The Prognostic Impact of Tumor Differentiation on Recurrence and Survival after Resection of Hepatocellular Carcinoma Is Dependent on Tumor Size

Artificial intelligence-based model for the recurrence of hepatocellular carcinoma after liver transplantation

BACKGROUND

Artificial intelligence-based models might improve patient selection for liver transplantation in hepatocellular carcinoma. The objective of the current study was to develop artificial intelligence-based deep learning models and determine the risk of recurrence after living donor liver transplantation for hepatocellular carcinoma.

METHODS

The study was a single-center retrospective cohort study. Patients who underwent living donor liver transplantation for hepatocellular carcinoma were divided into training and validation cohorts (n = 192). The deep learning models were used to stratify patients in the training cohort into low- and high-risk groups, and 5-year recurrence-free survival was assessed in the validation cohort.

RESULTS

The median follow-up period was 59.1 (33.9-72.4) months. The artificial intelligence model (pretransplant factors) had an area under the curve of 0.86 in the training cohort and 0.71 in the validation cohort. The largest tumor diameter and alpha-fetoprotein level had the greatest Shapley Additive exPlanations values for recurrence (>0.4). The 5-year recurrence-free survival rates in the low- and high-risk groups were 92.6% and 45% (P < .001). In the second artificial intelligence model (pretransplant factors + grade), the area under the curve for the validation cohort was 0.77, with 5-year recurrence-free survival rates of 96% and 30% in the low- and high-risk groups (P < .001). None of the low-risk patients outside the Milan and University of California San Francisco Criteria had recurrence during follow-up.

CONCLUSIONS

The artificial intelligence-based hepatocellular carcinoma transplant recurrence models might improve patient selection for liver transplantation.

Prevalence, management and outcomes of pulmonary metastases in hepatocellular carcinoma: a systematic review and meta-analysis

BACKGROUND

Hepatocellular carcinoma (HCC) presents a significant global health burden, with varying survival rates across regions. The presence of pulmonary metastases (PM) in HCC predicts a poorer prognosis, yet the global understanding of the progression and management is limited.

METHODS

This study aims to systematically review the burden of PM in HCC, document current treatment approaches, and evaluate treatment effectiveness through meta-analysis. A comprehensive literature search was conducted across multiple databases. Articles were screened, and data extraction was performed independently by two reviewers. Statistical analyses were conducted to synthesise data and assess treatment outcomes.

RESULTS

A total of 82 articles were included, comprising a population of 3241 participants with documented PM. Our analysis revealed a linear relationship between the HCC population size and the occurrence of PM (p < 0.005). Surgical intervention demonstrated the lowest hazard ratio (0.128) and significantly improved survival rates compared to other treatment modalities. However, data quality limitations underscore the need for further research to delineate patient subsets benefitting from surgical intervention for PM.

CONCLUSION

Our findings advocate for continued investigation into PM management strategies, notably the role of surgical resection alongside systemic therapies, to improve outcomes in HCC patients with PM.

A pathologic scoring system for predicting postoperative prognosis in patients with ruptured hepatocellular carcinoma

BACKGROUND

The accuracy of pathological factors to predict the prognosis of patients with ruptured hepatocellular carcinoma (rHCC) is unclear. We aimed to develop and validate a novel scoring system based on pathological factors to predict the postoperative survival of patients with rHCC.

METHOD

Patients with rHCC who underwent hepatectomy were recruited from three hospitals and allocated to the training (n = 221) and validation (n = 194) cohorts. A new scoring system, namely the MSE (microvascular invasion-satellite foci-Edmondson Steiner) score, was established based on three pathological factors using univariate and multivariate Cox proportional hazards regression analyses, including microvascular invasion, satellite foci, and differentiation grade. Finally, patients were stratified into three groups based on their risk of prognosis (low, intermediate, or high) according to their MSE score. We also constructed MSE score-based nomograms. The performance of the nomograms was assessed by receiver operating characteristic and calibration curve analyses and validated using the validation cohort.

RESULTS

Three pathological factors were significantly correlated with overall survival (OS) and recurrence-free survival (RFS), three of which were included in the MSE score. The score can clearly stratify rHCC patients after hepatectomy (P < 0.05). And we established nomograms based on the MSE score (MSE score, Barcelona Clinic Liver Cancer stage, and alpha-fetoprotein concentration) to predict postoperative OS and RFS in patients with rHCC. The nomograms showed good discrimination, with C-indices over 0.760 for OS and RFS at 1, 3, and 5 years, respectively. The calibration curve showed excellent nomogram calibration, which was also verified in the validation cohort.

CONCLUSION

The clinical MSE score were accurate in predicting OS and RFS in patients with rHCC with resectable lesions after hepatectomy.

MRI radiomics based on deep learning automated segmentation to predict early recurrence of hepatocellular carcinoma

OBJECTIVES

To investigate the utility of deep learning (DL) automated segmentation-based MRI radiomic features and clinical-radiological characteristics in predicting early recurrence after curative resection of single hepatocellular carcinoma (HCC).

METHODS

This single-center, retrospective study included consecutive patients with surgically proven HCC who underwent contrast-enhanced MRI before curative hepatectomy from December 2009 to December 2021. Using 3D U-net-based DL algorithms, automated segmentation of the liver and HCC was performed on six MRI sequences. Radiomic features were extracted from the tumor, tumor border extensions (5 mm, 10 mm, and 20 mm), and the liver. A hybrid model incorporating the optimal radiomic signature and preoperative clinical-radiological characteristics was constructed via Cox regression analyses for early recurrence. Model discrimination was characterized with C-index and time-dependent area under the receiver operating curve (tdAUC) and compared with the widely-adopted BCLC and CNLC staging systems.

RESULTS

Four hundred and thirty-four patients (median age, 52.0 years; 376 men) were included. Among all radiomic signatures, HCC with 5 mm tumor border extension and liver showed the optimal predictive performance (training set C-index, 0.696). By incorporating this radiomic signature, rim arterial phase hyperenhancement (APHE), and incomplete tumor "capsule," a hybrid model demonstrated a validation set C-index of 0.706 and superior 2-year tdAUC (0.743) than both the BCLC (0.550; p < 0.001) and CNLC (0.635; p = 0.032) systems. This model stratified patients into two prognostically distinct risk strata (both datasets p < 0.001).

