Development of a nomogram for screening of hepatitis B virus-associated hepatocellular carcinoma

Obesity and "obesity-related" cancers: are there body mass index cut-points?

BACKGROUND

Despite compelling links between excess body weight and cancer, body mass index (BMI) cut-points, or thresholds above which cancer incidence increased, have not been identified. The objective of this study was to determine if BMI cut-points exist for 14 obesity-related cancers.

SUBJECTS/METHODS

In this retrospective cohort study, patients 18-75 years old were included if they had ≥2 clinical encounters with BMI measurements in the electronic health record (EHR) at a single academic medical center from 2008 to 2018. Patients who were pregnant, had a history of cancer, or had undergone bariatric surgery were excluded. Adjusted logistic regression was performed to identify cancers that were associated with increasing BMI. For those cancers, BMI cut-points were calculated using adjusted quantile regression for cancer incidence at 80% sensitivity. Logistic and quantile regression models were adjusted for age, sex, race/ethnicity, and smoking status.

RESULTS

A total of 7079 cancer patients (mean age 58.5 years, mean BMI 30.5 kg/m2) and 270,441 non-cancer patients (mean age 43.8 years, mean BMI 28.8 kg/m2) were included in the study. In adjusted logistic regression analyses, statistically significant associations were identified between increasing BMI and the incidence of kidney, thyroid, and uterine cancer. BMI cut-points were identified for kidney (26.3 kg/m2) and uterine (26.9 kg/m2) cancer.

CONCLUSIONS

BMI cut-points that accurately predicted development kidney and uterine cancer occurred in the overweight category. Analysis of multi-institutional EHR data may help determine if these relationships are generalizable to other health care settings. If they are, incorporation of BMI into the screening algorithms for these cancers may be warranted.

A Primary Screening Method for Liver Cancer in Chronic Hepatitis B Carriers: A Prospective Community-Based Cohort Study

Background

Patients with hepatitis B virus (HBV) were invited to receive ultrasound and alpha-protein examination directly in China. However, not all HBV carriers need to be subjected to further tests. This study aimed to develop a feasible primary screening method to narrow down potential high-risk individuals of liver cancer among populations with HBV.

Methods

Based on a prospective community-based cohort, potential risk factors were selected as the predictors, including age, sex, smoking, alcohol consumption, diabetes, liver cancer family history, liver diseases in mothers, source of water, body mass index (BMI), and psychological trauma. Cox proportional regression model was applied to predict the 3-year absolute risk of liver cancer and derive risk scores. The area under receiver operating characteristic curve (AUROC) and calibration plot were used to assess the performance of the model. Bootstrap resampling was used for internal validation.

Results

Age, sex, BMI, alcohol consumption, liver diseases in mothers, and psychological trauma were independent risks of liver cancer. The 1- to 3-year AUROC of the prediction model was 71.15% (95% CI, 66.88–75.42), 71.16% (95% CI, 67.42–74.90), and 72.95% (95% CI, 64.20–81.70), respectively. The predicted risk was calibrated well with the observed liver cancer risk. Bootstrap resampling showed that C-index was 0.70 (0.67–0.74). A 32-point risk score was also developed and a score over 5 was identified for patients at extremely high risk.

Conclusions

A user-friendly primary screening method was created that could estimate the 3-year absolute risk of liver cancer and identify extremely high-risk individuals among the population with HBV.

Reporting and Performance of Hepatocellular Carcinoma Risk Prediction Models: Based on TRIPOD Statement and Meta-Analysis

BACKGROUND

The performance of risk prediction models for hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB) was uncertain. The aim of the study was to critically evaluate the reports of transparent and external validation performances of these prediction models based on system review and meta-analysis.

METHODS

A systematic search of the Web of Science and PubMed was performed for studies published until October 17, 2020. The transparent reporting of a multivariable prediction model for the individual prognosis or diagnosis (TRIPOD) tool was used to critically evaluate the quality of external validation reports for six models (CU-HCC, GAG-HCC, PAGE-B, mPAGE-B, REACH-B, and mREACH-B). The area under the receiver operator characteristic curve (AUC) values was to estimate the pooled external validating performance based on meta-analysis. Subgroup analysis and metaregression were also performed to explore heterogeneity.

