Hepatocellular carcinoma in a rapidly growing community: Epidemiology, clinico-pathology and predictors of extrahepatic metastasis

Radiomics and machine learning based on preoperative MRI for predicting extrahepatic metastasis in hepatocellular carcinoma patients treated with transarterial chemoembolization

Purpose

To develop and validate a radiomics machine learning (Rad-ML) model based on preoperative MRI to predict extrahepatic metastasis (EHM) in hepatocellular carcinoma (HCC) patients receiving transarterial chemoembolization (TACE) treatment.

Methods

A total of 355 HCC patients who received multiple TACE procedures were split at random into a training set and a test set at a 7:3 ratio. Radiomic features were calculated from tumor and peritumor in arterial phase and portal venous phase, and were identified using intraclass correlation coefficient, maximal relevance and minimum redundancy, and least absolute shrinkage and selection operator techniques. Cox regression analysis was employed to determine the clinical model. The best-performing algorithm among eight machine learning methods was used to construct the Rad-ML model. A nomogram combining clinical and Rad-ML parameters was used to develop a combined model. Model performance was evaluated using C-index, decision curve analysis, calibration plot, and survival analysis.

Results

In clinical model, elevated neutrophil to lymphocyte ratio and alpha-fetoprotein were associated with faster EHM. The XGBoost-based Rad-ML model demonstrated the best predictive performance for EHM. When compared to the clinical model, both the Rad-ML model and the combination model performed better (C-indexes of 0.61, 0.85, and 0.86 in the training set, and 0.62, 0.82, and 0.83 in the test set, respectively). However, the combined model's and the Rad-ML model's prediction performance did not differ significantly. The most influential feature was peritumoral waveletHLL_firstorder_Minimum in AP, which exhibited an inverse relationship with EHM risk.

Conclusions

Our study suggests that the preoperative MRI-based Rad-ML model is a valuable tool to predict EHM in HCC patients treated with TACE.

Conditional survival probability of distant-metastatic hepatocellular carcinoma: A population-based study

BACKGROUND

The prognosis of many patients with distant metastatic hepatocellular carcinoma (HCC) improved after they survived for several months. Compared with traditional survival analysis, conditional survival (CS) which takes into account changes in survival risk could be used to describe dynamic survival probabilities.

AIM

To evaluate CS of distant metastatic HCC patients.

METHODS

Patients diagnosed with distant metastatic HCC between 2010 and 2015 were extracted from the Surveillance, Epidemiology and End Results database. Univariate and multivariate Cox regression analysis were used to identify risk factors for overall survival (OS), while competing risk model was used to identify risk factors for cancer-specific survival (CSS). Six-month CS was used to calculate the probability of survival for an additional 6 mo at a specific time after initial diagnosis, and standardized difference (d) was used to evaluate the survival differences between subgroups. Nomograms were constructed to predict CS.

RESULTS

Positive α-fetoprotein expression, higher T stage (T3 and T4), N1 stage, non-primary site surgery, non-chemotherapy, non-radiotherapy, and lung metastasis were independent risk factors for actual OS and CSS through univariate and multivariate analysis. Actual survival rates decreased over time, while CS rates gradually increased. As for the 6-month CS, the survival difference caused by chemotherapy and radiotherapy gradually disappeared over time, and the survival difference caused by lung metastasis reversed. Moreover, the influence of age and gender on survival gradually appeared. Nomograms were fitted for patients who have lived for 2, 4 and 6 mo to predict 6-month conditional OS and CSS, respectively. The area under the curve (AUC) of nomograms for conditional OS decreased as time passed, and the AUC for conditional CSS gradually increased.

CONCLUSION

CS for distant metastatic HCC patients substantially increased over time. With dynamic risk factors, nomograms constructed at a specific time could predict more accurate survival rates.