CONCLUSION

A preoperative imaging model incorporating the DL automated segmentation-based radiomic signature with rim APHE and incomplete tumor "capsule" accurately predicted early postsurgical recurrence of a single HCC.

CRITICAL RELEVANCE STATEMENT

The DL automated segmentation-based MRI radiomic model with rim APHE and incomplete tumor "capsule" hold the potential to facilitate individualized risk estimation of postsurgical early recurrence in a single HCC.

KEY POINTS

A hybrid model integrating MRI radiomic signature was constructed for early recurrence prediction of HCC. The hybrid model demonstrated superior 2-year AUC than the BCLC and CNLC systems. The model categorized the low-risk HCC group carried longer RFS.

Application of artificial intelligence radiomics in the diagnosis, treatment, and prognosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, with an increasing incidence and poor prognosis. In the past decade, artificial intelligence (AI) technology has undergone rapid development in the field of clinical medicine, bringing the advantages of efficient data processing and accurate model construction. Promisingly, AI-based radiomics has played an increasingly important role in the clinical decision-making of HCC patients, providing new technical guarantees for prediction, diagnosis, and prognostication. In this review, we evaluated the current landscape of AI radiomics in the management of HCC, including its diagnosis, individual treatment, and survival prognosis. Furthermore, we discussed remaining challenges and future perspectives regarding the application of AI radiomics in HCC.

Prognostic Role of Basal Serum Alpha-Fetoprotein in Patients with Hepatocellular Carcinoma Suitable for Curative Treatment

Background and Objectives: Serum alpha-fetoprotein (AFP) is a recognized affordable oncological marker in patients with hepatocellular carcinoma (HCC). However, AFP's prognostic role has been assessed mainly after specific treatments, and no unanimously recognized cut-offs have been identified. The aim of this study is to investigate the prognostic role of different basal AFP cut-offs on survival and HCC course. Materials and Methods: In this single-center, retrospective study, all patients newly diagnosed with HCC between January 2009 and December 2021 were prospectively enrolled. Only patients suitable for curative HCC treatments were included in the analyses. Patients were stratified according to AFP cut-offs of 20, 200, 400, and 1000 ng/mL, which were correlated with survival outcomes and clinical parameters. Results: A total of 266 patients were analyzed, with a median follow-up time of 41.5 months. Median overall survival (OS) of all cohort was 43 months. At the multivariate Cox-regression analysis, AFP value ≥ 1000 ng/mL correlated with impaired OS (1-year OS: 67% vs. 88%, 5-year OS: 1% vs. 43%; p = 0.005); other risk factors were tumor dimension ≥ 5 cm (HR 1.73; p = 0.002), Child-Pugh class B-C (HR 1.72; p = 0.002), BCLC stage A (vs. 0) (HR 2.4; p = 0.011), and malignant portal vein thrombosis (HR 2.57; p = 0.007). AFP ≥ 1000 ng/mL was also associated with a reduced recurrence-free survival (HR 2.0; p = 0.038), while starting from AFP ≥ 20 ng/mL, a correlation with development of HCC metastases over time (HR 3.5; p = 0.002) was seen. AFP values ≥ 20 ng/mL significantly correlated with tumor size and higher histological grading; starting from AFP values ≥ 400 ng/mL, a significant correlation with Child-Pugh class B-C and female gender was also observed. Conclusions: Basal AFP correlates with relevant outcomes in patients with HCC. It could help identify patients at a higher risk of worse prognosis who might benefit from personalized surveillance and treatment programs. Prospective studies are needed to confirm these results.

An open, multicenter, exploratory study of apatinib mesylate maintenance therapy for recurrent/metastatic head and neck squamous cell carcinoma (ChiCTR1800019375)

BACKGROUND

This study evaluated the efficacy of apatinib in maintenance therapy in patients with recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC).

METHODS

Twenty-six patients from three centers were enrolled from November 2018 to September 2021. These patients received 2 weeks apatinib, administered at 250 mg qd. Then apatinib dose may be administered to 500 mg qd continuous in 4 weeks cycle if no patients experienced adverse reaction. Enrolled patients can receive a combination of radiotherapy or chemotherapy. The primary endpoints were progression-free survival (PFS), and secondary endpoints included overall survival (OS), disease control rate (DCR), objective response rate (ORR), quality of life (QOL) score, and adverse drug reactions.

RESULTS

Median PFS of all patients was 3.2 months (95% CI: 2.06-4.33). Median OS of all patients was 7.3 months (95% CI: 2.14-12.46). The DCR was 92.3%. The ORR was 30.8%. In univariate analysis, the results showed that ECOG score 0-1 (HR = 0.31, p = 0.006) and treated with apatinib for more than 60 days (HR = 0.31, p = 0.003) were independent prognostic indicators affecting PFS, and ECOG score 0-1 (HR = 0.40, p = 0.027) and moderately differentiated or highly differentiated (HR = 0.38, p = 0.048) were independent prognostic indicators of OS. The most common adverse events among treated subjects included hypertension (46.1%), fatigue (42.3%), and hand-foot syndrome (23.1%). There were only two cases (7.7%) of Grade III or above adverse reactions.

CONCLUSIONS

Maintenance therapy with apatinib is an effective and well-tolerated regimen in patients with R/M HNSCC.

Surgical resection for hepatocellular carcinoma: a single-centre's one decade of experience

Background and aims

Liver cancer is the third leading cause of global cancer deaths, and hepatocellular carcinoma is its most common type. Liver resection is one of the treatment options for hepatocellular carcinoma (HCC). This study aims to explore our hospital's more than a decade of experience in liver resection for HCC patients.

Methods

This is a retrospective cohort study on HCC patients undergoing resection from 2010 to 2021 in a tertiary-level hospital in Jakarta, Indonesia. Mortality rates were explored as the primary outcome of this study. Statistical analysis was done on possible predictive factors using Pearson's χ2. Survival analysis was done using the Log-Rank test and Cox Regression.

