Beneficial body mass index to enhance survival outcomes in patients with early-stage hepatocellular carcinoma following microwave ablation treatment

The hepatocyte nuclear factor 1 homeobox A (HNF1A) gene polymorphism and AFP serum levels in Egyptian HCC patients

Background

Hepatocyte nuclear factors were first identified as liver-enriched transcription factors that might participate in various activities related to the transcription of genes unique to the liver.

Objective

The study aimed to reveal the impact of HNF1A gene variations on disease progression in hepatocellular carcinoma (HCC) patients and its relation to serum alpha-fetoprotein level.

Methods

Participants in the study were classified as Group I, 32 HCC patients; Group II, 36 chronic hepatitis C patients; and Group III, 26 healthy volunteers as a control group.

Each patient underwent full history taking, thorough clinical examination, and radiological examination. Furthermore, tumor staging was done using BCLC staging system. HNF1A gene polymorphisms (rs 2,464,196 and rs 1,169,310) were genotyped by real-time PCR.

Results

The findings revealed the highest frequency of AA and GA genotypes of HNF1A (rs2464196) polymorphism in both HCC (P = 0.002) and chronic HCV (P = 0.004) patients in comparison with controls. Regarding rs1169310gene polymorphism, no significant variation was observed across various genotypes when comparing the experimental groups to the control group. Additionally, HCC patients harboring the AA genotype for rs2464196 had significantly increased AFP (≥ 200 ng/ml) levels, whereas HCC patients with rs1169310 SNPs for HNF1A had no significant association regarding the AFP level.

Conclusion

The rs2464196 polymorphism of HNF1 is associated with increased AFP levels and HCC disease progression, which may be a prognostic and diagnostic genetic indicator.

Prognostic model for predicting recurrence in hepatocellular carcinoma patients with high systemic immune-inflammation index based on machine learning in a multicenter study

Introduction

This study aims to use machine learning to conduct in-depth analysis of key factors affecting the recurrence of HCC patients with high preoperative systemic immune-inflammation index (SII) levels after receiving ablation treatment, and based on this, construct a nomogram model for predicting recurrence-free survival (RFS) of patients.

Methods

This study included clinical data of 505 HCC patients who underwent ablation therapy at Beijing You'an Hospital from January 2014 to January 2020, and accepted 65 HCC patients with high SII levels from Beijing Ditan Hospital as an external validation cohort. 505 patients from Beijing You'an Hospital were divided into low SII and high SII groups based on the optimal cutoff value of SII scores. The high SII group was further randomly divided into training and validation cohorts in a 7:3 ratio. eXtreme Gradient Boosting (XGBoost), random survival forest (RSF), and multivariate Cox regression analysis, were used to explore the factors affecting the post-ablation RFS of HCC patients. Based on the identified key factors, a nomogram model were developed to predict RFS in HCC patients, and their performance were evaluated using the concordance index (C index), receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). The optimal cutoff value for nomogram scores was used to divide patients into low- and high-risk groups, and the effectiveness of the model in risk stratification was evaluated using Kaplan-Meier (KM) survival curves.

Results

This study confirmed that age, BCLC stage, tumor number, and GGT level were independent risk factors affecting RFS in HCC patients. Based on the selected risk factors, an RFS nomogram was successfully constructed. The C-index, ROC curve, calibration curve, and DCA curve each demonstrated the discrimination, accuracy, and decision-making utility of the nomogram, indicating that it has good predictive performance. KM curve revealed the nomogram could significantly differentiate patient populations with different recurrence risk.

Conclusion

We developed a reliable nomogram that can accurately predict the 1-, 3-, and 5-year RFS for HCC patients with high SII levels following ablation therapy.