Current Status of Management of Hepatocellular Carcinoma in The Gulf Region: Challenges and Recommendations

The burden of hepatocellular carcinoma (HCC) is on the rise in the Gulf region, with most patients being diagnosed in the intermediate or advanced stages. Surgery is a treatment option for only a few, and the majority of patients receive either locoregional treatment (percutaneous ethanol injection, radiofrequency ablation, transarterial chemoembolization [TACE], radioembolization, radiotherapy, or transarterial radioembolization) or systemic therapy (for those ineligible for locoregional treatments or who do not benefit from TACE). The recent emergence of novel immunotherapies such as immune checkpoint inhibitors has begun to change the landscape of systemic HCC treatment in the Gulf. The combination of atezolizumab and bevacizumab is currently the preferred first-line therapy in patients not at risk of bleeding. Additionally, the HIMALAYA trial has demonstrated the superiority of the durvalumab plus tremelimumab combination (STRIDE regimen) therapy in efficacy and safety compared with sorafenib in patients with unresectable HCC. However, there is a lack of data on post-progression treatment after first-line therapy with either atezolizumab plus bevacizumab or durvalumab plus tremelimumab regimens, highlighting the need for better-designed studies for improved management of patients with unresectable HCC in the Gulf region.

Short-term prognosis for hepatocellular carcinoma patients with lung metastasis: A retrospective cohort study based on the SEER database

Our study aimed to develop a prediction model to predict the short-term mortality of hepatocellular carcinoma (HCC) patients with lung metastasis. The retrospective data of HCC patients with lung metastasis was from the Surveillance, Epidemiology, and End Results registration database between 2010 and 2015. 1905 patients were randomly divided into training set (n = 1333) and validation set (n = 572). There were 1092 patients extracted from the Surveillance, Epidemiology, and End Results database 2015 to 2019 as the validation set. The variable importance was calculated to screen predictors. The constructed prediction models of logistic regression, random forest, broad learning system, deep neural network, support vector machine, and naïve Bayes were compared through the predictive performance. The mortality of HCC patients with lung metastasis was 51.65% within 1 month. The screened prognostic factors (age, N stage, T stage, tumor size, surgery, grade, radiation, and chemotherapy) and gender were used to construct prediction models. The area under curve (0.853 vs. 0.771) of random forest model was more optimized than that of logistic regression model in the training set. But, there were no significant differences in testing and validation sets between random forest and logistic regression models. The value of area under curve in the logistic regression model was significantly higher than that of the broad learning system model (0.763 vs. 0.745), support vector machine model (0.763 vs. 0.689) in the validation set, and higher than that of the naïve Bayes model (0.775 vs. 0.744) in the testing model. We further chose the logistic regression prediction model and built the prognostic nomogram. We have developed a prediction model for predicting short-term mortality with 9 easily acquired predictors of HCC patients with lung metastasis, which performed well in the internal and external validation. It could assist clinicians to adjust treatment strategies in time to improve the prognosis.

Predictors of Macrovascular Invasion and Extrahepatic Metastasis in Treatment Naive Hepatocellular Carcinoma: When Is [18F] FDG PET/CT Relevant?

Purpose

Hypermetabolic macrovascular invasion (MVI) and extrahepatic metastasis (EHM) occur in aggressive hepatocellular carcinoma (HCC) and carry unfavorable prognosis. [¹⁸F] FDG PET/CT, despite having low sensitivity in primary HCC, is valuable in patients with aggressive HCC for detection of hypermetabolic MVI and EHM. The study aimed at identifying the parameters that could predict hypermetabolic MVI and/or EHM in treatment naive HCC patients for tailored approach to utilize [¹⁸F] FDG PET/CT.

Methods

Data of 131 treatment naive HCC patients (median age, 60 years; range, 21-80 years; 90.8% males) who underwent [¹⁸F] FDG PET/CT were retrospectively analyzed to determine the proportion of patients with hypermetabolic MVI and/or EHM. Logistic regression analysis was performed to define independent predictors of hypermetabolic MVI and/or EHM.

Results

78/131 (59.5%) patients had hypermetabolic MVI and/or EHM. 52/131 (39.7%) patients had EHM. 56/131 (42.7%) patients had hypermetabolic MVI of which, 30 had concomitant EHM with majority (90%; 27/30) having distant metastasis. 26/131 (19.8%) patients had hypermetabolic MVI without EHM while 22/131 (16.8%) patients had EHM without hypermetabolic MVI of which, majority (95.5%; 21/22) had distant metastasis. Hypermetabolic MVI was associated with EHM (χ² = 7.868; p value = 0.007). AFP > 93.7 ng/ml, SUVmax > 3.5, and maximum tumor size > 5.0 cm were the independent predictors of hypermetabolic MVI and/or EHM.