Results

Ninety-one patients were included in this study. The authors found that the postoperative mortality rates were 8.8% (in hospital), 11.5% (30 days), and 24.1% (90 days). Excluding postoperative mortalities, the long-term mortality rates were 44.4% (first year), 58.7% (3 years), and 69.7% (5 years). Cumulatively, the mortality rates were 46.4% (1 year), 68.9% (3 years), 77.8% (5 years), and 67.0% (all time). Significant predictive factors for cumulative 1-year mortality include large tumour diameter [odds ratio (OR) 14.06; 95% CI: 2.59-76.35; comparing <3 cm and >10 cm tumours; P<0.01], positive resection margin (OR 2.86; 1.17-77.0; P=0.02), and tumour differentiation (P=0.01). Multivariate analysis found hazard ratios of 6.35 (2.13-18.93; P<0.01) and 1.81 (1.04-3.14; P=0.04) for tumour diameter and resection margin, respectively.

Conclusion

The mortality rate of HCC patients undergoing resection is still very high. Significant predictive factors for mortality found in this study benefit from earlier diagnosis and treatment; thus, highlighting the importance of HCC surveillance programs.

Development and evaluation of nomograms and risk stratification systems to predict the overall survival and cancer-specific survival of patients with hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, and patients with HCC have a poor prognosis and low survival rates. Establishing a prognostic nomogram is important for predicting the survival of patients with HCC, as it helps to improve the patient's prognosis. This study aimed to develop and evaluate nomograms and risk stratification to predict overall survival (OS) and cancer-specific survival (CSS) in HCC patients. Data from 10,302 patients with initially diagnosed HCC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017. Patients were randomly divided into the training and validation set. Kaplan-Meier survival, LASSO regression, and Cox regression analysis were conducted to select the predictors of OS. Competing risk analysis, LASSO regression, and Cox regression analysis were conducted to select the predictors of CSS. The validation of the nomograms was performed using the concordance index (C-index), the Akaike information criterion (AIC), the Bayesian information criterion (BIC), Net Reclassification Index (NRI), Discrimination Improvement (IDI), the receiver operating characteristic (ROC) curve, calibration curves, and decision curve analyses (DCAs). The results indicated that factors including age, grade, T stage, N stage, M stage, surgery, surgery to lymph node (LN), Alpha-Fetal Protein (AFP), and tumor size were independent predictors of OS, whereas grade, T stage, surgery, AFP, tumor size, and distant lymph node metastasis were independent predictors of CSS. Based on these factors, predictive models were built and virtualized by nomograms. The C-index for predicting 1-, 3-, and 5-year OS were 0.788, 0.792, and 0.790. The C-index for predicting 1-, 3-, and 5-year CSS were 0.803, 0.808, and 0.806. AIC, BIC, NRI, and IDI suggested that nomograms had an excellent predictive performance with no significant overfitting. The calibration curves showed good consistency of OS and CSS between the actual observation and nomograms prediction, and the DCA showed great clinical usefulness of the nomograms. The risk stratification of OS and CSS was built that could perfectly classify HCC patients into three risk groups. Our study developed nomograms and a corresponding risk stratification system predicting the OS and CSS of HCC patients. These tools can assist in patient counseling and guiding treatment decision making.

Impact of inflammation and the immune system on hepatocellular carcinoma recurrence after hepatectomy

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide. Hepatectomy remains the first-line treatment for patients with resectable HCC. However, the reported recurrence rate of HCC at 5 years after surgery is between 50% and 70%. Tumor-related factors, including tumor size, number and differentiation, and underlying liver disease are well-known risk factors for recurrence after treatment. In addition to tumor-related factors, ever-increasing amounts of studies are finding that the tumor microenvironment also plays an important role in the recurrence of HCC, including systemic inflammatory response and immune regulation. Based on this, some inflammatory and immune markers were used in predicting postoperative cancer recurrence. These include neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, cytotoxic T cells, and regulatory T cells, among others. In this review, we summarized the inflammatory and immune markers that affect recurrence after HCC resection in order to provide direction for adjuvant therapy after HCC resection and ultimately achieve the goal of reducing recurrence.

Impact of frailty on long-term outcomes after liver resection for hepatocellular carcinoma in elderly patients: A prospective study

BACKGROUND

sFrailty affects short-term outcomes after liver resection in elderly patients. However, frailty's effects on long-term outcomes after liver resection in elderly patients with hepatocellular carcinoma (HCC) are unknown.

METHODS

This prospective, single-center study included 81 independently living patients aged ≥65 years scheduled to undergo liver resection for initial HCC. Frailty was evaluated according to the Kihon Checklist, a phenotypic frailty index." We investigated and compared postoperative long-term outcomes after liver resection between patients with and without frailty.

RESULTS

Of the 81 patients, 25 (30.9%) were frail. The proportion of patients with cirrhosis, high serum alpha-fetoprotein level (≥200 ng/mL), and poorly differentiated HCC was higher in the frail group than in the nonfrail group (n = 56). Among the patients with postoperative recurrence, the incidence of extrahepatic recurrence was higher in the frail group than in the nonfrail group (30.8% vs. 3.6%, P = 0.028). Moreover, the proportion of patients who underwent repeat liver resection and ablation for recurrence who met the Milan criteria tended to be lower in the frail group than in the nonfrail group. Although there was no difference in disease-free survival between the two groups, the overall survival rate in the frail group was significantly worse than that in the nonfrail group (5-year overall survival: 42.7% vs. 77.2%, P = 0.005). Results of the multivariate analysis indicated that frailty and blood loss were independent prognostic factors for postoperative survival.

CONCLUSION

Frailty is associated with unfavorable long-term outcomes after liver resection in elderly patients with HCC.

Machine Learning-Based CEMRI Radiomics Integrating LI-RADS Features Achieves Optimal Evaluation of Hepatocellular Carcinoma Differentiation

Purpose

To develop and compare various machine learning (ML) classifiers that employ radiomics extracted from contrast-enhanced magnetic resonance imaging (CEMRI) for diagnosing pathological differentiation of hepatocellular carcinoma (HCC), and validate the performance of the best model.

