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Health related quality of life (HRQOL) is increasingly recognized as an important clinical parameter and research endpoint in patients with hepatocellular carcinoma (HCC). HRQOL in HCC patients is multifaceted and affected by medical factor which encompasses HCC and its complications, oncological and palliative treatment for HCC, underlying liver disease, as well as the psychological, social or spiritual reaction to the disease. Many patients presented late with advanced disease and limited survival, plagued with multiple symptoms, rendering QOL a very important aspect in their general well being. Various instruments have been developed and validated to measure and report HRQOL in HCC patients, these included general HRQOL instruments, e.g ., Short form (SF)-36, SF-12, EuroQoL-5D, World Health Organization Quality of Life Assessment 100 (WHOQOL-100), World Health Organization Quality of Life Assessment abbreviated version; general cancer HRQOL instruments, e.g ., the European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30, Functional Assessment of Cancer Therapy (FACT)-General, Spitzer Quality of Life Index; and liver-cancer specific HRQOL instruments, e.g ., EORTC QLQ-HCC18, FACT-Hepatobiliary (FACT-Hep), FACT-Hep Symptom Index, Trial Outcome Index. Important utilization of HRQOL in HCC patients included description of symptomatology and HRQOL of patients, treatment endpoint in clinical trial, prognostication of survival, benchmarking of palliative care service and health care valuation. In this review, difficulties regarding the use of HRQOL data in research and clinical practice, including choosing a suitable instrument, problems of missing data, data interpretation, analysis and presentation are examined. Potential solutions are also discussed., Competing Interests: Conflict-of-interest statement: No potential conflicts of interest. No financial support.

Purpose: Most patients with hepatocellular carcinoma (HCC) have associated chronic liver disease, the severity of which is currently assessed by the Child-Pugh (C-P) grade. In this international collaboration, we identify objective measures of liver function/dysfunction that independently influence survival in patients with HCC and then combine these into a model that could be compared with the conventional C-P grade., Patients and Methods: We developed a simple model to assess liver function, based on 1,313 patients with HCC of all stages from Japan, that involved only serum bilirubin and albumin levels. We then tested the model using similar cohorts from other geographical regions (n = 5,097) and other clinical situations (patients undergoing resection [n = 525] or sorafenib treatment for advanced HCC [n = 1,132]). The specificity of the model for liver (dys)function was tested in patients with chronic liver disease but without HCC (n = 501)., Results: The model, the Albumin-Bilirubin (ALBI) grade, performed at least as well as the C-P grade in all geographic regions. The majority of patients with HCC had C-P grade A disease at presentation, and within this C-P grade, ALBI revealed two classes with clearly different prognoses. Its utility in patients with chronic liver disease alone supported the contention that the ALBI grade was indeed an index of liver (dys)function., Conclusion: The ALBI grade offers a simple, evidence-based, objective, and discriminatory method of assessing liver function in HCC that has been extensively tested in an international setting. This new model eliminates the need for subjective variables such as ascites and encephalopathy, a requirement in the conventional C-P grade., (© 2014 by American Society of Clinical Oncology.)

Aim: To investigate the age differences in the risk factors, clinicopathological characteristics and patterns of treatment of female breast cancer patients., Methods: Seven thousand one hundred and fifty-two women with primary breast cancer from the Hong Kong Breast Cancer Registry were recruited after receiving patients' consent, they were asked to complete standardized questionnaires which captured their sociodemographic characteristics and risk factors associated with breast cancer development. Among them, clinicopathological data and patterns of treatment were further collected from medical records of 5523 patients with invasive breast cancers. Patients were divided into two groups according to the age at diagnosis: younger (< 40 years old) vs older patients (≥ 40 years old) for subsequent analyses., Results: Analysis on the sociodemographic characteristics and exposure to risk factors were performed on 7152 women with primary breast cancer and the results revealed that younger patients were more likely to have unhealthy lifestyles; these include a lack of exercise (85.4% vs 73.2%, P < 0.001), having high stress in life (46.1% vs 35.5%, P < 0.001), having dairy/meat-rich diets (20.2% vs 12.9%, P < 0.001), having alcohol drinking habit (7.7% vs 5.2%, P = 0.002). Younger patients were also more likely to have hormone-related risk factors including nulliparity (43.3% vs 17.8%, P < 0.001) and an early age at menarche (20.7% vs 13.2%, P < 0.001). Analyses on clinicopathological characteristics and patterns of treatment were performed on 5523 women diagnosed with invasive breast cancer. The invasive tumours in younger patients showed more aggressive pathological features such as having a higher percentage of grade 3 histology (45.7% vs 36.5%, P < 0.001), having a higher proportion of tumours with lymphovascular invasion (39.6% vs 33.2%, P = 0.003), and having multifocal disease (15.7% vs 10.3%, P < 0.001); they received different patterns of treatment than their older counterparts., Conclusion: Younger patients in Hong Kong are more likely to encounter risk factors associated with breast cancer development and have more aggressive tumours than their older counterparts.

A growing number of multi-targeted tyrosine kinase inhibitor (TKI) has undergone testing for hepatocellular carcinoma (HCC). Unfortunately, this enthusiasm has recently been discouraged by a number of negative phase III studies on several anti-angiogenic TKIs in HCC. Several postulations have been made to account for this phenomenon, namely the plateau effects of anti-angiogenesis approach, the heterogeneity of HCC in terms of background hepatitis/cirrhosis and tumor biology, as well as the way how clinical trials are designed. Regardless of the underlying reasons, these results suggested that alternative strategies are necessary to further develop systemic therapy for HCC. Several new strategies are currently evaluated: for examples, molecular agents with activities against targets other than vascular endothelial growth factor receptor are being evaluated in on-going clinical trials. In addition, different approaches of targeted agents in combination with various treatment modalities, such as concurrently with another molecular agent, cytotoxic chemotherapy or transarterial chemoembolization, are being developed. This review aims to give a summary on the results of recently released clinical trials on TKIs, followed by discussion on some of the potential novel agents and combinational approaches. Future directions for testing innovative systemic agents for HCC will also be discussed.

Microarray techniques using cDNA array and comparative genomic hybridization (CGH) have been developed for several discovery applications. They are frequently applied for the prediction and diagnosis of cancer in recent years. Many studies have shown that integrating genomic data from different sources may increase the reliability of gene expression analysis results in understanding cancer progression. Therefore, developing a good prognostic model dealing simultaneously with different types of dataset is important. The challenge with these types of data is high background noise. We describe an analytical two-stage framework with a multi-parallel data analysis method named wavelet-based generalized singular value decomposition and shaving method (WGSVD-shaving). This method is proposed for de-noising and dimension-reduction during early stage prognosis modeling. We also applied a supervised gene clustering technique with penalized logistic regression with Cox-model on an integrated data. We show the accuracy of the method using a simulated dataset with a case study on Hepatocelluar Carcinoma (HCC) cDNA and CGH data. The method shows improved results from GSVD-shaving and has application in the discovery of candidate genes associated with cancer.