Prediction of early recurrence of hepatocellular carcinoma after resection using digital pathology images assessed by machine learning

Association between early job loss and prognosis among hepatocellular carcinoma survivors

BACKGROUND

Early job loss after curative treatment for hepatocellular carcinoma (HCC) is associated with significant socioeconomic and health challenges, potentially worsening patient outcomes.

AIMS

To examine the impact of early job loss on all-cause mortality among HCC survivors following curative treatment.

METHODS

We conducted a retrospective cohort study using Korean National Health Insurance Service data on 4578 HCC survivors (aged 35-54) with economic activity treated between 2009 and 2015. Primary and secondary outcomes were all-cause mortality and HCC recurrence, respectively. Early job loss was defined as a shift from insurer to dependent status. Adjusted hazard ratio (HR) and 95% confidence interval (CI) were estimated using multivariable Cox regression models, and subgroup analyses were performed. Causal mediation analysis assessed early HCC recurrence as a mediator between early job loss and all-cause mortality.

RESULTS

Among 4578 patients (median follow-up, 8.3 years), 1189 (26%) died including 989 (25%) in the job-maintained group and 200 (35%) in the early job loss group (P < .001). Early job loss was significantly associated with increased risk of all-cause mortality (adjusted HR 1.52 [95% CI 1.30-1.78]), but not with HCC recurrence (adjusted HR 1.07 [95% CI 0.91-1.25]). Subgroup analyses showed prominent association among middle-income level, non-liver cirrhosis, non-alcoholism, or surgical resection group. Early HCC recurrence plays a significant mediating role on the relationship between early job loss and all-cause mortality (mediated proportion 19%, 95% CI 5-31%).

CONCLUSIONS

Early job loss may increase risk of all-cause mortality among HCC survivors undergoing curative treatment.

Artificial intelligence in gastrointestinal cancer research: Image learning advances and applications

With the rapid advancement of artificial intelligence (AI) technologies, including deep learning, large language models, and neural networks, these methodologies are increasingly being developed and integrated into cancer research. Gastrointestinal tumors are characterized by complexity and heterogeneity, posing significant challenges for early detection, diagnostic accuracy, and the development of personalized treatment strategies. The application of AI in digestive oncology has demonstrated its transformative potential. AI not only alleviates the diagnostic burden on clinicians, but it improves tumor screening sensitivity, specificity, and accuracy. Additionally, AI aids the detection of biomarkers such as microsatellite instability and mismatch repair, supports intraoperative assessments of tumor invasion depth, predicts treatment responses, and facilitates the design of personalized treatment plans to potentially significantly enhance patient outcomes. Moreover, the integration of AI with multiomics analyses and imaging technologies has led to substantial advancements in foundational research on the tumor microenvironment. This review highlights the progress of AI in gastrointestinal oncology over the past 5 years with focus on early tumor screening, diagnosis, molecular marker identification, treatment planning, and prognosis predictions. We also explored the potential of AI to enhance medical imaging analyses to aid tumor detection and characterization as well as its role in automating and refining histopathological assessments.

Radiomics and clinicoradiological factors as a promising approach for predicting microvascular invasion in hepatitis B-related hepatocellular carcinoma

Microvascular invasion (MVI) is a critical factor in hepatocellular carcinoma (HCC) prognosis, particularly in hepatitis B virus (HBV)-related cases. This editorial examines a recent study by Xu et al who developed models to predict MVI and high-risk (M2) status in HBV-related HCC using contrast-enhanced computed tomography (CECT) radiomics and clinicoradiological factors. The study analyzed 270 patients, creating models that achieved an area under the curve values of 0.841 and 0.768 for MVI prediction, and 0.865 and 0.798 for M2 status prediction in training and validation datasets, respectively. These results are comparable to previous radiomics-based approaches, which reinforces the potential of this method in MVI prediction. The strengths of the study include its focus on HBV-related HCC and the use of widely accessible CECT imaging. However, limitations, such as retrospective design and manual segmentation, highlight areas for improvement. The editorial discusses the implications of the study including the need for standardized radiomics approaches and the potential impact on personalized treatment strategies. It also suggests future research directions, such as exploring mechanistic links between radiomics features and MVI, as well as integrating additional biomarkers or imaging modalities. Overall, this study contributes significantly to HCC management, paving the way for more accurate, personalized treatment approaches in the era of precision oncology.

Early screening, diagnosis and recurrence monitoring of hepatocellular carcinoma in patients with chronic hepatitis B based on serum N-glycomics analysis: A cohort study

BACKGROUND AND AIMS

HCC poses a significant global health burden, with HBV being the predominant etiology in China. However, current diagnostic markers lack the requisite sensitivity and specificity. This study aims to develop and validate serum N-glycomics-based models for the diagnosis and prognosis of HCC in patients with chronic hepatitis B-related cirrhosis.

APPROACH AND RESULTS

This study enrolled a total of 397 patients with chronic hepatitis B-related cirrhosis and HCC for clinical management. N-glycomics profiling was conducted on all participants, and clinical data were collected. First, machine learning-based models, Hepatocellular Carcinoma Glycomics Random Forest model and Hepatocellular Carcinoma Glycomics Support Vector Machine model, were established for early screening and diagnosis of HCC using N-glycomics. The AUC values in the validation set were 0.967 (95% CI: 0.930-1.000) and 0.908 (0.840-0.976) for Hepatocellular Carcinoma Glycomics Random Forest model and Hepatocellular Carcinoma Glycomics Support Vector Machine model, respectively, outperforming AFP (0.687 [0.575-0.765]) and Protein Induced by Vitamin K Absence or Antagonist-II (PIVKA-II) (0.665 [0.507-0.823]). It also showed superiority in subgroup analysis and external validation. Calibration and decision curve analysis also showed good predictive performance. Additionally, we developed a prognostic model, the prog-G model, based on N-glycans to monitor recurrence in patients with HCC after curative treatment. During the follow-up period, it was observed that this model correlated with the clinical condition of the patients and could identify all recurrent HCC cases (n=12) prior to imaging findings, outperforming AFP (n=7) and PIVKA-II (n=9), while also detecting recurrent lesions earlier than imaging.

CONCLUSIONS

N-glycomics models can effectively predict the occurrence and recurrence of HCC to improving the efficiency of clinical decision-making and promoting the precision treatment of HCC.

Machine Learning-Based Pathomics Model Predicts Angiopoietin-2 Expression and Prognosis in Hepatocellular Carcinoma

Angiopoietin-2 (ANGPT2) shows promise as prognostic marker and therapeutic target in hepatocellular carcinoma (HCC). However, assessing ANGPT2 expression and prognostic potential using histopathology images viewed with naked eye is challenging. Herein, machine learning was employed to develop a pathomics model for analyzing histopathology images to predict ANGPT2 status. HCC cases obtained from The Cancer Genome Atlas (TCGA-HCC; n = 267) were randomly assigned to the training or testing set, and cases from a single center were employed as a validation set (n = 91). In the TCGA-HCC cohort, the group with high ANGPT2 expression had a significantly lower overall survival compared with the group with low ANGPT2. Histopathologic features in the training set were extracted, screened, and incorporated into a gradient-boosting machine model that generated a pathomics score, which successfully predicted ANGPT2 expression in the three data sets and showed remarkable risk stratification for overall survival in both the TCGA-HCC (P < 0.0001) and single-center cohorts (P = 0.001). Multivariate analysis suggested that the pathomics score could serve as a predictor of prognosis (P < 0.001). Bioinformatics analysis illustrated a distinction in tumor growth and development related gene-enriched pathways, vascular endothelial growth factor-related gene expression, and immune cell infiltration between high and low pathomics scores. This study indicates that the use of histopathology image features can enhance the prediction of molecular status and prognosis in HCC. The integration of image features with machine learning may improve prognosis prediction in HCC.

Deep learning and digital pathology powers prediction of HCC development in steatotic liver disease

BACKGROUND AND AIMS

Identifying patients with steatotic liver disease who are at a high risk of developing HCC remains challenging. We present a deep learning (DL) model to predict HCC development using hematoxylin and eosin-stained whole-slide images of biopsy-proven steatotic liver disease.

APPROACH AND RESULTS

We included 639 patients who did not develop HCC for ≥7 years after biopsy (non-HCC class) and 46 patients who developed HCC <7 years after biopsy (HCC class). Paired cases of the HCC and non-HCC classes matched by biopsy date and institution were used for training, and the remaining nonpaired cases were used for validation. The DL model was trained using deep convolutional neural networks with 28,000 image tiles cropped from whole-slide images of the paired cases, with an accuracy of 81.0% and an AUC of 0.80 for predicting HCC development. Validation using the nonpaired cases also demonstrated a good accuracy of 82.3% and an AUC of 0.84. These results were comparable to the predictive ability of logistic regression model using fibrosis stage. Notably, the DL model also detected the cases of HCC development in patients with mild fibrosis. The saliency maps generated by the DL model highlighted various pathological features associated with HCC development, including nuclear atypia, hepatocytes with a high nuclear-cytoplasmic ratio, immune cell infiltration, fibrosis, and a lack of large fat droplets.