Methods

A total of 251 patients with HCCs (n = 262) were assigned to a training (n = 200) cohort and a validation (n = 62) cohort. A collection of 5502 radiomics signatures were extracted from the CEMRI images for each HCC nodule. To reduce redundancy and dimensionality, Spearman rank correlation, minimum redundancy maximum relevance (mRMR), and the least absolute shrinkage and selection operator (LASSO) approach were employed. Eight ML classifiers were trained to obtain the best radiomics model. The performance of each model was evaluated based on the area under the receiver operating characteristic curve (AUC). The radiomics model was integrated with liver imaging reporting and data system (LI-RADS) features to design a combined model.

Results

The eXtreme Gradient Boosting (XGBoost)-based radiomics model outperformed other ML classifiers in evaluating pHCC, achieving an AUC of 1.00 and accuracy of 1.00 in the training cohort. The LI-RADS model demonstrated an AUC value of 0.77 and 0.82 in the training and validation cohorts. The combined model exhibited best performance in both the training and validation cohorts, with AUCs of 1.00 and 0.86 for evaluating HCC differentiation, respectively.

Conclusion

CEMRI radiomics integrating LI-RADS features demonstrated excellent performance in evaluating HCC differentiation, suggesting an optimal clinical decision tool for individualized diagnosis of HCC differentiation.

Ultrasound Patterns of Hepatocellular Carcinoma and Their Prognostic Impact: A Retrospective Study

BACKGROUND

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death. Abdominal ultrasound (US) is by far the most widely used first-level exam for the diagnosis of HCC. We aimed to assess whether different ultrasound patterns were related to tumor prognosis.

METHODS

We retrospectively reviewed all patients with a new diagnosis of HCC (single nodule) and undergoing radiofrequency thermal ablation (RFTA) at our clinic between January 2009 and December 2021. Patients were classified according to four HCC ultrasound patterns: 1A, single capsulated nodule; 1B, well capsulated intra-node nodule; 1C, cluster consisting of capsulated nodules; and 2, non-capsulated nodule.

RESULTS

149 patients were analysed; median follow-up time was 43 months. US patterns 1A (32.9%) and 1B (61.1%) were the most commonly seen. Median overall survival (OS) and recurrence-free survival (RFS) from RFTA were 54 months (95% CI, 42-66) and 22 months (95% CI, 12-32), respectively. Pattern 1A showed the best OS. Compared to pattern 1A, 1B was independently associated with worse OS (51 months (95% CI, 34-68) vs. 46 months (95% CI, 18-62)) and RFS (34 months (95% CI, 27-41) vs. 18 months (95% CI, 12-24)). Patterns 1C and 2 were associated with worse RFS compared to 1A, while no difference was seen for OS. Among baseline clinical variables, pattern 1B exhibited higher histological grade (p = 0.048) and tumor dimension (p = 0.034) compared to pattern 1A.

CONCLUSIONS

Our findings demonstrate that different US patterns correlate with different survival outcomes and tumor behavior in patients with HCC. Prospective studies are needed to confirm these results.

A hierarchical prognostic model for Co-diabetes pancreatic adenocarcinoma

Background

Co-diabetes pancreatic adenocarcinoma has a poorer prognosis than pancreatic adenocarcinoma without diabetes. This study aimed to develop a reliable prognostic model for patients with co-diabetes pancreatic adenocarcinoma.

Method

Overall, 169 patients with co-diabetes pancreatic adenocarcinoma were included in our study. First, the independent risk factors affecting the prognosis of patients with co-diabetes pancreatic adenocarcinoma were determined by univariate and multivariate Cox regression analyses. Based on these identified risk factors, we developed a nomogram and evaluated its predictive ability using the concordance index, receiver operating characteristic curve, calibration plot, decision curve, and net reclassification index.

Results

In this study, prealbumin, transferrin, carcinoembryonic antigen, distant metastasis, tumor differentiation neutrophil count, lymphocyte count and fasting blood glucose were confirmed as significant prognostic factors. Based on these predictors, a new nomogram was developed. Compared with the American Joint Committee on Cancer 8 staging system and other models, the nomogram achieved a higher concordance index in the training (0.795) and validation (0.729) queues. The area under the nomogram's curve for predicting patient survival at 0.5, 1, and 1.5 years in the training queue was >0.8. Patients were risk-stratified using the nomogram, and Kaplan-Meier survival curves of subgroups were plotted. The Kaplan-Meier curve also showed better separation than the American Joint Committee on Cancer 8 staging system, indicating that our model has a better risk hierarchical ability.

Conclusions

Compared to the American Joint Committee on Cancer 8 staging system and other predictive models, our model showed better predictive ability for patients with co-diabetes pancreatic adenocarcinoma. Our model will help in patients' risk stratification and improves their prognosis.

Parameters Predicting Microvascular Invasion and Poor Differentiation in Hepatocellular Carcinoma Patients with Normal Alpha-fetoprotein Level Before Liver Transplantation

BACKGROUND/AIMS

The aim of this study is to evaluate the parameters that might be associated with pathologically diagnosed microvascular invasion and poor differentiation, using complete blood count and routine clinical biochemistry test results, in hepatocellular carcinoma patients before liver transplantation.

MATERIALS AND METHODS

The data of patients who underwent liver transplantation for hepatocellular carcinoma at our institute, between March 2006 and November 2021, was researched retrospectively.

RESULTS

The incidence of microvascular invasion was 28.6%, poor differentiation rate was 9.3%, hepatocellular carcinoma recurrence rate after liver transplantation was 12.1%, and median time to recurrence was 13 months, in the patients with normal alpha-fetoprotein levels. After univariate and multivariate analysis, maximum tumor diameter >4.5 cm and the number of nodules (n > 5) were found to be independent risk factors for microvascular invasion, and number of nodules >4 and mean platelet volume ≤8.6 fL were found to be independent risk factors for poor differentiation. Serum alpha-fetoprotein levels were still within the normal range at the recurrence time, in 53% of the patients who had recurrence after liver transplantation, but surprisingly were elevated in 47% of the patients at time of hepatocellular carcinoma recurrence.