Associations between body mass index and all-cause mortality: A systematic review and meta-analysis

Fasting insulin and c-reactive protein confound the association between mortality and body mass index. An increase in fat mass may mediate the associations between hyperinsulinemia, hyperinflammation, and mortality. The objective of this study was to describe the "average" associations between body mass index and the risk of mortality and to explore how adjusting for fasting insulin and markers of inflammation might modify the association of BMI with mortality. MEDLINE and EMBASE were searched for studies published in 2020. Studies with adult participants where BMI and vital status was assessed were included. BMI was required to be categorized into groups or parametrized as non-first order polynomials or splines. All-cause mortality was regressed against mean BMI squared within seven broad clinical populations. Study was modeled as a random intercept. β coefficients and 95% confidence intervals are reported along with estimates of mortality risk by BMIs of 20, 30, and 40 kg/m2 . Bubble plots with regression lines are drawn, showing the associations between mortality and BMI. Splines results were summarized. There were 154 included studies with 6,685,979 participants. Only five (3.2%) studies adjusted for a marker of inflammation, and no studies adjusted for fasting insulin. There were significant associations between higher BMIs and lower mortality risk in cardiovascular (unadjusted β -0.829 [95% CI -1.313, -0.345] and adjusted β -0.746 [95% CI -1.471, -0.021]), Covid-19 (unadjusted β -0.333 [95% CI -0.650, -0.015]), critically ill (adjusted β -0.550 [95% CI -1.091, -0.010]), and surgical (unadjusted β -0.415 [95% CI -0.824, -0.006]) populations. The associations for general, cancer, and non-communicable disease populations were not significant. Heterogeneity was very large (I2  ≥ 97%). The role of obesity as a driver of excess mortality should be critically re-examined, in parallel with increased efforts to determine the harms of hyperinsulinemia and chronic inflammation.

Locoregional Thermal and Chemical Tumor Ablation: Review of Clinical Applications and Potential Opportunities for Use in Low- and Middle-Income Countries

This review highlights opportunities to develop accessible ablative therapies to reduce the cancer burden in LMICs.

A Novel Nomogram Model to Predict the Recurrence-Free Survival and Overall Survival of Hepatocellular Carcinoma

Background

Treatments for patients with early‐stage hepatocellular carcinoma (HCC) include liver transplantation (LT), liver resection (LR), radiofrequency ablation (RFA), and microwave ablation (MWA), are critical for their long-term survival. However, a computational model predicting treatment-independent prognosis of patients with HCC, such as overall survival (OS) and recurrence-free survival (RFS), is yet to be developed, to our best knowledge. The goal of this study is to identify prognostic factors associated with OS and RFS in patients with HCC and develop nomograms to predict them, respectively.

Methods

We retrospectively retrieved 730 patients with HCC from three hospitals in China and followed them up for 3 and 5 years after invasive treatment. All enrolled patients were randomly divided into the training cohort and the validation cohort with a 7:3 ratio, respectively. Independent prognostic factors associated with OS and RFS were determined by the multivariate Cox regression analysis. Two nomogram prognostic models were built and evaluated by concordance index (C-index), calibration curves, area under the receiver operating characteristics (ROC) curve, time-dependent area under the ROC curve (AUC), the Kaplan–Meier survival curve, and decision curve analyses (DCAs), respectively.

Results

Prognostic factors for OS and RFS were identified, and nomograms were successfully built. Calibration discrimination was good for both the OS and RFS nomogram prediction models (C-index: 0.750 and 0.746, respectively). For both nomograms, the AUC demonstrated outstanding predictive performance; the DCA shows that the model has good decision ability; and the calibration curve demonstrated strong predictive power. The nomograms successfully discriminated high-risk and low-risk patients with HCC associated with OS and RFS.

Conclusions

We developed nomogram survival prediction models to predict the prognosis of HCC after invasive treatment with acceptable accuracies in both training and independent testing cohorts. The models may have clinical values in guiding the selection of clinical treatment strategies.

Are all local tumour progressions of HCC related to thermal ablation? A study of the causes and classification of local tumour progression

OBJECTIVES

Local tumour progression (LTP) is believed to be a negative consequence of imperfect thermal ablation, but we wondered if all LTP is truly due to imperfect ablation.

METHODS

This study included 185 LTPs occurring within 1 cm of the ablation zone (AZ) after clinical curative thermal ablation for ≤ 5 cm hepatocellular carcinoma between 2010 and 2019. The AZ was divided into 8 quadrants by coronal, sagittal, and horizontal planes. Two methods, visual assessment through pre- and post-MRI (VA) and tumour mapping for 3D visualisation pre- and post-MRI fusion (MF), were used to assess which AZ quadrant included the shortest ablation margin (AM) by three doctors. LTP subclassification was based on whether LTP contacted the AZ margin (contacted LTP and dissociated-type LTP) and occurrence at different time points (12, 18, and 24 months).