CONCLUSIONS

The ability of the DL model to capture subtle pathological features beyond fibrosis suggests its potential for identifying early signs of hepatocarcinogenesis in patients with steatotic liver disease.

Recurrent hepatocellular carcinoma is associated with the enrichment of MYC targets gene sets, elevated high confidence deleterious mutations and alternative splicing of DDB2 and BRCA1 transcripts

PURPOSE

Recurrence is the main cause of hepatocellular carcinoma (HCC) related deaths. Underlying recurrence biology can be better understood by comparative analysis of the complete set of transcripts between recurrent and non-recurrent HCC. In this study, transcriptomic data (GSE56545) from 21 male patients diagnosed with either recurrent or non-recurrent HCC were reanalyzed to identify deregulated pathways, somatic mutations, fusion transcripts, alternative splicing events, and the immune context in recurrent HCC.

MATERIALS AND METHODS

DESeq2 was used for differential expression analysis, Mutect2 for somatic mutation analysis, Arriba and STAR-Fusion for fusion transcript analysis, and rMATs for alternative splicing analysis.

RESULTS

The results revealed that MYC targets gene sets (Hallmark_MYC_targets_V1 and Hallmark_MYC_targets_V2) were significantly enriched in recurrent HCC. Among the MYC targets, CBX3, NOP56, CDK4, NPM1, MCM5, MCM4 and PA2G4 upregulation was significantly associated with poor survival. Somatic mutation analysis demonstrated that the numbers of high confidence deleterious mutations were significantly increased in recurrent HCC. Alternative splicing-mediated production of non-functional DDB2 and oncogenic BRCA1 D11q were discovered in recurrent HCC. Finally, CD8+ T-cells were significantly decreased in recurrent HCC.

CONCLUSIONS

These results indicated that the enrichment of MYC targets gene sets is one of the most critical factors that leads to the development of recurrent HCC. In addition, elevated deleterious mutation numbers and alternative spliced DDB2 and BRCA1 isoforms have been identified as prominent contributors to increasing genomic instability in male patients with recurrent HCC.

Application of Intravoxel Incoherent Motion in the Prediction of Intra-Tumoral Tertiary Lymphoid Structures in Hepatocellular Carcinoma

Objective

To explore the value of intravoxel incoherent motion (IVIM) sequences in predicting intra-tumoral tertiary lymphoid structures (TLSs).

Materials and Methods

This prospective study pre-operatively enrolled hepatocellular carcinoma (HCC) patients who underwent magnetic resonance imaging including IVIM sequences, between January 2019 and April 2021. Intra-tumoral TLSs presence was assessed on pathological slide images. Clinical and radiological characteristics were collected. IVIM quantitative parameters and radiomics features were obtained based on the whole delineated tumor volume. By using feature selection techniques, 22 radiomics features, clinical-radiological features (lymphocyte count and satellite nodules), and IVIM parameters (apparent diffusion coefficient (ADC_90Percentile), perfusion fraction (f_Maximum)) were selected. The logistic regression algorithm was used to construct the prediction model based on the combination of these features. The diagnostic performance was assessed using the area under the receiver operating characteristic (AUC). The recurrence-free survival (RFS) was evaluated with Kaplan-Meier curves.

Results

A total of 168 patients were divided into training (n=128) and testing (n=40) cohorts (mean age: 56.83±14.43 years; 149 [88.69%] males; 130 TLSs+). In testing cohort, the model combining multimodal features demonstrated a good performance (AUC: 0.86) and significantly outperformed models based on single-modality features. The model based on radiomics features (AUC: 0.80) had better performance than other features, including IVIM parameter maps (ADC_90Percentile and f_Maximum, AUC: 0.72) and clinical-radiological characteristics (satellite nodules and lymphocyte counts, AUC: 0.59). TLSs+ patients had higher RFS than TSLs- patients (all p <0.05).

Conclusion

The nomogram based on the proposed model can be used as a pre-operative predictive biomarker of TLSs.

Critical Relevance Statement

The nomogram incorporating IVIM sequences may serve as a pre-operative predictive biomarker of intra-tumoral tertiary lymphoid structure (TLS) status.

Revolutionizing MASLD: How Artificial Intelligence Is Shaping the Future of Liver Care

Metabolic dysfunction-associated steatotic liver disease (MASLD) is emerging as a leading cause of chronic liver disease. In recent years, artificial intelligence (AI) has attracted significant attention in healthcare, particularly in diagnostics, patient management, and drug development, demonstrating immense potential for application and implementation. In the field of MASLD, substantial research has explored the application of AI in various areas, including patient counseling, improved patient stratification, enhanced diagnostic accuracy, drug development, and prognosis prediction. However, the integration of AI in hepatology is not without challenges. Key issues include data management and privacy, algorithmic bias, and the risk of AI-generated inaccuracies, commonly referred to as "hallucinations". This review aims to provide a comprehensive overview of the applications of AI in hepatology, with a focus on MASLD, highlighting both its transformative potential and its inherent limitations.

Digital Pathology as an Innovative Tool for Improving Cancer Diagnosis and Treatment

For more than a century, the "gold" standard for diagnosing malignant neoplasms has been pathohistology. However, the continuous advancement of modern technologies is leading to a radical transformation of this field and the emergence of digital pathology. The main advantages of digital pathology include the convenience of the data storage and transfer, as well as the potential for automating diagnostic processes through the application of artificial intelligence technologies. Integrating digital pathology into clinical practice is expected to accelerate the analysis of histological samples, reduce the costs associated with such procedures, and enable the accumulation of large datasets for future scientific research. At the same time, the development of digital pathology faces certain challenges such as the need for technical upgrades in laboratories, ensuring data cybersecurity, and training qualified personnel.

Predicting hepatocellular carcinoma survival with artificial intelligence

Despite the extensive research on hepatocellular carcinoma (HCC) exploring various treatment strategies, the survival outcomes have remained unsatisfactory. The aim of this research was to evaluate the ability of machine learning (ML) methods in predicting the survival probability of HCC patients. The study retrospectively analyzed cases of patients with stage 1-4 HCC. Demographic, clinical, pathological, and laboratory data served as input variables. The researchers employed various feature selection techniques to identify the key predictors of patient mortality. Additionally, the study utilized a range of machine learning methods to model patient survival rates. The study included 393 individuals with HCC. For early-stage patients (stages 1-2), the models reached recall values ​​of up to 91% for 6-month survival prediction. For advanced-stage patients (stage 4), the models achieved accuracy values ​​of up to 92% for 3-year overall survival prediction. To predict whether patients are ex or not, the accuracy was 87.5% when using all 28 features without feature selection with the best performance coming from the implementation of weighted KNN. Further improvements in accuracy, reaching 87.8%, were achieved by applying feature selection methods and using a medium Gaussian SVM. This study demonstrates that machine learning techniques can reliably predict survival probabilities for HCC patients across all disease stages. The research also shows that AI models can accurately identify a high proportion of surviving individuals when assessing various clinical and pathological factors.

Clinical Applications of Artificial Intelligence (AI) in Human Cancer: Is It Time to Update the Diagnostic and Predictive Models in Managing Hepatocellular Carcinoma (HCC)?

In recent years, novel findings have progressively and promisingly supported the potential role of Artificial intelligence (AI) in transforming the management of various neoplasms, including hepatocellular carcinoma (HCC). HCC represents the most common primary liver cancer. Alarmingly, the HCC incidence is dramatically increasing worldwide due to the simultaneous "pandemic" spreading of metabolic dysfunction-associated steatotic liver disease (MASLD). MASLD currently constitutes the leading cause of chronic hepatic damage (steatosis and steatohepatitis), fibrosis, and liver cirrhosis, configuring a scenario where an HCC onset has been reported even in the early disease stage. On the other hand, HCC represents a serious plague, significantly burdening the outcomes of chronic hepatitis B (HBV) and hepatitis C (HCV) virus-infected patients. Despite the recent progress in the management of this cancer, the overall prognosis for advanced-stage HCC patients continues to be poor, suggesting the absolute need to develop personalized healthcare strategies further. In this "cold war", machine learning techniques and neural networks are emerging as weapons, able to identify the patterns and biomarkers that would have normally escaped human observation. Using advanced algorithms, AI can analyze large volumes of clinical data and medical images (including routinely obtained ultrasound data) with an elevated accuracy, facilitating early diagnosis, improving the performance of predictive models, and supporting the multidisciplinary (oncologist, gastroenterologist, surgeon, radiologist) team in opting for the best "tailored" individual treatment. Additionally, AI can significantly contribute to enhancing the effectiveness of metabolomics-radiomics-based models, promoting the identification of specific HCC-pathogenetic molecules as new targets for realizing novel therapeutic regimens. In the era of precision medicine, integrating AI into routine clinical practice appears as a promising frontier, opening new avenues for liver cancer research and treatment.