CONCLUSIONS

In hepatocellular carcinoma patients with normal alpha-fetoprotein levels before liver transplantation, independent risk factors of the presence of microvascular invasion were maximum tumor diameter and number of nodules, and independent risk factors of poor differentiation were mean platelet volume and number of nodules. Furthermore, serum alpha-fetoprotein levels were still normal at time of recurrence in 53% of hepatocellular carcinoma patients whose alpha-fetoprotein levels were normal before liver transplantation but were elevated in 47% of the patients at recurrence time, despite having normal levels before liver transplantation.

What Imaging Modality Is More Effective in Predicting Early Recurrence of Hepatocellular Carcinoma after Hepatectomy Using Radiomics Analysis: CT or MRI or Both?

BACKGROUND

It is of great importance to predict the early recurrence (ER) of hepatocellular carcinoma (HCC) after hepatectomy using preoperative imaging modalities. Nevertheless, no comparative studies have been conducted to determine which modality, CT or MRI with radiomics analysis, is more effective.

METHODS

We retrospectively enrolled 119 HCC patients who underwent preoperative CT and MRI. A total of 3776 CT features and 4720 MRI features were extracted from the whole tumor. The minimum redundancy and maximum relevance algorithm (MRMR) and least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection, then support vector machines (SVMs) were applied for model construction. Multivariable logistic regression analysis was employed to construct combined models that integrate clinical-radiological-pathological (CRP) traits and radscore. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to compare the efficacy of CT, MRI, and CT and MRI models in the test cohort.

RESULTS

The CT model and MRI model showed no significant difference in the prediction of ER in HCC patients (p = 0.911). Radiomics^CT&MRI demonstrated a superior predictive performance than either Radiomics^CT or Radiomics^MRI alone (p = 0.032, 0.039). The combined CT and MRI model can significantly stratify patients at high risk of ER (area under the curve (AUC) of 0.951 in the training set and 0.955 in the test set) than the CT model (AUC of 0.894 and 0.784) and the MRI model (AUC of 0.856 and 0.787). DCA demonstrated that the CT and MRI model provided a greater net benefit than the models without radiomics analysis.

CONCLUSIONS

No significant difference was found in predicting the ER of HCC between CT models and MRI models. However, the multimodal radiomics model derived from CT and MRI can significantly improve the prediction of ER in HCC patients after resection.

Risk stratification of solitary hepatocellular carcinoma ≤ 5 cm without microvascular invasion: prognostic values of MR imaging features based on LI-RADS and clinical parameters

OBJECTIVES

To estimate the potential of preoperative MR imaging features and clinical parameters in the risk stratification of patients with solitary hepatocellular carcinoma (HCC) ≤ 5 cm without microvascular invasion (MVI) after hepatectomy.

METHODS

The study enrolled 166 patients with histopathological confirmed MVI-negative HCC retrospectively. The MR imaging features were evaluated by two radiologists independently. The risk factors associated with recurrence-free survival (RFS) were identified by univariate Cox regression analysis and the least absolute shrinkage and selection operator Cox regression analysis. A predictive nomogram was developed based on these risk factors, and the performance was tested in the validation cohort. The RFS was analyzed by using the Kaplan-Meier survival curves and log-rank test.

RESULTS

Among the 166 patients with solitary MVI-negative HCC, 86 patients presented with postoperative recurrence. Multivariate Cox regression analysis indicated that cirrhosis, tumor size, hepatitis, albumin, arterial phase hyperenhancement (APHE), washout, and mosaic architecture were risk factors associated with poor RFS and then incorporated into the nomogram. The nomogram achieved good performance with C-index values of 0.713 and 0.707 in the development and validation cohorts, respectively. Furthermore, patients were stratified into high- and low-risk subgroups, and significant prognostic differences were found between the different subgroups in both cohorts (p < 0.001 and p = 0.024, respectively).

CONCLUSION

The nomogram incorporated preoperative MR imaging features, and clinical parameters can be a simple and reliable tool for predicting RFS and achieving risk stratification in patients with solitary MVI-negative HCC.

KEY POINTS

• Application of preoperative MR imaging features and clinical parameters can effectively predict RFS in patients with solitary MVI-negative HCC. • Risk factors including cirrhosis, tumor size, hepatitis, albumin, APHE, washout, and mosaic architecture were associated with worse prognosis in patients with solitary MVI-negative HCC. • Based on the nomogram incorporating these risk factors, the MVI-negative HCC patients could be stratified into two subgroups with significant different prognoses.

Dual segmentation models for poorly and well-differentiated hepatocellular carcinoma using two-step transfer deep learning on dynamic contrast-enhanced CT images

The aim of this study was to develop dual segmentation models for poorly and well-differentiated hepatocellular carcinoma (HCC), using two-step transfer learning (TSTL) based on dynamic contrast-enhanced (DCE) computed tomography (CT) images. From 2013 to 2019, DCE-CT images of 128 patients with 80 poorly differentiated and 48 well-differentiated HCCs were selected at our hospital. In the first transfer learning (TL) step, a pre-trained segmentation model with 192 CT images of lung cancer patients was retrained as a poorly differentiated HCC model. In the second TL step, a well-differentiated HCC model was built from a poorly differentiated HCC model. The average three-dimensional Dice's similarity coefficient (3D-DSC) and 95th-percentile of the Hausdorff distance (95% HD) were mainly employed to evaluate the segmentation accuracy, based on a nested fourfold cross-validation test. The DSC denotes the degree of regional similarity between the HCC reference regions and the regions estimated using the proposed models. The 95% HD is defined as the 95th-percentile of the maximum measures of how far two subsets of a metric space are from each other. The average 3D-DSC and 95% HD were 0.849 ± 0.078 and 1.98 ± 0.71 mm, respectively, for poorly differentiated HCC regions, and 0.811 ± 0.089 and 2.01 ± 0.84 mm, respectively, for well-differentiated HCC regions. The average 3D-DSC for both regions was 1.2 times superior to that calculated without the TSTL. The proposed model using TSTL from the lung cancer dataset showed the potential to segment poorly and well-differentiated HCC regions on DCE-CT images.