Conclusion

In treatment naive HCC patients with AFP > 93.7 ng/ml or maximum tumor size > 5.0 cm, [¹⁸F] FDG PET/CT can be valuable.

Predicting early refractoriness of transarterial chemoembolization in patients with hepatocellular carcinoma using a random forest algorithm: A pilot study

Purpose: To develop and validate a random forest (RF) based predictive model of early refractoriness to transarterial chemoembolization (TACE) in patients with unresectable hepatocellular carcinoma (HCC).

Methods: A total of 227 patients with unresectable HCC who initially treated with TACE from three independent institutions were retrospectively included. Following a random split, 158 patients (70%) were assigned to a training cohort and the remaining 69 patients (30%) were assigned to a validation cohort. The process of variables selection was based on the importance variable scores generated by RF algorithm. A RF predictive model incorporating the selected variables was developed, and five-fold cross-validation was performed. The discrimination and calibration of the RF model were measured by a receiver operating characteristic (ROC) curve and the Hosmer-Lemeshow test.

Results: The potential variables selected by RF algorithm for developing predictive model of early TACE refractoriness included patients' age, number of tumors, tumor distribution, platelet count (PLT), and neutrophil-to-lymphocyte ratio (NLR). The results showed that the RF predictive model had good discrimination ability, with an area under curve (AUC) of 0.863 in the training cohort and 0.767 in the validation cohort, respectively. In Hosmer-Lemeshow test, the RF model had a satisfactory calibration with P values of 0.538 and 0.068 in training cohort and validation cohort, respectively.

Conclusion: The RF algorithm-based model has a good predictive performance in the prediction of early TACE refractoriness, which may easily be deployed in clinical routine and help to determine the optimal patient of care.

Development and Validation of a Predictive Model for Early Refractoriness of Transarterial Chemoembolization in Patients With Hepatocellular Carcinoma

Objectives: To develop and validate a predictive model for early refractoriness of transarterial chemoembolization (TACE) in patients with hepatocellular carcinoma (HCC).

Methods: In this multicenter retrospective study, a total of 204 consecutive patients who initially underwent TACE were included. Early TACE refractoriness was defined as patients presented with TACE refractoriness after initial two consecutive TACE procedures. Of all patients, 147 patients (approximately 70%) were assigned to a training set, and the remaining 57 patients (approximately 30%) were assigned to a validation set. Predictive model was established using forward stepwise logistic regression and nomogram. Based on factors selected by logistic regression, a one-to-one propensity score matching (PSM) was conducted to compare progression-free survival (PFS) between patients who were present or absent of early TACE refractoriness. PFS curve was estimated by Kaplan-Meier method and compared by log-rank test.

Results: Logistic regression revealed that bilobar tumor distribution (p = 0.002), more than three tumors (p = 0.005) and beyond up-to-seven criteria (p = 0.001) were significantly related to early TACE refractoriness. The discriminative abilities, as determined by the area under the receiver operating characteristic (ROC) curve, were 0.788 in the training cohort and 0.706 in the validation cohort. After PSM, the result showed that patients who were absent of early TACE refractoriness had a significantly higher PFS rate than those of patients who were present (p < 0.001).

Conclusion: This study presents a predictive model with moderate accuracy to identify patients with high risk of early TACE refractoriness, and patients with early TACE refractoriness may have a poor prognosis.

LncRNA UCA1 Affects the Cell Proliferation, Migration, Invasion and Apoptosis of Hepatic Carcinoma Cells by Targeting MicroRNA-193a-3p

OBJECTIVE/BACKGROUND

Hepatic carcinoma (HCC) is the fourth lethal cancer in the world, but its relationship with lncRNA urothelial cancer-associated 1 (UCA1)/microRNA-193a-3p axis remains unclear, so this study would explore the relationship.