Combining a deep learning model with clinical data better predicts hepatocellular carcinoma behavior following surgery

Hepatocellular carcinoma (HCC) is among the most common cancers worldwide, and tumor recurrence following liver resection or transplantation is one of the highest contributors to mortality in HCC patients after surgery. Using artificial intelligence (AI), we developed an interdisciplinary model to predict HCC recurrence and patient survival following surgery. We collected whole-slide H&E images, clinical variables, and follow-up data from 300 patients with HCC who underwent transplant and 169 patients who underwent resection at the Cleveland Clinic. A deep learning model was trained to predict recurrence-free survival (RFS) and disease-specific survival (DSS) from the H&E-stained slides. Repeated cross-validation splits were used to compute robust C-index estimates, and the results were compared to those obtained by fitting a Cox proportional hazard model using only clinical variables. While the deep learning model alone was predictive of recurrence and survival among patients in both cohorts, integrating the clinical and histologic models significantly increased the C-index in each cohort. In every subgroup analyzed, we found that a combined clinical and deep learning model better predicted post-surgical outcome in HCC patients compared to either approach independently.

NecroGlobalGCN: Integrating micronecrosis information in HCC prognosis prediction via graph convolutional neural networks

BACKGROUND AND OBJECTIVE

Hepatocellular carcinoma (HCC) ranks fourth in cancer mortality, underscoring the importance of accurate prognostic predictions to improve postoperative survival rates in patients. Although micronecrosis has been shown to have high prognostic value in HCC, its application in clinical prognosis prediction requires specialized knowledge and complex calculations, which poses challenges for clinicians. It would be of interest to develop a model to help clinicians make full use of micronecrosis to assess patient survival.

METHODS

To address these challenges, we propose a HCC prognosis prediction model that integrates pathological micronecrosis information through Graph Convolutional Neural Networks (GCN). This approach enables GCN to utilize micronecrosis, which has been shown to be highly correlated with prognosis, thereby significantly enhancing prognostic stratification quality. We developed our model using 3622 slides from 752 patients with primary HCC from the FAH-ZJUMS dataset and conducted internal and external validations on the FAH-ZJUMS and TCGA-LIHC datasets, respectively.

RESULTS

Our method outperformed the baseline by 8.18% in internal validation and 9.02% in external validations. Overall, this paper presents a deep learning research paradigm that integrates HCC micronecrosis, enhancing both the accuracy and interpretability of prognostic predictions, with potential applicability to other pathological prognostic markers.

CONCLUSIONS

This study proposes a composite GCN prognostic model that integrates information on HCC micronecrosis, collecting large dataset of HCC histopathological images. This approach could assist clinicians in analyzing HCC patient survival and precisely locating and visualizing necrotic tissues that affect prognosis. Following the research paradigm outlined in this paper, other prognostic biomarker integration models with GCN could be developed, significantly enhancing the predictive performance and interpretability of prognostic model.

Prognostic significance of the tumor budding and tumor-infiltrating lymphocytes in survival of hepatocellular carcinoma patients

Objective

In spite of great advance in the management of hepatocellular carcinoma (HCC), the prognostic factors are still obviously not understood. The role of tumor budding (TB) and tumor-infiltrating lymphocytes (TILs) in HCC as pathological parameters affecting prognosis stands principally unknown.

Methods

Seventy-four surgical resection pathology specimens of HCC patients were used. Assessment of TB and TILs were performed using hematoxylin-eosin-stained slides. Follow-up data were collected over a 5-year period to determine disease-free survival rates, overall survival (OS) rates, and how they related to TB, TILs, and other clinicopathological factors.

Results

There was a significant statistical association between high-grade TB and lymphovascular embolization (LVE), tumor necrosis, and grade of HCC with P = 0.003, 0.036, and 0.017, respectively. The positive TILs group showed a statistically significant correlation with histological grade, LVE, and serum alpha-fetoprotein (AFP) level with P = 0.002, 0.006, and 0.043, respectively. Multivariate analysis using the Cox proportional hazard model revealed that TILs are not an independent pathological factor for disease-free and OS, although TB is an independent pathological factor for both.

Conclusions

In all HCC patients, TB was seen, and there was a significant link between the grade of the HCC and the presence of tumor necrosis, LVE, and high-grade TB. The majority (92%) of HCC patients had TILs, and there was a strong relationship between the histological grade, LVE, and serum AFP level. While TILs show variation of the immunologic reaction to the tumor, TB tends to suggest a hostile biologic nature and a bad prognosis.

The power of deep learning in simplifying feature selection for hepatocellular carcinoma: a review

BACKGROUND

Hepatocellular Carcinoma (HCC) is a highly aggressive, prevalent, and deadly type of liver cancer. With the advent of deep learning techniques, significant advancements have been made in simplifying and optimizing the feature selection process.

OBJECTIVE

Our scoping review presents an overview of the various deep learning models and algorithms utilized to address feature selection for HCC. The paper highlights the strengths and limitations of each approach, along with their potential applications in clinical practice. Additionally, it discusses the benefits of using deep learning to identify relevant features and their impact on the accuracy and efficiency of diagnosis, prognosis, and treatment of HCC.

DESIGN

The review encompasses a comprehensive analysis of the research conducted in the past few years, focusing on the methodologies, datasets, and evaluation metrics adopted by different studies. The paper aims to identify the key trends and advancements in the field, shedding light on the promising areas for future research and development.

RESULTS

The findings of this review indicate that deep learning techniques have shown promising results in simplifying feature selection for HCC. By leveraging large-scale datasets and advanced neural network architectures, these methods have demonstrated improved accuracy and robustness in identifying predictive features.

CONCLUSIONS

We analyze published studies to reveal the state-of-the-art HCC prediction and showcase how deep learning can boost accuracy and decrease false positives. But we also acknowledge the challenges that remain in translating this potential into clinical reality.

Exploring the Role of GITR/GITRL Signaling: From Liver Disease to Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common primary liver cancer and presents a continuously growing incidence and high mortality rates worldwide. Besides advances in diagnosis and promising results of pre-clinical studies, established curative therapeutic options for HCC are not currently available. Recent progress in understanding the tumor microenvironment (TME) interactions has turned the scientific interest to immunotherapy, revolutionizing the treatment of patients with advanced HCC. However, the limited number of HCC patients who benefit from current immunotherapeutic options creates the need to explore novel targets associated with improved patient response rates and potentially establish them as a part of novel combinatorial treatment options. Glucocorticoid-induced TNFR-related protein (GITR) belongs to the TNFR superfamily (TNFRSF) and promotes CD8+ and CD4+ effector T-cell function with simultaneous inhibition of Tregs function, when activated by its ligand, GITRL. GITR is currently considered a potential immunotherapy target in various kinds of neoplasms, especially with the concomitant use of programmed cell-death protein-1 (PD-1) blockade. Regarding liver disease, a high GITR expression in liver progenitor cells has been observed, associated with impaired hepatocyte differentiation, and decreased progenitor cell-mediated liver regeneration. Considering real-world data proving its anti-tumor effect and recently published evidence in pre-clinical models proving its involvement in pre-cancerous liver disease, the idea of its inclusion in HCC therapeutic options theoretically arises. In this review, we aim to summarize the current evidence supporting targeting GITR/GITRL signaling as a potential treatment strategy for advanced HCC.

Nomogram for preoperative estimation of microvascular invasion risk in hepatocellular carcinoma

Microvascular invasion (MVI) is an adverse prognostic indicator of tumor recurrence after surgery for hepatocellular carcinoma (HCC). Therefore, developing a nomogram for estimating the presence of MVI before liver resection is necessary. We retrospectively included 260 patients with pathologically confirmed HCC at the Fifth Medical Center of Chinese PLA General Hospital between January 2021 and April 2024. The patients were randomly divided into a training cohort (n = 182) for nomogram development, and a validation cohort (n = 78) to confirm the performance of the model (7:3 ratio). Significant clinical variables associated with MVI were then incorporated into the predictive nomogram using both univariate and multivariate logistic analyses. The predictive performance of the nomogram was assessed based on its discrimination, calibration, and clinical utility. Serum carnosine dipeptidase 1 ([CNDP1] OR 2.973; 95 % CI 1.167-7.575; p = 0.022), cirrhosis (OR 8.911; 95 % CI 1.922-41.318; p = 0.005), multiple tumors (OR 4.095; 95 % CI 1.374-12.205; p = 0.011), and tumor diameter ≥3 cm (OR 4.408; 95 % CI 1.780-10.919; p = 0.001) were independent predictors of MVI. Performance of the nomogram based on serum CNDP1, cirrhosis, number of tumors and tumor diameter was achieved with a concordance index of 0.833 (95 % CI 0.771-0.894) and 0.821 (95 % CI 0.720-0.922) in the training and validation cohorts, respectively. It fitted well in the calibration curves, and the decision curve analysis further confirmed its clinical usefulness. The nomogram, incorporating significant clinical variables and imaging features, successfully predicted the personalized risk of MVI in HCC preoperatively.