RESULTS

Fleiss's Kappa of VA and MF was 0.769 and 0.886, respectively. Cohen's Kappa coefficient between VA and MF was 0.830. For all LTPs, 98/185 (53.0%) occurred in the shortest AM quadrant, which showed a significant central tendency (p < 0.001). However, only 8/51 (15.7%) dissociated - type LTPs and 6/39 (15.4%) LTPs after 24 months occurred in the shortest AM quadrant, which showed no evenly distributed difference (p = 0.360 and 0.303).

CONCLUSIONS

MF is an accurate and convenient method to assess the shortest AM quadrant. LTP is a central tendency in the shortest AM quadrant, but dissociated-type and LTPs after 24 months are not, and these LTP types could be considered nonablation-related LTPs.

KEY POINTS

• LTPs are not evenly distributed around the AZ. More than half of LTPs occur in the shortest AM quadrant. • Subgroup analysis showed that the occurrence of contacted-type LTPs (tumour margin has direct contact with the AZ) within 24 months after ablation indeed had a high proportion in the shortest AM quadrant, and they could be called ablation-related LTPs. • However, the dissociated-type LTPs (tumour margin adjacent to but not in contact with the AZ) or LTPs occurring beyond 24 months after ablation were evenly distributed around the AZ, and they could be called nonablation-related LTPs.

Fat Quantification Imaging and Biophysical Modeling for Patient-Specific Forecasting of Microwave Ablation Therapy

Computational tools are beginning to enable patient-specific surgical planning to localize and prescribe thermal dosing for liver cancer ablation therapy. Tissue-specific factors (e.g., tissue perfusion, material properties, disease state, etc.) have been found to affect ablative therapies, but current thermal dosing guidance practices do not account for these differences. Computational modeling of ablation procedures can integrate these sources of patient specificity to guide therapy planning and delivery. This paper establishes an imaging-data-driven framework for patient-specific biophysical modeling to predict ablation extents in livers with varying fat content in the context of microwave ablation (MWA) therapy. Patient anatomic scans were segmented to develop customized three-dimensional computational biophysical models and mDIXON fat-quantification images were acquired and analyzed to establish fat content and determine biophysical properties. Simulated patient-specific microwave ablations of tumor and healthy tissue were performed at four levels of fatty liver disease. Ablation models with greater fat content demonstrated significantly larger treatment volumes compared to livers with less severe disease states. More specifically, the results indicated an eightfold larger difference in necrotic volumes with fatty livers vs. the effects from the presence of more conductive tumor tissue. Additionally, the evolution of necrotic volume formation as a function of the thermal dose was influenced by the presence of a tumor. Fat quantification imaging showed multi-valued spatially heterogeneous distributions of fat deposition, even within their respective disease classifications (e.g., low, mild, moderate, high-fat). Altogether, the results suggest that clinical fatty liver disease levels can affect MWA, and that fat-quantitative imaging data may improve patient specificity for this treatment modality.

Percutaneous microwave ablation versus robot-assisted hepatectomy for early hepatocellular carcinoma: A real-world single-center study

BACKGROUND

Both microwave ablation and robot-assisted hepatectomy are representative minimally invasive treatments for early hepatocellular carcinoma. Our study compares the practicability and medium-term therapeutic efficacy between them.

METHODS

Patients with early HCC treated by MWA or RH from 2013 to 2019 were included. Propensity score matching (PSM) and inverse probability of treatment weight (IPTW) were used to minimize baseline imbalance. Operation trauma, postoperative recovery, complications, cost and oncological efficacy were compared.

RESULTS

401 patients with a median follow-up of 28 months were included (MWA n = 240; RH n = 161). After PSM, 3-year recurrence-free survival (RFS), overall survival (OS) and cancer-specific survival (CSS) of MWA group and RH group were 52.2% vs 65.8%, 91.5% vs 91.3% and 91.5% vs 91.3%, respectively. OS and CSS were comparable (p = 0.44 and 0.96), while RFS of MWA was slightly lower but not significant (p = 0.097). The above results after IPTW followed the same trend. After PSM, MWA showed advantages in operation time and blood loss, while RH performed better in postoperative liver function. There was no significant difference in incidence of severe complications between two groups.

