carcinoma (HCC) management. The pathology database at a large university hospital was searched between 2013 and 2018 for all instances of when a separate biopsy of the nontumoral liver was performed within 6 months of an HCC biopsy. Patients were evaluated for baseline demographic and clinical characteristics, treatment proposed prior to biopsy, and impact of biopsy results on management. Among the 104 identified cases of paired liver biopsies, 22% were women; the median age was 64 years; and most were of earlier HCC stages at diagnosis (Barcelona Clinic Liver Cancer stages 0-A: 70%). Four patients among 10 in whom cirrhosis status was clinically unclear were confirmed to have cirrhosis on biopsy, and 4 patients did not have cirrhosis despite clinical suspicion. Treatment was altered by the background parenchymal findings for 5 patients (5%): management was less aggressive for 4 patients and more aggressive for 1 patient. A background liver biopsy can significantly impact the management of a small subset of HCC patients, especially those with early disease, and should be considered concurrently with the biopsy of the mass.