Peripheral immune factors aiding clinical parameter for better early recurrence prediction of hepatocellular carcinoma after thermal ablation

OBJECTIVES

Current predictors are largely unsatisfied for early recurrence (ER) of hepatocellular carcinoma (HCC) after thermal ablation. We aimed to explore the prognostic value of peripheral immune factors (PIFs) for better ER prediction of HCC after thermal ablation.

METHODS

Patients who received peripheral blood mononuclear cells (PBMCs) tests before thermal ablation were included. Clinical parameters and 18 PIFs were selected to construct Model^Clin, Model^PIFs and the hybrid Model^PIFs-Clin. Model performances were evaluated using area under the curve (AUC), and recurrence-free survival (RFS) were analyzed by Kaplan-Meier analysis and log-rank tests.

RESULTS

244 patients were included and were randomly divided in 3:1 ratio to discovery and validation cohorts. Clinical parameters including tumor size and AFP, and PIFs including neutrophils, platelets, CD3⁺CD16⁺CD56⁺ NKT and CD8⁺CD28^- T lymphocytes were selected. The Model^PIFs-Clin showed increase in predictive performance compared with Model^Clin, with the AUC improved from 0.664 (95%CI:0.588-0.740) to 0.801 (95%CI:0.734-0.867) in discovery cohort (p < 0.0001), and from 0.645 (95%CI:0.510-0.781) to 0.737(95%CI:0.608-0.865) in validation cohort (p = 0.1006). Model^PIFs-Clin enabled ER risk stratification of patients. Patients predicted in Model^PIFs-Clin high-risk subgroup had a poor RFS compared with those predicted as Model^PIFs-Clin low-risk subgroup, with the median RFS was 18.00 month versus 100.78 month in discovery cohort (p < 0.0001); and 24.00 month versus 60.35 month in validation cohort (p = 0.288). Patients in different risk subgroups exhibited distinct peripheral immune contexture.

CONCLUSIONS

Peripheral immune cells aiding clinical parameters boosted the prediction ability for ER of HCC after thermal ablation, which be helpful for pre-ablation ER risk stratification.

Predicting histologic differentiation of solitary hepatocellular carcinoma up to 5 cm on gadoxetate disodium-enhanced MRI

BACKGROUND

To establish a preoperative score based on gadoxetate disodium-enhanced magnetic resonance imaging (EOB-MRI) and clinical indicators for predicting histologic differentiation of solitary HCC up to 5 cm.

METHODS

From July 2015 to January 2022, consecutive patients with surgically proven solitary HCC measuring ≤ 5 cm at preoperative EOB-MRI were retrospectively enrolled. All MR images were independently evaluated by two radiologists who were blinded to all clinical and pathologic information. Univariate and multivariate logistic regression analyses were performed to identify significant clinicoradiological features associated with poorly differentiated (PD) HCC, which were then incorporated into the predictive score. The predictive score was validated in an independent validation set by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.

RESULTS

A total of 182 patients were included, 42 (23%) with PD HCC. According to the multivariate analysis, marked hepatobiliary phase hypointensity (odds ratio [OR], 9.98), LR-M category (OR, 5.60), and serum alpha-fetoprotein (AFP) level > 400 ng/mL (OR, 3.58) were incorporated into the predictive model; the predictive score achieved an AUC of 0.802 and 0.830 on the training and validation sets, respectively. The sensitivity, specificity, and accuracy of the predictive score were 66.7%, 85.7%, and 81.3%, respectively, on the training set and 66.7%, 81.0%, and 77.8%, respectively, on the validation set.

CONCLUSION

The proposed score integrating two EOB-MRI features and AFP level can accurately predict PD HCC in the preoperative setting.

Prognostic impact of tumor size on isolated hepatocellular carcinoma without vascular invasion may have age variance

Background

Previous studies suggested that tumor size was an independent risk factor of prognosis for hepatocellular carcinoma (HCC). However, the general prognostic analysis did not consider the interaction between variables. The purpose of this study was to investigate whether the effect of tumor size on the prognosis of isolated HCC without vascular invasion varies according to covariates.

Methods

Patients were selected from the Surveillance, Epidemiology, and End Results (SEER) database to investigate whether there was an interaction between age and tumor size on the prognosis. Then the trend test and the value of per 1 SD of tumor size were calculated. In addition, the data of Zhejiang Provincial People's Hospital meeting the requirements were selected to verify the obtained conclusions.

Results

Multivariable Cox regression analysis of the database cohort showed that age, gender, tumor size, pathological grade and marital status were independent risk factors for prognosis. Interaction test showed that there was an interaction between age and tumor size (P for interaction < 0.05). Stratified analysis by age showed that tumor size was an independent risk factor for prognosis when age ≤65 years old (HR:1.010,95%CI1.007-1.013 P < 0.001), while tumor size was not an independent risk factor for prognosis when age >65 years old. This result was confirmed by trend analysis (P for trend < 0.001), and the prognostic risk increased by 42.1% for each standard deviation increase of tumor size among patients age ≤65 years. Consistent conclusion was obtained by multivariable cox regression analysis and interaction test on the verification cohort. In the validation cohort, for each standard deviation increase of tumor size in patients ≤65 years old, the risk of prognosis increased by 52.4%.

Conclusion

Tumor size is not an independent risk factor for the prognosis of isolated HCC without vascular invasion when patient's age >65 years. Therefore, when analyzing the relationship between tumor size and prognosis, stratified analysis should be performed according to age.

A new prognostic model predicting hepatocellular carcinoma early recurrence in patients with microvascular invasion who received postoperative adjuvant transcatheter arterial chemoembolization

BACKGROUD

In this study, we aimed to develop a prognostic model to predict HCC early recurrence (within 1-year) in patients with microvascular invasion who received postoperative adjuvant transcatheter arterial chemoembolization (PA-TACE).

