Detection Of Liver Cancer Research Articles

Abstract 888: Development of a urine DNA based marker panel for early detection of liver cancer

Abstract Hepatocellular carcinoma (HCC) or liver cancer is an aggressive disease and one of the fastest growing cancers in incidence in the United States. The 5 year survival rate drops dramatically from a poor 26% in early stages to a mere 2% in later stages of the disease. Currently, the only available biomarker for early detection is serum alpha-fetoprotein which can identify only 40-60% of cases. We aim to improve the poor disease prognosis by developing a screening test capable of detecting liver cancer in its earlier stages. Since cancer is a disease of the genome and epigenome, if we can detect these underlying genetic mutations and epigenetic modifications in the periphery, we should be able to effectively detect cancer early. We have previously shown that urine contains fragmented, cell-free, cancer related DNA, both mutated and methylated, derived from the circulation of cancer patients. We have also demonstrated that the concentration of tumor-derived DNA in plasma and in urine is similar in patients with tumors. Hence, we propose the use of a panel of HCC-associated genetic and epigenetic methylation DNA biomarkers to develop a sensitive, noninvasive urine screening test for HCC. In order to detect these markers in the circulation derived urine DNA, we developed short amplicon (∼50 bp) PCR based assays targeting HCC-associated mutations in TP53, CTNNB1, hTERT genes and HCC associated methylation in GSTP1, RASSF1A and CDKN2A. Urine DNA isolated from hepatitis, cirrhosis and HCC patients were tested in each of the assays and analyzed. Panel performance parameters such as sensitivity, specificity and area under the receiver operating curve were calculated for individual markers and then as a group in order to evaluate their ability to distinguish HCC patients from hepatitis and cirrhosis subjects. The potential of this urine test in the early detection of HCC is discussed. Citation Format: Sitong Chen, Surbhi Jain, Selena Lin, Ying-Hsiu Su, Wei Song. Development of a urine DNA based marker panel for early detection of liver cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 888. doi:10.1158/1538-7445.AM2014-888

Identification of copy number variation-driven genes for liver cancer via bioinformatics analysis.

To screen out copy number variation (CNV)-driven differentially expressed genes (DEGs) in liver cancer and advance our understanding of the pathogenesis, an integrated analysis of liver cancer-related CNV data from The Cancer Genome Atlas (TCGA) and gene expression data from EBI Array Express database were performed. The DEGs were identified by package limma based on the cut-off of |log2 (fold-change)|>0.585 and adjusted p-value<0.05. Using hg19 annotation information provided by UCSC, liver cancer-related CNVs were then screened out. TF-target gene interactions were also predicted with information from UCSC using DAVID online tools. As a result, 25 CNV-driven genes were obtained, including tripartite motif containing 28 (TRIM28) and RanBP-type and C3HC4-type zinc finger containing 1 (RBCK1). In the transcriptional regulatory network, 8 known cancer-related transcription factors (TFs) interacted with 21 CNV-driven genes, suggesting that the other 8 TFs may be involved in liver cancer. These genes may be potential biomarkers for early detection and prevention of liver cancer. These findings may improve our knowledge of the pathogenesis of liver cancer. Nevertheless, further experiments are still needed to confirm our findings.

A Hybrid Approach to Diagnosis of Hepatic Tumors in Computed Tomography Images

Liver cancer is one of the most popular cancer diseases and causes a large amount of death every year, can be reduced by early detection and diagnosis. Computer-aided liver analysis can help in the early detection and diagnosis of liver cancer. In this paper, enhancement and segmentation process is applied to increase the computation and focus on liver parenchyma. This parenchyma also segmented using Watershed and Region Growing algorithms to extract liver tumors. These tumors will be analyzed and characterized to distinguish between hemangioma (benign) and hepatocellular (malignant) tumors using Local Binary Pattern (LBP), Gray Level Co-occurrence matrix (GLCM), Fractal Dimension (FD) and feature fusion technique is applied to maximize and enhance the performance of the classifier rate. The authors review different methods for liver segmentation and abnormality classification. An attempt was made to combine the individual scores from different techniques in order to compensate their individual weaknesses and to preserve their strength. The authors present and exhaustively evaluate algorithms using computer vision techniques. The experimental results based on confusion matrix and kappa coefficient show that the higher accuracy is obtained of automatic agreement classification and suggest that the developed CAD system has great potential and promise in the automatic diagnosis of both benign and malignant tumors of liver.

APPLICATION OF GENETIC ALGORITHM FOR LIVER CANCER DETECTION

Human life is the most precious and valuable gift of God. The internal structure of human body is complex and weird. When there is any medical complication or problem it gets very important for doctor to analyze it correctly. X ray, CT and MRI images help doctor in this analysis. Cancer is a prominent reason for deaths in India. The cancer can be detected in human body through MRI images but it gets difficult to correctly analyze it due to the reason of having almost same intensity of the images of other body parts. A better system has to be find for the segmentation of liver from MRI images so that it can be identified clearly from the adjacent organs which have similar intensity. Also livers geometry is very complex due to its high anatomical variability both in health and disease. In this paper we will present different techniques for segmentation and will design a method for liver segmentation and cancer detection. Keyword: Liver Segmentation, Genetic algorithm, MRI, Threshold based approaches. CBIR, Contour, Histogram

Ultrasensitive detection of human liver hepatocellular carcinoma cells using a label-free aptasensor.