Effects of propofol-based total intravenous anesthesia versus desflurane anesthesia on natural killer cell cytotoxicity after hepatocellular carcinoma resection

Background and Aims

Inhalation anesthesia suppresses the immune system and stimulates the growth of tumor cells, contrary to intravenous anesthesia. However, no consensus exists on which anesthetic technique is better for preventing cancer recurrence. Therefore, this study compared the effects of two different anesthetic techniques on natural killer cell cytotoxicity (NKCC) in hepatocellular carcinoma (HCC) patients undergoing open hepatic resection.

Material and Methods

Patients diagnosed with nonmetastatic HCC were scheduled for hepatic resection and randomly assigned to receive either propofol- or desflurane-based anesthesia. The primary outcome was pre- and postoperative NKCC assay. Cytokine levels were assessed by measuring interleukin (IL)-2, IL-4, IL-6, IL-10, tumor necrosis factor-alpha (TNF-α), and interferon-gamma (IFN-γ) levels, and the secondary outcome was postoperative cancer recurrence evaluated using diagnostic imaging scans for 2 years.

Results

Twenty-eight patients were analyzed, including 15 and 13 in the total intravenous anesthesia (TIVA) and inhalation (INH) groups, respectively. Two patients in the INH group were excluded due to non-HCC postoperative pathologic results. At 24 h, the postoperative change in NKCC between both groups showed no significant differences at a ratio of effector cell: target cell = 1:1, 5:1, and 10:1 (P = 0.345, 0.345, and 0.565, respectively). Also, there were no significant differences in IL-2, IL-4, IL-6, IL-10, TNF-α, and IFN-γ levels (P = 0.588, 0.182, 0.730, 0.076, 0.518, 0.533, respectively). Postoperative tumor recurrence occurred in five and six patients in the TIVA and INH groups, respectively.

Conclusion

NKCC did not differ significantly among HCC patients undergoing open hepatic resection under either propofol or desflurane anesthesia 24 h postoperatively.

A clinical-radiomic-pathomic model for prognosis prediction in patients with hepatocellular carcinoma after radical resection

PURPOSE

Radical surgery, the first-line treatment for patients with hepatocellular cancer (HCC), faces the dilemma of high early recurrence rates and the inability to predict effectively. We aim to develop and validate a multimodal model combining clinical, radiomics, and pathomics features to predict the risk of early recurrence.

MATERIALS AND METHODS

We recruited HCC patients who underwent radical surgery and collected their preoperative clinical information, enhanced computed tomography (CT) images, and whole slide images (WSI) of hematoxylin and eosin (H & E) stained biopsy sections. After feature screening analysis, independent clinical, radiomics, and pathomics features closely associated with early recurrence were identified. Next, we built 16 models using four combination data composed of three type features, four machine learning algorithms, and 5-fold cross-validation to assess the performance and predictive power of the comparative models.

RESULTS

Between January 2016 and December 2020, we recruited 107 HCC patients, of whom 45.8% (49/107) experienced early recurrence. After analysis, we identified two clinical features, two radiomics features, and three pathomics features associated with early recurrence. Multimodal machine learning models showed better predictive performance than bimodal models. Moreover, the SVM algorithm showed the best prediction results among the multimodal models. The average area under the curve (AUC), accuracy (ACC), sensitivity, and specificity were 0.863, 0.784, 0.731, and 0.826, respectively. Finally, we constructed a comprehensive nomogram using clinical features, a radiomics score and a pathomics score to provide a reference for predicting the risk of early recurrence.

CONCLUSIONS

The multimodal models can be used as a primary tool for oncologists to predict the risk of early recurrence after radical HCC surgery, which will help optimize and personalize treatment strategies.

Proposal of an integrated staging system using albumin-bilirubin grade and serum alpha-fetoprotein values for predicting postoperative prognosis of recurrent hepatocellular carcinoma

BACKGROUND

Because repeat hepatectomy for recurrent hepatocellular carcinoma is a potentially invasive procedure, it is necessary to identify patients who truly benefit from repeat hepatectomy. Albumin-bilirubin grading has been reported to predict survival in patients with hepatocellular carcinoma. However, as prognosis also depends on tumor factors, a staging system that adds tumor factors to albumin-bilirubin grading may lead to a more accurate prognostication in patients with recurrent hepatocellular carcinoma.

METHODS

Albumin-bilirubin grading and serum alpha-fetoprotein levels were combined and the albumin-bilirubin-alpha-fetoprotein score was created ([albumin-bilirubin grading = 1; 1 point, 2 or 3; 2 points] + [alpha-fetoprotein<75 ng/mL, 0 points; ≥5, 1 point]). Patients were classified into three groups, and their characteristics and survival were evaluated. The predictive ability of the albumin-bilirubin-alpha-fetoprotein score was compared with that of the Cancer of the Liver Italian Program and the Japan Integrated Stage scores.

RESULTS

Albumin-bilirubin-alpha-fetoprotein score significantly stratified postoperative survival (albumin-bilirubin-alpha-fetoprotein score = 1/2/3: 5-year recurrence-free survival [%]: 22.4/20.7/0.0, p < 0.001) and showed the highest predictive value for survival among the integrated systems (albumin-bilirubin-alpha-fetoprotein score/Japan Integrated Stage/Cancer of the Liver Italian Program: 0.785/0.708/0.750).

CONCLUSIONS

Albumin-bilirubin-alpha-fetoprotein score is useful for predicting the survival of patients with recurrent hepatocellular carcinoma undergoing repeat hepatectomy.

Preoperative Bone Loss Predicts Decreased Survival Associated with Microvascular Invasion after Resection of Hepatocellular Carcinoma

BACKGROUND

Osteopenia is a well-known risk factor for survival in patients with hepatocellular carcinoma; however, it is unclear whether osteopenia can apply to both genders and how osteopenia is associated with cancer progression. The aim of this study was to elucidate whether osteopenia predicts reduced survival in regression models in both genders and whether osteopenia is associated with the pathological factors associated with reduced survival.

METHODS

This study included 188 consecutive patients who underwent hepatectomy. Bone mineral density was assessed using computed tomography (CT) scan images taken within 3 months before surgery. Non-contrast CT scan images at the level of the 11th thoracic vertebra were used. The cutoff value of osteopenia was calculated using a threshold value of 160 Hounsfield units. Overall survival (OS) curves and recurrence-free survival (RFS) were constructed using the Kaplan-Meier method, as was a log-rank test for survival. The hazard ratio and 95% confidence interval for overall survival were calculated using Cox's proportional hazard model.

RESULTS

In the regression analysis, age predicted bone mineral density. The association in females was greater than that in males. The OS and RFS of osteopenia patients were shorter than those for non-osteopenia patients. According to univariate and multivariate analyses, osteopenia was an independent risk factor for OS and RFS. The sole pathological factor associated with osteopenia was microvascular portal vein invasion.

CONCLUSION

Models suggest that osteopenia may predict decreased OS and RFS in patients undergoing resection of hepatocellular carcinoma due to the mechanisms mediated via microvascular portal vein invasion.

Role of Fibrinogen-like Protein 1 in Tumor Recurrence Following Hepatectomy

Partial hepatectomy is a first-line treatment for hepatocellular carcinoma. Within 2 weeks following partial hepatectomy, specific molecular pathways are activated to promote liver regeneration. Nevertheless, residual microtumors may also exploit these pathways to reappear and metastasize. Therapeutically targeting molecules that are differentially regulated between normal cells and malignancies, such as fibrinogen-like protein 1 (FGL1), appears to be an effective approach. The potential functions of FGL1 in both regenerative and malignant cells are discussed within the ambit of this review. While FGL1 is normally elevated in regenerative hepatocytes, it is normally downregulated in malignant cells. Hepatectomy does indeed upregulate FGL1 by increasing the release of transcription factors that promote FGL1, including HNF-1α and STAT3, and inflammatory effectors, such as TGF-β and IL6. This, in turn, stimulates certain proliferative pathways, including EGFR/Src/ERK. Hepatectomy alters the phase transition of highly differentiated hepatocytes from G0 to G1, thereby transforming susceptible cells into cancerous ones. Activation of the PI3K/Akt/mTOR pathway by FGL1 allele loss on chromosome 8, a tumor suppressor area, may also cause hepatocellular carcinoma. Interestingly, FGL1 is specifically expressed in the liver via HNF-1α histone acetylase activity, which triggers lipid metabolic reprogramming in malignancies. FGL1 might also be involved in other carcinogenesis processes such as hypoxia, epithelial-mesenchymal transition, immunosuppression, and sorafenib-mediated drug resistance. This study highlights a research gap in these disciplines and the necessity for additional research on FGL1 function in the described processes.