CONCLUSIONS

For early HCC parents, both treatments can achieve good, safe and comparable medium-term therapeutic effects. MWA is more minimally invasive, while RH has better accuracy and causes less damage to liver parenchyma.

A Hybrid Machine Learning Model Based on Semantic Information Can Optimize Treatment Decision for Naïve Single 3-5-cm HCC Patients

BACKGROUND

Tumor recurrence is an abomination for hepatocellular carcinoma (HCC) patients receiving local treatment.

PURPOSE

The aim of the study was to build a hybrid machine learning model to recommend optimized first treatment (laparoscopic hepatectomy [LH] or microwave ablation [MWA]) for naïve single 3-5-cm HCC patients based on early recurrence (ER, ≤2 years) probability.

METHODS

This retrospective study collected 20 semantic variables of 582 patients (LH: 300, MWA: 282) from 13 hospitals with at least 24 months follow-up. Both groups were divided into training, validation, and test set, respectively. Five algorithms (logistics regression, random forest, neural network, stochastic gradient boosting, and eXtreme Gradient Boosting [XGB]) were used for model building. A model with highest area under the receiver operating characteristic curve (AUC) in a validation set of LH and MWA was selected to connect as a hybrid model which made decision based on ER probability. Model testing was performed in a comprehensive set comprising LH and MWA test sets.

RESULTS

Four variables in each group were selected to build LH and MWA models, respectively. LH-XGB model (AUC = 0.744) and MWA-stochastic gradient method (AUC = 0.750) model were selected for model building. In the comprehensive set, a treatment confusion matrix was established based on recommended and actual treatment. The predicted ER probabilities were comparable with the actual ER rates for various types of patients in matrix (p > 0.05). ER rate of patients whose actual treatment consistent with recommendation was lower than that of inconsistent patients (LH: 21.2% vs. 46.2%, p = 0.042; MWA: 26.3% vs. 54.1%, p = 0.048). By recommending optimal treatment, the hybrid model can significantly reduce ER probability from 38.2% to 25.6% for overall patients (p < 0.001).

CONCLUSIONS

The hybrid model can accurately predict ER probability of different treatments and thereby provide reliable evidence to make optimal treatment decision for patients with single 3-5-cm HCC.

MR-guided microwave ablation of hepatocellular carcinoma (HCC): is general anesthesia more effective than local anesthesia?

Background

Percutaneous magnetic resonance-guided (MR-guided) MWA procedures have traditionally been performed under local anesthesia (LA) and sedation. However, pain control is often difficult to manage, especially in some cases when the tumor is large or in a specific location, such as near the abdominal wall or close to the hepatic dome. This study retrospectively compared the results of general anesthesia (GA) and local anesthesia (LA) for MR-guided microwave ablation (MWA) in patients with hepatocellular carcinoma (HCC ≤ 5.0 cm) to investigate whether different anesthesia methods lead to different clinical outcomes.

Methods

The results of the analysis include procedure-related complications, imaging response, and the time to complete two sets of procedures. According to the type of anesthesia, the Kaplan-Meier method was used to compare the local tumor progression (LTP) of the two groups who underwent MR-guided MWA.

Results

All patients achieved technical success. The mean ablation duration of each patient in the GA group and LA group was remarkably different (P = 0.012). Both groups had no difference in complications or LTP (both P > 0.05). Notably, the tumor location (challenging locations) and the number of lesions (2–3 lesions) could be the main factors affecting LTP (p = 0.000, p = 0.015). Univariate Cox proportional hazard regression indicated that using different anesthesia methods (GA and LA) was not associated with longer LTP (P = 0.237), while tumor location (challenging locations) and the number of lesions (2–3 lesions) were both related to shorter LTP (P = 0.000, P = 0.020, respectively). Additionally, multivariate Cox regression further revealed that the tumor location (regular locations) and the number of lesions (single) could independently predict better LTP (P = 0.000, P = 0.005, respectively).

Conclusions

No correlation was observed between GA and LA for LTP after MR-guided MWA. However, tumors in challenging locations and the number of lesions (2–3 lesions) appear to be the main factors affecting LTP.