METHODS

A total of 148 HCC patients with MVI who received PA-TACE were included in this study. The modes were verified in an internal validation cohort (n = 112) and an external cohort (n = 36). Univariate and multivariate Cox regression analyses were performed to identify the independent prognostic factors relevant to early recurrence. A clinical nomogram prognostic model was established, and nomogram performance was assessed via internal validation and calibration curve statistics.

RESULTS

After data dimensionality reduction and element selection, multivariate Cox regression analysis indicated that alpha fetoprotein level, systemic inflammation response index, alanine aminotransferase, tumour diameter and portal vein tumour thrombus were independent prognostic factors of HCC early recurrence in patients with MVI who underwent PA-TACE. Nomogram with independent factors was established and achieved a better concordance index of 0.765 (95% CI: 0.691-0.839) and 0.740 (95% CI: 0.583-0.898) for predicting early recurrence in training cohort and validation cohort, respectively. Time-dependent AUC indicated comparative stability and adequate discriminative ability of the model. The DCA revealed that the nomogram could augment net benefits and exhibited a wider range of threshold probabilities than AJCC T stage.

CONCLUSIONS

The nomogram prognostic model showed adequate discriminative ability and high predictive accuracy.

Higher Risk of Tumor Recurrence in NASH-Related Hepatocellular Carcinoma Following Curative Resection

The outcomes for patients with NASH-related HCC after curative resection have not been clarified. This study compared the overall survival (OS), time-to-tumor recurrence (TTR), and recurrence-free survival (RFS) associated with NASH-related HCC and virus-related HCC after resection. Methods: Patients with HCC who underwent curative resection were retrospectively enrolled. Baseline characteristics, including disease etiologies and clinical and tumor features, were reviewed. The primary outcomes were OS, TTR, and RFS. Results: Two hundred and six patients were enrolled (HBV: n = 121, HCV: n = 54, NASH: n = 31). Of those with virus-related HCC, 84.0% achieved viral suppression. In both the overall and propensity-score-matched cohorts, those with NASH-related HCC experienced recurrence significantly earlier than those with virus-related HCC (median TTR: 1108 days vs. non-reached; p = 0.03). Through multivariate analysis, NASH-related HCC (hazard ratio (HR), 2.27; 95% confidence interval (CI), 1.25-4.12) was independently associated with early recurrence. The unadjusted RFS rate of the NASH-related HCC group was lower than the virus-related HCC group. There was no difference in the OS between the two groups. Conclusions: NASH-related HCC was associated with earlier tumor recurrence following curative resection compared to virus-related HCC. Post-surgical surveillance is crucial for detecting early recurrence in patients with NASH-related HCC.

Cancer cells produce liver metastasis via gap formation in sinusoidal endothelial cells through proinflammatory paracrine mechanisms

Intracellular gap (iGap) formation in liver sinusoidal endothelial cells (LSECs) is caused by the destruction of fenestrae and appears under pathological conditions; nevertheless, their role in metastasis of cancer cells to the liver remained unexplored. We elucidated that hepatotoxin-damaged and fibrotic livers gave rise to LSECs-iGap formation, which was positively correlated with increased numbers of metastatic liver foci after intrasplenic injection of Hepa1-6 cells. Hepa1-6 cells induced interleukin-23-dependent tumor necrosis factor-α (TNF-α) secretion by LSECs and triggered LSECs-iGap formation, toward which their processes protruded to transmigrate into the liver parenchyma. TNF-α triggered depolymerization of F-actin and induced matrix metalloproteinase 9 (MMP9), intracellular adhesion molecule 1, and CXCL expression in LSECs. Blocking MMP9 activity by doxycycline or an MMP2/9 inhibitor eliminated LSECs-iGap formation and attenuated liver metastasis of Hepa1-6 cells. Overall, this study revealed that cancer cells induced LSEC-iGap formation via proinflammatory paracrine mechanisms and proposed MMP9 as a favorable target for blocking cancer cell metastasis to the liver.

Accurate prediction of microvascular invasion occurrence and effective prognostic estimation for patients with hepatocellular carcinoma after radical surgical treatment

Background

Hepatocellular carcinoma (HCC) is the third most common cause of cancer death worldwide, with an overall 5-year survival rate of less than 18%, which may be related to tumor microvascular invasion (MVI). This study aimed to compare the clinical prognosis of HCC patients with or without MVI after radical surgical treatment, and further analyze the preoperative risk factors related to MVI to promote the development of a new treatment strategy for HCC.

Methods

According to the postoperative pathological diagnosis of MVI, 160 study patients undergoing radical hepatectomy were divided into an MVI-negative group (n = 68) and an MVI-positive group (n = 92). The clinical outcomes and prognosis were compared between the two groups, and then the parameters were analyzed by multivariate logistic regression to construct an MVI prediction model. Then, the practicability and validity of the model were evaluated, and the clinical prognosis of different MVI risk groups was subsequently compared.

Result

There were no significant differences between the MVI-negative and MVI-positive groups in clinical baseline, hematological, or imaging data. Additionally, the clinical outcome comparison between the two groups presented no significant differences except for the pathological grading (P = 0.002) and survival and recurrence rates after surgery (P < 0.001). The MVI prediction model, based on preoperative AFP, tumor diameter, and TNM stage, presented superior predictive efficacy (AUC = 0.7997) and good practicability (high H-L goodness of fit, P = 0.231). Compared with the MVI high-risk group, the patients in the MVI low-risk group had a higher survival rate (P = 0.002) and a lower recurrence rate (P = 0.004).

Conclusion

MVI is an independent risk factor for a poor prognosis after radical resection of HCC. The MVI prediction model, consisting of AFP, tumor diameter, and TNM stage, exhibits superior predictive efficacy and strong clinical practicability for MVI prediction and prognostication, which provides a new therapeutic strategy for the standardized treatment of HCC patients.

