BACKGROUND
An ideal staging system for hepatocellular carcinoma (HCC) should rely on the hepatic reserve function and tumor burden. With the improvement in diagnostic and treatment strategies for HCC, in addition to recent treatment of viral hepatitis, finding a suitable assessment tool for hepatic reserve has become mandatory.
AIM
To validate a recently proposed modified albumin-bilirubin-TNM (mALBI-T) grade as a prognostic model for patients with HCC in Egypt.
METHODS
For patients diagnosed with HCC, Child-Turcotte-Pugh (CTP) score, Barcelona Clinic Liver Cancer (BCLC) stage, albumin-bilirubin (ALBI), plateltet-albumin–bilirubin (PALBI), ALBI-based BCLC, ALBI-T and mALBI-T grades were estimated. Patients were followed from time of diagnosis to date of death or date of data collection if they remained alive. Overall survival and received treatments were determined. Survival data were analyzed.
RESULTS
A total of 1910 patients were included (mean age, 57 years; 1575 males). At presentation, 50.6% had CTP A, 36.1% had CTP B and 13.4 % had CTP C; 12% had ALBI grade 1, 62.3% had ALBI grade 2 and 24.7% had ALBI grade 3. Overall median survival was 13 mo; survival was better in patients with ALBI 1 than in those with ALBI 2 and 3 (28.6
vs
14 and 5.8 mo, respectively,
P
< 0.001). Patients with ALBI-T grades 0 and 1 had better survival than those with ALBI-T grades 2, 3, 4 and 5 (
P
< 0.001). The modified ALBI-T showed better stratification and significant improvement in prediction of survival.
CONCLUSION
ALBI-T grade is a superior prognostic tool that selects patients with HCC who have better liver reservoir and tumor stage. mALBI-T is a better prognostic model in patients with HCC.
Adding AFP and ascites to the BCLC staging classification can improve prognosis prediction for early and intermediate stages of hepatocellular carcinoma.
Hepatocellular carcinoma (HCC) is the fifth most common malignancy, the third most common cause of cancer death, and the most common primary liver cancer. Overall, there is a need for more reliable biomarkers for HCC, as those currently available lack sensitivity and specificity. For example, the current gold-standard biomarker, serum alpha-fetoprotein, has a sensitivity of roughly only 70%. Cancer cells have different characteristic metabolic signatures in biofluids, compared to healthy cells; therefore, metabolite analysis in blood or urine should lead to the detection of suitable candidates for the detection of HCC. With the advent of metabonomics, this has increased the potential for new biomarker discovery. In this article, we look at approaches used to identify biomarkers of HCC using proton nuclear magnetic resonance (1H-NMR) spectroscopy of urine samples. The various multivariate statistical analysis techniques used are explained, and the process of biomarker identification is discussed, with a view to simplifying the knowledge base for the average clinician.
This review aims to develop an appropriate review tool for systematically collating metabolites that are dysregulated in disease and applies the method to identify novel diagnostic biomarkers for hepatocellular carcinoma (HCC). Studies that analysed metabolites in blood or urine samples where HCC was compared with comparison groups (healthy, pre-cirrhotic liver disease, cirrhosis) were eligible. Tumour tissue was included to help differentiate primary and secondary biomarkers. Searches were conducted on Medline and EMBASE. A bespoke 'risk-of-bias' tool for metabolomic studies was developed adjusting for analytical quality. Discriminant metabolites for each sample type were ranked using a weighted score accounting for the direction and extent of change and the risk of bias of the reporting publication. A total of 84 eligible studies were included in the review (54 blood, 9 urine and 15 tissue), with six studying multiple sample types. High-ranking metabolites, based on their weighted score, comprised energy metabolites, bile acids, acylcarnitines and lysophosphocholines. This new review tool addresses an unmet need for incorporating quality of study design and analysis to overcome the gaps in standardisation of reporting of metabolomic data. Validation studies, standardised study designs and publications meeting minimal reporting standards are crucial for advancing the field beyond exploratory studies.
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