RESULTS

Our meta-analysis included 22 studies published between 2011 and 2020. The compliance of the included studies to TRIPOD ranged from 59% to 90% (median, 74%; interquartile range (IQR), 70%, 79%). The AUC values of the six models ranged from 0.715 to 0.778. In the antiviral therapy subgroups, the AUC values of mREACH-B, GAG-HCC, and mPAGE-B were 0.785, 0.760, and 0.778, respectively. In the cirrhosis subgroup, all models had poor discrimination performance (AUC < 0.7).

CONCLUSIONS

A full report of calibration and handling of missing values would contribute to a greater improvement in the quality of external validation reports for CHB-related HCC risk prediction. It was necessary to develop a specific HCC risk prediction model for patients with cirrhosis.

A Systematic Review and Narrative Synthesis of Health Economic Evaluations of Hepatocellular Carcinoma Screening Strategies

OBJECTIVES

Many economic evaluations of hepatocellular carcinoma (HCC) screenings have been conducted; however, these vary substantially with regards to screening strategies, patient group, and setting. This review aims to report the current knowledge of the cost-effectiveness of screening and describe the published data.

METHODS

We conducted a search of biomedical and health economic databases up to July 2020. We included full and partial health economic studies if they evaluated the costs or outcomes of HCC screening strategies.

RESULTS

The review included 43 studies. Due to significant heterogeneity in key aspects across the studies, a narrative synthesis was conducted. Most studies reported using ultrasound or alpha fetoprotein as screening strategies. Screening intervals were mostly annual or biannual. Incidence, diagnostic performance, and health state utility values were the most critical parameters affecting the cost-effectiveness of screening. The majority of studies reported HCC screening to be cost-effective, with the biannual ultrasound + alpha fetoprotein standing out as the most cost-effective strategy. However, few studies considered the utilization rate, and none considered the diagnostic performance of ultrasound in the context of central adiposity. Computed tomography and magnetic resonance imaging were also evaluated, but its cost-effectiveness was still controversial.

CONCLUSIONS

Although many studies suggested HCC screening was cost-effective, substantial limitations of the quality of these studies means the results should be interpreted with caution. Future modeling studies should consider the impact of central adiposity on the precision of ultrasound, real-world utilization rates and projections of increased HCC incidence.

Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature

PURPOSE

This study was conducted in order to investigate the value of magnetic resonance imaging (MRI)-based radiomics signatures for the preoperative prediction of hepatocellular carcinoma (HCC) grade.

METHODS

Data from 170 patients confirmed to have HCC by surgical pathology were divided into a training group (n = 125) and a test group (n = 45). The radiomics features of tumours based on both T1-weighted imaging (WI) and T2WI were extracted by using Matrix Laboratory (MATLAB), and radiomics signatures were generated using the least absolute shrinkage and selection operator (LASSO) logistic regression model. The predicted values of pathological HCC grades using radiomics signatures, clinical factors (including age, sex, tumour size, alpha fetoprotein (AFP) level, history of hepatitis B, hepatocirrhosis, portal vein tumour thrombosis, portal hypertension and pseudocapsule) and the combined models were assessed.

RESULTS

Radiomics signatures could successfully categorise high-grade and low-grade HCC cases (p < 0.05) in both the training and test datasets. Regarding the performances of clinical factors, radiomics signatures and the combined clinical and radiomics signature (from the combined T1WI and T2WI images) models for HCC grading prediction, the areas under the curve (AUCs) were 0.600, 0.742 and 0.800 in the test datasets, respectively. Both the AFP level and radiomics signature were independent predictors of HCC grade (p < 0.05).

CONCLUSIONS

Radiomics signatures may be important for discriminating high-grade and low-grade HCC cases. The combination of the radiomics signatures with clinical factors may be helpful for the preoperative prediction of HCC grade.

KEY POINTS

• The radiomics signature based on non-contrast-enhanced MR images was significantly associated with the pathological grade of HCC. • The radiomics signatures based on T1WI or T2WI images performed similarly at predicting the pathological grade of HCC. • Combining the radiomics signature and clinical factors (including age, sex, tumour size, AFP level, history of hepatitis B, hepatocirrhosis, portal vein tumour thrombosis, portal hypertension and pseudocapsule) may be helpful for the preoperative prediction of HCC grade.