Prediction Model and Nomogram of Early Recurrence of Hepatocellular Carcinoma after Radiofrequency Ablation Based on Logistic Regression Analysis

The purpose of this study was to screen for high-risk factors leading to the early recurrence of hepatocellular carcinoma (HCC) after radiofrequency ablation (RFA) and to construct a prediction model and nomogram. This retrospective study included 108 patients with primary HCC who underwent RFA treatment at the Harbin Medical University Cancer Hospital between January 2018 and June 2019. Four risk factors were screened for using univariate and multivariate logistic regression analyses: number of tumors (hazard ratio [HR] = 14.684, 95% confidence interval [CI]: 1.099-196.215, p = 0.042), neutrophil-to-lymphocyte ratio (NLR) (HR = 2.178, 95% CI: 1.003-4.730, p = 0.049), contrast-enhanced ultrasound (CEUS) performance (HR = 6.482, 95% CI: 1.161-36.184, p = 0.033) and α-fetoprotein (AFP) level (HR = 1.001, 95% CI: 1.000-1.003, p = 0.040). We established a prediction model: Logit(p) = -3.096 + 2.827 × (number of tumors >1 = 1) + 1.851 × (CEUS revealing rapid enhancement of blood flow signal in the arterial phase and clearance in the portal phase = 1) + 1.941 × (NLR >1.55 = 1) + 0.257 × (AFP >32.545 = 1). Through clinical decision curve analysis, the model's threshold was 0.043-0.873, indicating a high clinical value. Patients with a high AFP level, typical CEUS enhancement pattern, multiple tumors and elevated NLR are more likely to relapse early.

Integrative Analysis Identifies Cell-Type-Specific Genes Within Tumor Microenvironment as Prognostic Indicators in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, but effective early detection and prognostication methods are lacking.

Methods

The Cox regression model was built to stratify the HCC patients. The single-cell RNA sequencing data analysis and gene set enrichment analysis were employed to investigate the biological function of identified markers. PLCB1 gain- or loss-of-function experiments were performed, and obtained HCC samples were analyzed using quantitative real-time PCR and immunohistochemistry assay to validate the biological function of identified markers.

Results

In this study, we developed a model using optimized markers for HCC recurrence prediction. Specifically, we screened out 8 genes through a series of data analyses, and built a multivariable Cox model based on their expression. The risk stratifications using the Eight-Gene Cox (EGC) model were closely associated with the recurrence-free survivals (RFS) in both training and three validation cohorts. We further demonstrated that this risk stratification could serve as an independent predictor in predicting HCC recurrence, and that the EGC model could outperform other models. Moreover, we also investigated the cell-type-specific expression patterns of the eight recurrence-related genes in tumor microenvironment using single-cell RNA sequencing data, and interpreted their functional roles from correlation and gene set enrichment analyses, in vitro and in vivo experiments. Particularly, PLCB1 and SLC22A7 were predominantly expressed in malignant cells, and they were predicted to promote angiogenesis and to help maintain normal metabolism in liver, respectively. In contrast, both FASLG and IL2RB were specifically expressed in T cells, and were highly correlated with T cell marker genes, suggesting that these two genes might assist in maintaining normal function of T cell-mediated immune response in tumor tissues.

Conclusion

In conclusion, the EGC model and eight identified marker genes could not only facilitate the accurate prediction of HCC recurrence, but also improve our understanding of the mechanisms behind HCC recurrence.

Identification of the Immune Subtype of Hepatocellular Carcinoma for the Prediction of Disease-Free Survival Time and Prevention of Recurrence by Integrated Analysis of Bulk- and Single-Cell RNA Sequencing Data

Background

The main factors affecting the long-term prognosis of hepatocellular carcinoma (HCC) patients undergoing radical surgery are recurrence and metastasis. However, the methods for predicting disease-free survival (DFS) time and preventing postoperative recurrence of HCC are still very limited.

Methods

In this study, immune cell abundances in HCC samples were analyzed by single-sample gene set enrichment analysis (ssGSEA), while the prognostic values of immune cells for DFS time prediction were evaluated by the least absolute shrinkage and selection operator (LASSO) and subsequent univariate and multivariate Cox analyses. Next, a risk score was constructed based on the most prognostic immune cells and their corresponding coefficients. Interactions among prognostic immune cells and the specific targets for the prevention of recurrence were further identified by single-cell RNA (scRNA) sequencing data and CellMiner.

Results

A novel efficient T cell risk score (TCRS) was constructed based on data from the three most prognostic immune cell types (effector memory CD8 T cells, regulatory T cells and follicular helper T cells) for identifying an immune subtype of HCC patients with longer DFS times and inflammatory immune characteristics. Functional differences between the high- and low-score groups separated by TCRS were clarified, and the cell-cell communication among these immune cells was elucidated. Finally, fifteen hub genes that may be potential therapeutic targets for the prevention of recurrence were identified.

Conclusions

We constructed and verified a useful model for the prediction of DFS time of HCC after surgery. In addition, fifteen hub genes were identified as candidates for the prevention of recurrence, and a preliminarily investigation of potential drugs targeting these hub genes was carried out.

Distance Matters: Correlation of Hepatocellular Carcinoma Nodule Distance to Overall Survival

INTRODUCTION

Hepatocellular carcinoma (HCC) may occur with several simultaneous tumor foci in the liver (multifocal HCC). Molecular biology indicated that the larger the distance between two tumor nodules, the more those two nodules differed in their genetic composition. Therefore, we explored whether the overall survival (OS) of patients with HCC depends on the mutual distance of the HCC nodules.

METHODS

In a retrospective study of 92 patients, CT/MRI images and survival data of the patients were collected. Based on the CT or MRI images at the time of diagnosis, the size of each tumor, the distance between the centers (center distance), and adjacent edges (edge distance) of the tumor nodules were measured, respectively. These data, combined with the number of tumor nodules and clinical characteristics, were compared with the patient's OS data.

RESULTS

As expected, the average tumor diameter was significantly associated with patient survival in univariate Cox regression analysis (p = 0.00028, hazard ratio [HR] = 1.2). However, in multivariate analysis, the average center distance (p = 0.036, HR = 1.18) and average edge distance (p = 0.033, HR = 0.84) were also significantly associated with survival.

CONCLUSION

Thus, not only the size of multiple HCC lesions but also their distance is important for the prognosis of patients with HCC. This may be of particular interest in patients with two nodules and BCLC B and C stages for the selection of therapeutic modalities and/or procedures.