RNF125‑mediated ubiquitination of MCM6 regulates the proliferation of human liver hepatocellular carcinoma cells

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-associated mortality worldwide. Minichromosome maintenance proteins (MCMs), particularly MCM2-7, are upregulated in various cancers, including HCC. The aim of the present study was to investigate the role of MCM2-7 in human liver HCC (LIHC) and the regulation of the protein homeostasis of MCM6 by a specific E3 ligase. Bioinformatics analyses demonstrated that MCM2-7 were highly expressed in LIHC compared with corresponding normal tissues at the mRNA and protein levels, and patients with LIHC and high mRNA expression levels of MCM2, MCM3, MCM6 and MCM7 had poor overall survival rates. Cell Counting Kit-8 and colony formation assays revealed that the knockdown of MCM2, MCM3, MCM6 or MCM7 in Huh7 and Hep3B HCC cells inhibited cell proliferation and colony formation. In addition, pull-down, co-immunoprecipitation and ubiquitination assays demonstrated that RNF125 interacts with MCM6 and mediates its ubiquitination. Furthermore, co-transfection experiments indicated that RNF125 promoted the proliferation of HCC cells mainly through MCM6. In summary, the present study suggests that the RNF125-MCM6 axis plays an important role in the regulation of HCC cell proliferation and is a promising therapeutic target for the treatment of LIHC.

Radiomics analysis of R2* maps to predict early recurrence of single hepatocellular carcinoma after hepatectomy

OBJECTIVES

This study aimed to evaluate the effectiveness of radiomics analysis with R2* maps in predicting early recurrence (ER) in single hepatocellular carcinoma (HCC) following partial hepatectomy.

METHODS

We conducted a retrospective analysis involving 202 patients with surgically confirmed single HCC having undergone preoperative magnetic resonance imaging between 2018 and 2021 at two different institutions. 126 patients from Institution 1 were assigned to the training set, and 76 patients from Institution 2 were assigned to the validation set. A least absolute shrinkage and selection operator (LASSO) regularization was conducted to operate a logistic regression, then features were identified to construct a radiomic score (Rad-score). Uni- and multi-variable tests were used to assess the correlations of clinicopathological features and Rad-score with ER. We then established a combined model encompassing the optimal Rad-score and clinical-pathological risk factors. Additionally, we formulated and validated a predictive nomogram for predicting ER in HCC. The nomogram's discrimination, calibration, and clinical utility were thoroughly evaluated.

RESULTS

Multivariable logistic regression revealed the Rad-score, microvascular invasion (MVI), and α fetoprotein (AFP) level > 400 ng/mL as significant independent predictors of ER in HCC. We constructed a nomogram based on these significant factors. The areas under the receiver operator characteristic curve of the nomogram and precision-recall curve were 0.901 and 0.753, respectively, with an F1 score of 0.831 in the training set. These values in the validation set were 0.827, 0.659, and 0.808.

CONCLUSION

The nomogram that integrates the radiomic score, MVI, and AFP demonstrates high predictive efficacy for estimating the risk of ER in HCC. It facilitates personalized risk classification and therapeutic decision-making for HCC patients.

From prediction to prevention: Machine learning revolutionizes hepatocellular carcinoma recurrence monitoring

In this editorial, we comment on the article by Zhang et al entitled Development of a machine learning-based model for predicting the risk of early postoperative recurrence of hepatocellular carcinoma. Hepatocellular carcinoma (HCC), which is characterized by high incidence and mortality rates, remains a major global health challenge primarily due to the critical issue of postoperative recurrence. Early recurrence, defined as recurrence that occurs within 2 years posttreatment, is linked to the hidden spread of the primary tumor and significantly impacts patient survival. Traditional predictive factors, including both patient- and treatment-related factors, have limited predictive ability with respect to HCC recurrence. The integration of machine learning algorithms is fueled by the exponential growth of computational power and has revolutionized HCC research. The study by Zhang et al demonstrated the use of a groundbreaking preoperative prediction model for early postoperative HCC recurrence. Chall-enges persist, including sample size constraints, issues with handling data, and the need for further validation and interpretability. This study emphasizes the need for collaborative efforts, multicenter studies and comparative analyses to validate and refine the model. Overcoming these challenges and exploring innovative approaches, such as multi-omics integration, will enhance personalized oncology care. This study marks a significant stride toward precise, effi-cient, and personalized oncology practices, thus offering hope for improved patient outcomes in the field of HCC treatment.

Systemic Drugs for Hepatocellular Carcinoma: What Do Recent Clinical Trials Reveal About Sequencing and the Emerging Complexities of Clinical Decisions?

Liver cancer was the fourth leading cause of cancer death in 2015 with increasing incidence between 1990 and 2015. Orthotopic liver transplantation, surgical resection and ablation comprise the only curative therapy options. However, due to the late manifestation of clinical symptoms, many patients present with intermediate or advanced disease, resulting in no curative treatment option being available. Whereas intermediate-stage hepatocellular carcinoma (HCC) is usually still addressable by transarterial chemoembolization (TACE), advanced-stage HCC is amenable only to pharmacological treatments. Conventional cytotoxic agents failed demonstrating relevant effect on survival also because their use was severely limited by the mostly underlying insufficient liver function. For a decade, tyrosine kinase inhibitor (TKI) sorafenib was the only systemic therapy that proved to have a clinically relevant effect in the treatment of advanced HCC. In recent years, the number of substances for systemic treatment of advanced HCC has increased enormously. In addition to tyrosine kinase inhibitors, immune checkpoint inhibitors (ICI) and antiangiogenic drugs are increasingly being applied. The combination of anti-programmed death ligand 1 (PD-L1) antibody atezolizumab and anti-vascular endothelial growth factor (VEGF) antibody bevacizumab has become the new standard of care for advanced HCC due to its remarkable response rates. This requires more and more complex clinical decisions regarding tumor therapy. This review aims at summarizing recent developments in systemic therapy, considering data on first- and second-line treatment, use in the neoadjuvant and adjuvant setting and combination with locoregional procedures.

Drug‐Eluting Shear‐Thinning Hydrogel for the Delivery of Chemo‐ and Immunotherapeutic Agents for the Treatment of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a malignant and deadly form of liver cancer with limited treatment options. Transcatheter arterial chemoembolization, a procedure that delivers embolic and chemotherapeutic agents through blood vessels, is a promising cancer treatment strategy. However, it still faces limitations, such as inefficient agent delivery and the inability to address tumor‐induced immunosuppression. Here, a drug‐eluting shear‐thinning hydrogel (DESTH) loaded with chemotherapeutic and immunotherapeutic agents in nanocomposite hydrogels composed of gelatin and nanoclays is presented as a therapeutic strategy for a catheter‐based endovascular anticancer approach. DESTH is manually deliverable using a conventional needle and catheter. In addition, drug release studies show a sustained and pH‐dependent co‐delivery of the chemotherapy doxorubicin (acidic pH) and the immune‐checkpoint inhibitor aPD‐1 (neutral pH). In a mouse liver tumor model, the DESTH‐based chemo/immunotherapy combination has the highest survival rate and smallest residual tumor size. Finally, immunofluorescence analysis confirms that DESTH application enhances cell death and increases intratumoral infiltration of cytotoxic T‐cells. In conclusion, the results show that DESTH, which enables efficient ischemic tumor cell death and effective co‐delivery of chemo‐ and immunotherapeutic agents, may have the potential to be an effective therapeutic modality in the treatment of HCC.

The Molecular Twin artificial-intelligence platform integrates multi-omic data to predict outcomes for pancreatic adenocarcinoma patients

Contemporary analyses focused on a limited number of clinical and molecular biomarkers have been unable to accurately predict clinical outcomes in pancreatic ductal adenocarcinoma. Here we describe a precision medicine platform known as the Molecular Twin consisting of advanced machine-learning models and use it to analyze a dataset of 6,363 clinical and multi-omic molecular features from patients with resected pancreatic ductal adenocarcinoma to accurately predict disease survival (DS). We show that a full multi-omic model predicts DS with the highest accuracy and that plasma protein is the top single-omic predictor of DS. A parsimonious model learning only 589 multi-omic features demonstrated similar predictive performance as the full multi-omic model. Our platform enables discovery of parsimonious biomarker panels and performance assessment of outcome prediction models learning from resource-intensive panels. This approach has considerable potential to impact clinical care and democratize precision cancer medicine worldwide.