METHODS

A real-time polymerase chain reaction (RT-PCR) assay was carried out to quantify lncRNA UCA1 and microRNA-193a-3p in HCC tissues and cells, and relevant overexpression or inhibition vectors were constructed to analyze the influences of lncRNA UCA1 and microRNA-193a-3p on HCC cells. A Transwell assay was used to measure invasion and migration of HCC cells, and a Western blot assay to quantify protein biomarkers of apoptosis, invasion, and migration, a MTT assay to determine cell viability, a flow cytometry to detect cell cycle, and a dual-luciferase reporter gene assay to analyze the correlation between lncRNA UCA1 and microRNA-193a-3p.

RESULTS

LncRNA UCA1 was increased in HCC, while microRNA-193a-3p was decreased. Down-regulated lncRNA UCA1 could up-regulate microRNA-193a-3p, and down-regulated lncRNA UCA1 or up-regulated microRNA-193a-3p would strengthen cell apoptosis and weaken cell migration, invasion, and proliferation. Furthermore, lncRNA UCA1 could negatively regulate microRNA-193a-3p by binding to it.

CONCLUSION

LncRNA UCA1 promotes malignant hyperproliferation of HCC cells by repressing microRNA-193a-3p.

Diagnostic and prognostic nomograms for bone metastasis in hepatocellular carcinoma

Background

Bone metastasis (BM) is one of the common sites of hepatocellular carcinoma (HCC), and the prognosis of BM patients is worse than patients without it. Our study aimed to identify predictors and prognostic factors of BM in HCC patients and develop two nomograms to quantify the risk of BM and the prognosis of HCC patients with BM.

Methods

We retrospectively reviewed the data of patients who were diagnosed as HCC between 2010 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database. Independent predictors for BM from HCC patients were determined by the univariate and multivariate logistic regression analysis. Independent prognostic factors for HCC patients with BM were identified by univariate and multivariate Cox regression analysis. Two nomograms were established and evaluated by calibration curves, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).

Results

Nine thousand and forty-seven patients were included. The independent risk factors of BM in newly diagnosed HCC patients are sex, grade, T stage, and N stage. The independent prognostic factors for HCC patients with BM are radiotherapy, chemotherapy, and lung metastasis. The AUC of diagnostic nomogram were 0.726 in the training set and 0.629 in the testing set. For the prognostic nomogram, the AUCs of 6-, 9-, and 12-months were 0.753, 0.799, and 0.732 in the training set and 0.698, 0.770, and 0.823 in the validation set. The calibration curve and DCA indicated the good performance of the nomogram.

Conclusions

Two nomograms were established to predict the incidence of BM in HCC patients and the prognosis of HCC patients with BM, respectively. Both nomograms have satisfactory accuracy, and clinical utility may benefit for clinical decision-making.

Risk and prognostic nomograms for hepatocellular carcinoma with newly-diagnosed pulmonary metastasis using SEER data

Purpose

This research aimed to identify risk factors of pulmonary metastasis (PM) from hepatocellular carcinoma (HCC) and prognostic factors of patients with PM from HCC at initial diagnosis.

Methods

Patients diagnosed with HCC between 2010 and 2015 were reviewed retrospectively in the Surveillance, Epidemiology, and End Results (SEER) database. Patients with PM from HCC at initial diagnosis were identified from the entire cohort. Predictors for PM from HCC were identified by multivariate logistic regression analysis. Independent prognostic factors for patients with PM were determined by univariate and multivariate Cox regression analysis. Nomograms were also constructed for quantifying risk of metastasis and overall survival estimation visually.

Results

Our research included 30,641 patients diagnosed with HCC, of whom 1,732 cases were with PM from HCC at initial diagnosis. The risk factors causing PM from HCC were age (P = 0.001), race (P < 0.001), primary tumor size (P < 0.001), T stage (P < 0.001), N stage (P < 0.001), alpha-fetoprotein (P < 0.001), bone metastasis (P < 0.001), brain metastasis (P < 0.001), and intrahepatic metastasis (P < 0.001). The significantly prognostic factors for overall survival were age (P = 0.014), T stage (P = 0.009), surgical approach (P < 0.001), and chemotherapy (P < 0.001). Harrell’s C-index statistics of two nomograms were 0.768 and 0.687 respectively, indicating satisfactory predictive power.

Conclusions

This research provided evaluation of risk factors and prognosis for patients with PM from HCC. Two nomograms we developed can be convenient individualized tools to facilitate clinical decision-making.