Multidisciplinary Approach in Hepatocellular Carcinoma in a Level II Hospital: The First Decade of Hospital Universitario Fundacion Alcorcon.

BACKGROUND/AIMS: Mean survival in hepatocellular carcinoma remains low. Many efforts have been done during the last years through screening, diagnosis and treatment to improve the results. The aim of this work is to present the experience of our hospital multidisciplinary group during the first decade of this century. METHODOLOGY: The patients with hepatocellullar carcinoma presented at the multidisciplinary meeting from 1999 to 2009 were prospectively studied. According to the tumor and functional status they were treated through the current available guidelines by transplant, partial hepatectomy, local/regional procedures, systemic or symptomatic treatment. RESULTS: One hundred and forty two patients were studied. Median tumor size was 3 cm. A single tumor was diagnosed in 64.8% of the patients. Eighteen patients had liver resection (6 transplantation and 12 with partial resection), 53 tumors were not treated due to advanced stage or liver dysfunction, and in the remaining patients radiofrequency, ethanol or embolization treatments were used, single or combined. CONCLUSIONS: a multidisciplinary approach of hepatocellular carcinoma in a second level hospital with trained professionals permits a diagnosis in early tumoral and functional stages in the majority of patients, and a variety of possible treatments with adequate survival outcomes.

Hepatitis C Virus positivity prediction from serum samples using NIRS and L1-penalized classification.

AIMS: The hepatitis C virus (HCV) has developed a strategy to coexist with its host resulting in varying degrees of tissue and cell damage, which generate different pathological phenotypes, such as varying degrees of fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). However, there is no integrated information that can predict the evolutionary course of the infection. We propose to combine Near-infrared spectroscopy (NIRS) and machine learning techniques to provide a predictive model. In this work, we propose to discriminate HCV positivity in biobank patient serum samples. METHODS: 126 serum samples from 38 HCV patients in different stages of the disease were obtained from the Biobank of Hospital Universitario Fundación Alcorcon. NIRS spectrum was captured by a FT-NIRS Spectrum 100 (Perkin Elmer) device in reflectance mode. For each patient, the HCV positivity was identified (PCR) and labeled as detectable =1 and undetectable =0. We propose an L1-penalized logistic regression model to classify each spectrum as positive (1) or negative (0) for HCV presence (x). The regularization parameter is selected using 5- fold cross-validation. The penalized model will induce sparsity in the solution so that only a few relevant wavelengths will be different from zero. RESULTS: L1-penalized logistic regression model provided 167 wavelengths different from zero. The accuracy on an independent test set was 0.78. CONCLUSIONS: We present a straightforward promising approach to detect HCV positivity from patient serum samples combining NIRS and machine learning techniques. This result is encouraging to predict HCV progression, among other applications. Clinical relevance- We presented a simple while promising approach to use machine learning and NIRS to analyze viral presence on sample serums.