Factors affecting overall survival and disease-free survival after surgery for hepatocellular carcinoma: a nomogram-based prognostic model-a Western European multicenter study

Few studies have assessed the clinical implications of the combination of different prognostic indicators for overall survival (OS) and disease-free survival (DFS) of resected hepatocellular carcinoma (HCC). This study aimed to evaluate the prognostic factors in HCC patients for OS and DFS outcomes and establish a nomogram-based prognostic model to predict the DFS of HCC. A multicenter, retrospective European study was conducted through the collection of data on 413 consecutive treated patients with a first diagnosis of HCC between January 2010 and December 2020. Univariate and multivariate Cox regression analyses were performed to identify all independent risk factors for OS and DFS outcomes. A nomogram prognostic staging model was subsequently established for DFS and its precision was verified internally by the concordance index (C-Index) and externally by calibration curves. For OS, multivariate Cox regression analysis indicated Child-Pugh B7 score (HR 4.29; 95% CI 1.74-10.55; p = 0.002) as an independent prognostic factor, along with Barcelona Clinic Liver Cancer (BCLC) stage ≥ B (HR 1.95; 95% CI 1.07-3.54; p = 0.029), microvascular invasion (MVI) (HR 2.54; 95% CI 1.38-4.67; p = 0.003), R1/R2 resection margin (HR 1.57; 95% CI 0.85-2.90; p = 0.015), and Clavien-Dindo Grade 3 or more (HR 2.73; 95% CI 1.44-5.18; p = 0.002). For DFS, multivariate Cox regression analysis indicated BCLC stage ≥ B (HR 2.15; 95% CI 1.34-3.44; p = 0.002) as an independent prognostic factor, along with multiple nodules (HR 2.04; 95% CI 1.25-3.32; p = 0.004), MVI (HR 1.81; 95% CI 1.19-2.75; p = 0.005), satellite nodules (HR 1.63; 95% CI 1.09-2.45; p = 0.018), and R1/R2 resection margin (HR 3.39; 95% CI 2.19-5.25; < 0.001). The C-Index of the nomogram, tailored based on the previous significant factors, showed good accuracy (0.70). Internal and external calibration curves for the probability of DFS rate showed optimal consistency and fit well between the nomogram-based prediction and actual observations. MVI and R1/R2 resection margins should be considered as significant OS and DFS predictors, while satellite nodules should be included as a significant DFS predictor. The nomogram-based prognostic model for DFS provides a more effective prognosis assessment for resected HCC patients, allowing for individualized treatment plans.

Current Status and Analysis of Machine Learning in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common tumor. Although the diagnosis and treatment of HCC have made great progress, the overall prognosis remains poor. As the core component of artificial intelligence, machine learning (ML) has developed rapidly in the past decade. In particular, ML has become widely used in the medical field, and it has helped in the diagnosis and treatment of cancer. Different algorithms of ML have different roles in diagnosis, treatment, and prognosis. This article reviews recent research, explains the application of different ML models in HCC, and provides suggestions for follow-up research.

Cathelicidin Antimicrobial Peptide Acts as a Tumor Suppressor in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is associated with high rates of metastasis and recurrence, and is one of the most common causes of cancer-associated death worldwide. This study examined the protein changes within circulating exosomes in patients with HCC against those in healthy people using isobaric tags for a relative or absolute quantitation (iTRAQ)-based quantitative proteomics analysis. The protein levels of von Willebrand factor (VWF), cathelicidin antimicrobial peptide (CAMP), and proteasome subunit beta type-2 (PSMB2) were altered in HCC. The increased levels of VWF and PSMB2 but decreased CAMP levels in the serum of patients with HCC were validated by enzyme-linked immunosorbent assays. The level of CAMP (the only cathelicidin found in humans) also decreased in the circulating exosomes and buffy coat of the HCC patients. The serum with reduced levels of CAMP protein in the HCC patients increased the cell proliferation of Huh-7 cells; this effect was reduced following the addition of CAMP protein. The depletion of CAMP proteins in the serum of healthy people enhances the cell proliferation of Huh-7 cells. In addition, supplementation with synthetic CAMP reduces cell proliferation in a dose-dependent manner and significantly delays G1-S transition in Huh-7 cells. This implies that CAMP may act as a tumor suppressor in HCC.

The mechanism of ferroptosis and its related diseases

Ferroptosis, a regulated form of cellular death characterized by the iron-mediated accumulation of lipid peroxides, provides a novel avenue for delving into the intersection of cellular metabolism, oxidative stress, and disease pathology. We have witnessed a mounting fascination with ferroptosis, attributed to its pivotal roles across diverse physiological and pathological conditions including developmental processes, metabolic dynamics, oncogenic pathways, neurodegenerative cascades, and traumatic tissue injuries. By unraveling the intricate underpinnings of the molecular machinery, pivotal contributors, intricate signaling conduits, and regulatory networks governing ferroptosis, researchers aim to bridge the gap between the intricacies of this unique mode of cellular death and its multifaceted implications for health and disease. In light of the rapidly advancing landscape of ferroptosis research, we present a comprehensive review aiming at the extensive implications of ferroptosis in the origins and progress of human diseases. This review concludes with a careful analysis of potential treatment approaches carefully designed to either inhibit or promote ferroptosis. Additionally, we have succinctly summarized the potential therapeutic targets and compounds that hold promise in targeting ferroptosis within various diseases. This pivotal facet underscores the burgeoning possibilities for manipulating ferroptosis as a therapeutic strategy. In summary, this review enriched the insights of both investigators and practitioners, while fostering an elevated comprehension of ferroptosis and its latent translational utilities. By revealing the basic processes and investigating treatment possibilities, this review provides a crucial resource for scientists and medical practitioners, aiding in a deep understanding of ferroptosis and its effects in various disease situations.

Pre-operative gamma-glutamyl transferase levels predict outcomes in hepatitis B-related hepatocellular carcinoma after curative resection

BACKGROUND

Surgical resection is a curative therapy for early-stage hepatocellular carcinoma (HCC); however, HCC recurrence is not uncommon. Identifying outcome predictors helps to manage the disease. Gamma-glutamyl transferase (GGT) may predict the development of HCC, but its role to predict the outcomes after surgical resection of HCC was unclear. This study aimed to investigate pre-operative GGT levels for outcome prediction in patients with hepatitis B virus (HBV)-related HCC.

METHODS

We conducted a retrospective cohort study to include patients with HBV-related HCC receiving surgical resection. Clinical information, HCC characteristics and usage of antiviral therapy were collected. A time-dependent Cox proportional hazard regression analysis were used to predict HCC recurrence and survival.

RESULTS

A total of 699 consecutive patients with HBV-related HCC who received surgical resection with curative intent between 2004 and 2013 were included. After a median of 4.4 years, 266 (38%) patients had HCC recurrence. Pre-operative GGT positively correlated with cirrhosis, tumor burden and significantly increased in patients to develop HCC recurrence. Multivariable analysis demonstrated that pre-operative GGT ≥38 U/L increased 57% risk (hazard ratio [HR]: 1.57, 95% confidence interval [CI]: 1.20-2.06) of recurrent HCC after adjustment for confounding factors. Specifically, pre-operative GGT ≥38 U/L predicted early (<2 years) HCC recurrence (HR: 1.94, 95% CI: 1.30-2.89). Moreover, pre-operative GGT ≥38 U/L predicted all-cause mortality (HR: 1.73, 95% CI: 1.06-2.84) after surgery.

CONCLUSION

Pre-operative GGT levels ≥38 U/L independently predict high risks of HCC recurrence and all-cause mortality in HBV-related HCC patients receiving surgical resection.

Artificial Intelligence-Driven Morphology-Based Enrichment of Malignant Cells from Body Fluid

Cell morphology is a fundamental feature used to evaluate patient specimens in pathologic analysis. However, traditional cytopathology analysis of patient effusion samples is limited by low tumor cell abundance coupled with the high background of nonmalignant cells, restricting the ability of downstream molecular and functional analyses to identify actionable therapeutic targets. We applied the Deepcell platform that combines microfluidic sorting, brightfield imaging, and real-time deep learning interpretations based on multidimensional morphology to enrich carcinoma cells from malignant effusions without cell staining or labels. Carcinoma cell enrichment was validated with whole genome sequencing and targeted mutation analysis, which showed a higher sensitivity for detection of tumor fractions and critical somatic variant mutations that were initially at low levels or undetectable in presort patient samples. Our study demonstrates the feasibility and added value of supplementing traditional morphology-based cytology with deep learning, multidimensional morphology analysis, and microfluidic sorting.

