Predictive factors of contrast-enhanced ultrasonography for the response to transarterial chemoembolization in hepatocellular carcinoma

Background/AimsThe predictive role of contrast-enhanced ultrasonography (CEUS) before performing transarterial chemoembolization (TACE) has not been determined. We assessed the possible predictive factors of CEUS for the response to TACE.MethodsSeventeen patients with 18 hepatocellular carcinoma (HCC) underwent TACE. All of the tumors were studied with CEUS before TACE using a second-generation ultrasound contrast agent (SonoVue®, Bracco, Milan, Italy). The tumor response to TACE was classified with a score between 1 and 4 according to the remaining enhancing-tumor percentage based on modified response evaluation criteria in solid tumors (mRECIST): 1, enhancing tumor <25%; 2, 25%≤enhancing tumor<50%; 3, 50%≤enhancing tumor<75%; and 4, enhancing tumor≥75%). A score of 1 was defined as a "good response" to TACE. The predictive factors for the response to TACE were evaluated during CEUS based on the maximum tumor diameter, initial arterial enhancing time, arterial enhancing duration, intensity of arterial enhancement, presence of a hypoenhanced pattern, and the feeding artery to the tumor.ResultsThe median tumor size was 3.1 cm. The distribution of tumor response scores after TACE in all tumors was as follows: 1, n=11; 2, n=4; 3, n=2; and 4, n=1. Fifteen tumors showed feeding arteries. The presence of a feeding artery and the tumor size (≤5 cm) were the predictive factors for a good response (P=0.043 and P=0.047, respectively).ConclusionsThe presence of a feeding artery and a tumor size of less than 5 cm were the predictive factors for a good response of HCC to TACE on CEUS

Single nucleotide polymorphisms in bone turnover-related genes in Koreans: ethnic differences in linkage disequilibrium and haplotype

<p>Abstract</p> <p>Background</p> <p>Osteoporosis is defined as the loss of bone mineral density that leads to bone fragility with aging. Population-based case-control studies have identified polymorphisms in many candidate genes that have been associated with bone mass maintenance or osteoporotic fracture. To investigate single nucleotide polymorphisms (SNPs) that are associated with osteoporosis, we examined the genetic variation among Koreans by analyzing 81 genes according to their function in bone formation and resorption during bone remodeling.</p> <p>Methods</p> <p>We resequenced all the exons, splice junctions and promoter regions of candidate osteoporosis genes using 24 unrelated Korean individuals. Using the common SNPs from our study and the HapMap database, a statistical analysis of deviation in heterozygosity depicted.</p> <p>Results</p> <p>We identified 942 variants, including 888 SNPs, 43 insertion/deletion polymorphisms, and 11 microsatellite markers. Of the SNPs, 557 (63%) had been previously identified and 331 (37%) were newly discovered in the Korean population. When compared SNPs in the Korean population with those in HapMap database, 1% (or less) of SNPs in the Japanese and Chinese subpopulations and 20% of those in Caucasian and African subpopulations were significantly differentiated from the Hardy-Weinberg expectations. In addition, an analysis of the genetic diversity showed that there were no significant differences among Korean, Han Chinese and Japanese populations, but African and Caucasian populations were significantly differentiated in selected genes. Nevertheless, in the detailed analysis of genetic properties, the LD and Haplotype block patterns among the five sub-populations were substantially different from one another.</p> <p>Conclusion</p> <p>Through the resequencing of 81 osteoporosis candidate genes, 118 unknown SNPs with a minor allele frequency (MAF) > 0.05 were discovered in the Korean population. In addition, using the common SNPs between our study and HapMap, an analysis of genetic diversity and deviation in heterozygosity was performed and the polymorphisms of the above genes among the five populations were substantially differentiated from one another. Further studies of osteoporosis could utilize the polymorphisms identified in our data since they may have important implications for the selection of highly informative SNPs for future association studies.</p

The Korea Cancer Big Data Platform (K-CBP) for Cancer Research

Data warehousing is the most important technology to address recent advances in precision medicine. However, a generic clinical data warehouse does not address unstructured and insufficient data. In precision medicine, it is essential to develop a platform that can collect and utilize data. Data were collected from electronic medical records, genomic sequences, tumor biopsy specimens, and national cancer control initiative databases in the National Cancer Center (NCC), Korea. Data were de-identified and stored in a safe and independent space. Unstructured clinical data were standardized and incorporated into cancer registries and linked to cancer genome sequences and tumor biopsy specimens. Finally, national cancer control initiative data from the public domain were independently organized and linked to cancer registries. We constructed a system for integrating and providing various cancer data called the Korea Cancer Big Data Platform (K-CBP). Although the K-CBP could be used for cancer research, the legal and regulatory aspects of data distribution and usage need to be addressed first. Nonetheless, the system will continue collecting data from cancer-related resources that will hopefully facilitate precision-based research

Distribution of the SNPs identified in the 81 candidate osteoporosis genes

<p><b>Copyright information:</b></p><p>Taken from "Single nucleotide polymorphisms in bone turnover-related genes in Koreans: ethnic differences in linkage disequilibrium and haplotype"</p><p>http://www.biomedcentral.com/1471-2350/8/70</p><p>BMC Medical Genetics 2007;8():70-70.</p><p>Published online 26 Nov 2007</p><p>PMCID:PMC2222243.</p><p></p> (A) Classification of the SNPs into minor allele frequency (MAF) classes. (B) Number of known and unknown SNPs. (C) Distribution of SNPs according to location or type. The percentages in (A), (B), and (C) refer to the percentage of SNPs within each MAF class in the given categories