FOS-Like Antigen 1 Expression Was Associated With Survival of Hepatocellular Carcinoma Patients

Background

Early diagnosis and proper management of hepatocellular carcinoma (HCC) improve patient prognosis. Several studies attempted to discover new genes to understand the pathogenesis and identify the prognostic and predictive factors in HCC patients, to improve patient's overall survival (OS) and maintain their physical and social activity. The transcription factor FOS-like antigen 1 (FOSL1) acts as one of the important prognostic factors in different tumors, and its overexpression correlates with tumors' progression and worse patient survival. However, its expression and molecular mechanisms underlying its dysregulation in human HCC remain poorly understood. Our study was conducted to evaluate the expression of FOSL1 in HCC tissues and its relationship with various clinicopathological parameters besides OS.

Methods

This study is a retrospective cohort study conducted among 113 patients with a proven diagnosis of HCC, who underwent tumor resection and received treatment at South Egypt Cancer Institute. Immunohistochemistry for FOSL1 expression and survival curves were conducted followed by statistical analysis.

Results

HCC occurred at older age group and affected males more than females. There was a statistically significant correlation between combined cytoplasmic and nuclear expression of FOSL1 and worse prognosis in HCC patients. There was a statistically significant correlation of FOSL1 expression with histological grade, lymphovascular embolization, and tumor budding where high expression indicated potential deterioration of HCC patients. There was statistically significant correlation between tumor size, tumor grade and FOSL1 expression with the cumulative OS.

Conclusions

Combined cytoplasmic and nuclear FOSL1 expression has significant prognostic association with HCC and diagnostic importance, as it can identify cirrhosis and premalignant lesions that can progress to HCC. Furthermore, Kaplan-Meier survival analysis found that overexpressed FOSL1 was correlated with poor OS.

Recurrence prediction in clear cell renal cell carcinoma using machine learning of quantitative nuclear features

The recurrence of non-metastatic renal cell carcinoma (RCC) may occur early or late after surgery. This study aimed to develop a recurrence prediction machine learning model based on quantitative nuclear morphologic features of clear cell RCC (ccRCC). We investigated 131 ccRCC patients who underwent nephrectomy (T1-3N0M0). Forty had recurrence within 5 years and 22 between 5 and 10 years; thirty-seven were recurrence-free during 5-10 years and 32 were for more than 10 years. We extracted nuclear features from regions of interest (ROIs) using a digital pathology technique and used them to train 5- and 10-year Support Vector Machine models for recurrence prediction. The models predicted recurrence at 5/10 years after surgery with accuracies of 86.4%/74.1% for each ROI and 100%/100% for each case, respectively. By combining the two models, the accuracy of the recurrence prediction within 5 years was 100%. However, recurrence between 5 and 10 years was correctly predicted for only 5 of the 12 test cases. The machine learning models showed good accuracy for recurrence prediction within 5 years after surgery and may be useful for the design of follow-up protocols and patient selection for adjuvant therapy.

Early warning and diagnosis of liver cancer based on dynamic network biomarker and deep learning

Background

Early detection of complex diseases like hepatocellular carcinoma remains challenging due to their network-driven pathology. Dynamic network biomarkers (DNB) based on monitoring changes in molecular correlations may enable earlier predictions. However, DNB analysis often overlooks disease heterogeneity.

Methods

We integrated DNB analysis with graph convolutional neural networks (GCN) to identify critical transitions during hepatocellular carcinoma development in a mouse model. A DNB-GCN model was constructed using transcriptomic data and gene expression levels as node features.

Results

DNB analysis identified a critical transition point at 7 weeks of age despite histological examinations being unable to detect cancerous changes at that time point. The DNB-GCN model achieved 100% accuracy in classifying healthy and cancerous mice, and was able to accurately predict the health status of newly introduced mice.

Conclusion

The integration of DNB analysis and GCN demonstrates potential for the early detection of complex diseases by capturing network structures and molecular features that conventional biomarker discovery methods overlook. The approach warrants further development and validation.

A convolutional neural network STIFMap reveals associations between stromal stiffness and EMT in breast cancer

Intratumor heterogeneity associates with poor patient outcome. Stromal stiffening also accompanies cancer. Whether cancers demonstrate stiffness heterogeneity, and if this is linked to tumor cell heterogeneity remains unclear. We developed a method to measure the stiffness heterogeneity in human breast tumors that quantifies the stromal stiffness each cell experiences and permits visual registration with biomarkers of tumor progression. We present Spatially Transformed Inferential Force Map (STIFMap) which exploits computer vision to precisely automate atomic force microscopy (AFM) indentation combined with a trained convolutional neural network to predict stromal elasticity with micron-resolution using collagen morphological features and ground truth AFM data. We registered high-elasticity regions within human breast tumors colocalizing with markers of mechanical activation and an epithelial-to-mesenchymal transition (EMT). The findings highlight the utility of STIFMap to assess mechanical heterogeneity of human tumors across length scales from single cells to whole tissues and implicates stromal stiffness in tumor cell heterogeneity.

Artificial Intelligence-Based Opportunities in Liver Pathology-A Systematic Review

BACKGROUND

Artificial Intelligence (AI)-based Deep Neural Networks (DNNs) can handle a wide range of applications in image analysis, ranging from automated segmentation to diagnostic and prediction. As such, they have revolutionized healthcare, including in the liver pathology field.

OBJECTIVE

The present study aims to provide a systematic review of applications and performances provided by DNN algorithms in liver pathology throughout the Pubmed and Embase databases up to December 2022, for tumoral, metabolic and inflammatory fields.

RESULTS

42 articles were selected and fully reviewed. Each article was evaluated through the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, highlighting their risks of bias.

CONCLUSIONS

DNN-based models are well represented in the field of liver pathology, and their applications are diverse. Most studies, however, presented at least one domain with a high risk of bias according to the QUADAS-2 tool. Hence, DNN models in liver pathology present future opportunities and persistent limitations. To our knowledge, this review is the first one solely focused on DNN-based applications in liver pathology, and to evaluate their bias through the lens of the QUADAS2 tool.

Combining a protein-targeting small molecule and a thiol-targeting small molecule for detecting a serum risk marker of liver tumor recurrence

This work proposes a novel bioassay designed to detect the 2B receptor of serotonin in serum samples, which can serve as a risk marker for cancer recurrence after surgical resection. Traditional methods for detecting this marker are often costly and time-consuming, requiring specialized reagents and equipment. The new bioassay is designed to enable direct and reagent-less detection of the 2B receptor in serum samples, without the need of antibodies or enzymes. The assay uses a small molecule ligand for the 2B receptor combined with a thiol-targeting fluorescent dye on a compact peptide-based molecular frame. This design allows for a rapid and specific readout of the fluorescent signal upon probe-protein interaction. In addition, the covalent biosensing process used in the assay allows for signal enhancement by electrochemical cross-linking of serum proteins. The bioassay was successfully used to detect the 2B receptor in serum samples from hepatocarcinoma patients, indicating its potential as a powerful tool for early cancer detection and monitoring.

A Paradigm Shift in Primary Liver Cancer Therapy Utilizing Genomics, Molecular Biomarkers, and Artificial Intelligence

Primary liver cancer is the sixth most common cancer worldwide and the third leading cause of cancer-related death. Conventional therapies offer limited survival benefit despite improvements in locoregional liver-directed therapies, which highlights the underlying complexity of liver cancers. This review explores the latest research in primary liver cancer therapies, focusing on developments in genomics, molecular biomarkers, and artificial intelligence. Attention is also given to ongoing research and future directions of immunotherapy and locoregional therapies of primary liver cancers.

Postoperative Adjuvant Hepatic Arterial Infusion Chemotherapy With FOLFOX in Hepatocellular Carcinoma With Microvascular Invasion: A Multicenter, Phase III, Randomized Study

PURPOSE

To report the efficacy and safety of postoperative adjuvant hepatic arterial infusion chemotherapy (HAIC) with 5-fluorouracil and oxaliplatin (FOLFOX) in hepatocellular carcinoma (HCC) patients with microvascular invasion (MVI).

PATIENTS AND METHODS

In this randomized, open-label, multicenter trial, histologically confirmed HCC patients with MVI were randomly assigned (1:1) to receive adjuvant FOLFOX-HAIC (treatment group) or routine follow-up (control group). The primary end point was disease-free survival (DFS) by intention-to-treat (ITT) analysis while secondary end points were overall survival, recurrence rate, and safety.

RESULTS

Between June 2016 and August 2021, a total of 315 patients (ITT population) at five centers were randomly assigned to the treatment group (n = 157) or the control group (n = 158). In the ITT population, the median DFS was 20.3 months (95% CI, 10.4 to 30.3) in the treatment group versus 10.0 months (95% CI, 6.8 to 13.2) in the control group (hazard ratio, 0.59; 95% CI, 0.43 to 0.81; P = .001). The overall survival rates at 1 year, 2 years, and 3 years were 93.8% (95% CI, 89.8 to 98.1), 86.4% (95% CI, 80.0 to 93.2), and 80.4% (95% CI, 71.9 to 89.9) for the treatment group and 92.0% (95% CI, 87.6 to 96.7), 86.0% (95% CI, 79.9 to 92.6), and 74.9% (95% CI, 65.5 to 85.7) for the control group (hazard ratio, 0.64; 95% CI, 0.36 to 1.14; P = .130), respectively. The recurrence rates were 40.1% (63/157) in the treatment group and 55.7% (88/158) in the control group. Majority of the adverse events were grade 0-1 (83.8%), with no treatment-related death in both groups.

CONCLUSION

Postoperative adjuvant HAIC with FOLFOX significantly improved the DFS benefits with acceptable toxicities in HCC patients with MVI.

Significance of nucleologenesis, ribogenesis, and nucleolar proteome in the pathogenesis and recurrence of hepatocellular carcinoma

INTRODUCTION

Emerging evidence suggests that enhanced ribosome biogenesis, increased size, and quantitative distribution of nucleoli are associated with dysregulated transcription, which in turn drives a cell into aberrant cellular proliferation and malignancy. Nucleolar alterations have been considered a prognostic histological marker for aggressive tumors. More recently, advancements in the understanding of chromatin network (nucleoplasm viscosity) regulated liquid-liquid phase separation mechanism of nucleolus formation and their multifunctional role shed light on other regulatory processes, apart from ribosomal biogenesis of the nucleolus.

AREAS COVERED

Using hepatocellular carcinoma as a model to study the role of nucleoli in tumor progression, we review the potential of nucleolus coalescence in the onset and development of tumors through non-ribosomal biogenesis pathways, thereby providing new avenues for early diagnosis and cancer therapy.

EXPERT OPINION

Molecular-based classifications have failed to identify the nucleolar-based molecular targets that facilitate cell-cycle progression. However, the algorithm-based tumor risk identification with high-resolution medical images suggests prominent nucleoli, karyotheca, and increased nucleus/cytoplasm ratio as largely associated with tumor recurrence. Nonetheless, the role of the non-ribosomal functions of nucleoli in tumorigenesis remains elusive. This clearly indicates the lacunae in the study of the nucleolar proteins pertaining to cancer. [Figure: see text].

ScRNA-seq revealed targeting regulator of G protein signaling 1 to mediate regulatory T cells in Hepatocellular carcinoma

BACKGROUND

Regulatory T cells (Tregs) are central to determine immune response outcomes, thus targeting Tregs for immunotherapy is a promising strategy against tumor development and metastasis.

OBJECTIVES

The objective of this study was to identify genes for targeting Tregs to improve the outcome of HCC.

METHODS

We integrated expression data from different samples to remove batch effects and further applied embedding function in Scanpy to conduct sub-clustering of CD4+ T cells in HCC for each of two independent scRNA-seq data. The activity of transcription factors (TFs) was inferred by DoRothEA. Gene expression network analysis was performed in WGCNA R package. We finally used R packages (survminer and survival) to conduct survival analysis. Multiplex immunofluorescence analysis was performed to validate the result from bioinformatic analyses.

RESULTS

We found that regulator of G protein signaling 1 (RGS1) expression was significantly elevated in Tregs compared to other CD4+ T cells in two independent public scRNA-seq datasets, and increased RGS1 predicted inferior clinical outcome of HCC patients. Multiplex immunofluorescence analysis supported that the higher expression of RGS1 in HCC Tregs in tumor tissue compared to it in adjacent tissue. Moreover, RGS1 expression in Tregs was positively correlated with the expression of marker genes of Tregs, C-X-C chemokine receptor 4 (CXCR4), and three CXCR4-dependent genes in both scRNA-seq and bulk RNA-seq data. We further identified that these three genes were selectively expressed in Tregs as compared to other CD4+ T cells. The activities of two transcription factors, recombination signal binding protein for immunoglobulin kappa J region (RBPJ) and yin yang 1 (YY1), were significantly different in HCC Tregs with RGS1 high and RGS1 low.

CONCLUSIONS

Our findings suggested that RGS1 may regulate Treg function possibly through CXCR4 signaling and RGS1 could be a potential target to improve responses for immunotherapy in HCC.

"SMART" cytology: The next generation cytology for precision diagnosis

Cytology plays an important role in diagnosing and managing human diseases, especially cancer, as it is often a simple, low cost yet effective, and non-invasive or minimally invasive diagnostic tool. However, traditional morphology-based cytology practice has limitations, especially in the era of precision diagnosis. Recently there have been tremendous efforts devoted to apply computational tools and to perform molecular analysis on cytological samples for a variety of clinical purposes. Now is probably the appropriate juncture to integrate morphology, machine learning, and molecular analysis together and transform cytology from a morphology-driven practice to the next level - "SMART" Cytology. In this article we will provide a rather brief review of the relevant works for computational analysis on cytology samples, focusing on single-cell-based multiplex quantitative analysis of biomarkers, and introduce the conceptual framework of "SMART (Single cell, Multiplex, AI-driven, and Real Time)" Cytology.

Potential Molecular Targeted Therapy for Unresectable Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most prevalent and lethal cancers, representing a serious worldwide health concern. The recurrence incidence of hepatocellular carcinoma (HCC) following surgery or ablation is as high as 70%. Thus, the clinical applicability of standard surgery and other locoregional therapy to improve the outcomes of advanced HCC is restricted and far from ideal. The registered trials did not identify a treatment that prolonged recurrence-free survival, the primary outcome of the majority of research. Several investigator-initiated trials have demonstrated that various treatments extend patients' recurrence-free or overall survival after curative therapies. In the past decade, targeted therapy has made significant strides in the treatment of advanced HCC. These targeted medicines produce antitumour effects via specific signals, such as anti-angiogenesis or advancement of the cell cycle. As a typical systemic treatment option, it significantly improves the prognosis of this fatal disease. In addition, the combination of targeted therapy with an immune checkpoint inhibitor is redefining the paradigm of advanced HCC treatment. In this review, we focused on the role of approved targeted medicines and potential therapeutic targets in unresectable HCC.

Prediction of chemotherapy-related complications in pediatric oncology patients: artificial intelligence and machine learning implementations

Although the overall incidence of pediatric oncological diseases tends to increase over the years, it is among the rare diseases of the pediatric population. The diagnosis, treatment, and healthcare management of this group of diseases are important. Prevention of treatment-related complications is vital for patients, particularly in the pediatric population. Nowadays, the use of artificial intelligence and machine learning technologies in the management of oncological diseases is becoming increasingly important. With the advancement of software technologies, improvements have been made in the early diagnosis of risk groups in oncological diseases, in radiology, pathology, and imaging technologies, in cancer staging and management. In addition, these technologies can be used to predict the outcome in chemotherapy treatment of oncological diseases. In this context, this study identifies artificial intelligence and machine learning methods used in the prediction of complications due to chemotherapeutic agents used in childhood cancer treatment. For this purpose, the concepts of artificial intelligence and machine learning are explained in this review. A general framework for the use of machine learning in healthcare and pediatric oncology has been drawn and examples of studies conducted on this topic in pediatric oncology have been given. IMPACT: Artificial intelligence and machine learning are advanced tools that can be used to predict chemotherapy-related complications. Algorithms can assist clinicians' decision-making processes in the management of complications. Although studies are using these methods, there is a need to increase the number of studies on artificial intelligence applications in pediatric clinics.

Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers

Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in "radiomics", a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities.

Ensemble learning based on efficient features combination can predict the outcome of recurrence-free survival in patients with hepatocellular carcinoma within three years after surgery

Preoperative prediction of recurrence outcome in hepatocellular carcinoma (HCC) facilitates physicians' clinical decision-making. Preoperative imaging and related clinical baseline data of patients are valuable for evaluating prognosis. With the widespread application of machine learning techniques, the present study proposed the ensemble learning method based on efficient feature representations to predict recurrence outcomes within three years after surgery. Radiomics features during arterial phase (AP) and clinical data were selected for training the ensemble models. In order to improve the efficiency of the process, the lesion area was automatically segmented by 3D U-Net. It was found that the mIoU of the segmentation model was 0.8874, and the Light Gradient Boosting Machine (LightGBM) was the most superior, with an average accuracy of 0.7600, a recall of 0.7673, a F1 score of 0.7553, and an AUC of 0.8338 when inputting radiomics features during AP and clinical baseline indicators. Studies have shown that the proposed strategy can relatively accurately predict the recurrence outcome within three years, which is helpful for physicians to evaluate individual patients before surgery.