Liver cancer is one of the most common cancers in the world and has no effective cure, especially in later stages. The development of a tangible protocol for early diagnosis of this disease remains a major challenge. In the present manuscript, an aptamer-based, label-free electrochemical biosensor for the sensitive detection of HepG2, a hepatocellular carcinoma cell line, is described. The target cells are captured in a sandwich architecture using TLS11a aptamer covalently attached to a gold surface and a secondary TLS11a aptamer. The application of TLS11a aptamer as a recognition layer resulted in a sensor with high affinity for HepG2 cancer cells in comparison with control cancer cells of human prostate, breast, and colon tumors. The aptasensor delivered a wide linear dynamic range over 1 × 10(2) to 1 × 10(6) cells/mL, with a detection limit of 2 cells/mL. This protocol provides a precise method for sensitive detection of liver cancer with significant advantages in terms of simplicity, low cost, and stability.

Diagnostic Thresholds with Three Ordinal Groups

In practice, there exist many disease processes with three ordinal disease classes; for example, in the detection of Alzheimer’s disease (AD) a patient can be classified as healthy (disease-free stage), mild cognitive impairment (early disease stage), or AD (full disease stage). The treatment interventions and effectiveness of such disease processes will depend on the disease stage. Therefore, it is important to develop diagnostic tests with the ability to discriminate between the three disease stages. Measuring the overall ability of diagnostic tests to discriminate between the three classes has been discussed extensively in the literature. However, there has been little proposed on how to select clinically meaningful thresholds for such diagnostic tests, except for a method based on the generalized Youden index by Nakas et al. (2010). In this article, we propose two new criteria for selecting diagnostic thresholds in the three-class setting. The numerical study demonstrated that the proposed methods may provide thresholds with less variability and more balance among the correct classification rates for the three stages. The proposed methods are applied to two real examples: the clinical diagnosis of AD from the Washington University Alzheimer’s Disease Research Center and the detection of liver cancer (LC) using protein segments.

Metabolomic Analysis of Liver Tissue from the VX2 Rabbit Model of Secondary Liver Tumors.

Purpose. The incidence of liver neoplasms is rising in USA. The purpose of this study was to determine metabolic profiles of liver tissue during early cancer development. Methods. We used the rabbit VX2 model of liver tumors (LT) and a control group consisting of sham animals implanted with Gelfoam into their livers (LG). After two weeks from implantation, liver tissue from lobes with and without tumor was obtained from experimental animals (LT+/LT−) as well as liver tissue from controls (LG+/LG−). Peaks obtained by Gas Chromatography-Mass Spectrometry were subjected to identification. 56 metabolites were identified and their profiles compared between groups using principal component analysis (PCA) and a mixed-effect two-way ANOVA model. Results. Animals recovered from surgery uneventfully. Analyses identified a metabolite profile that significantly differs in experimental conditions after controlling the False Discovery Rate (FDR). 16 metabolites concentrations differed significantly when comparing samples from (LT+/LT−) to samples from (LG+/LG−) livers. A significant difference was also shown in 20 metabolites when comparing samples from (LT+) liver lobes to samples from (LT−) liver lobes. Conclusion. Normal liver tissue harboring malignancy had a distinct metabolic signature. The role of metabolic profiles on liver biopsies for the detection of early liver cancer remains to be determined.

Indocyanine green fluorescence imaging in the surgical management of liver cancers: Current facts and future implications

Imaging detection of liver cancers and identification of the bile ducts during surgery, based on the fluorescence properties of indocyanine green, has recently been developed in liver surgery. The principle of this imaging technique relies on the intravenous administration of indocyanine green before surgery and the illumination of the surface of the liver by an infrared camera that simultaneously induces and collects the fluorescence. Detection by fluorescence is based on the contrast between the (fluorescent) tumoral or peri-tumoral tissues and the healthy (non-fluorescent) liver. Results suggest that indocyanine green fluorescence imaging is capable of identification of new liver cancers and enables the characterization of known hepatic lesions in real time during liver resection. The purpose of this paper is to present the fundamental principles of fluorescence imaging detection, to describe successively the practical and technical aspects of its use and the appearance of hepatic lesions in fluorescence, and to expose the diagnostic and therapeutic perspectives of this innovative imaging technique in liver surgery.

Cell-Free DNA for the Detection of Pancreatic, Liver and Upper Gastrointestinal Cancers: Has Progress Been Made?

Detecting alterations in blood cell-free DNA (cfDNA) is hoped to be a novel, noninvasive method for diagnosing, prognosing and monitoring cancer patients. Several studies have assessed the usefulness of measuring tumor-specific genetic and epigenetic changes of cfDNA, such as loss of heterozygosity, frequency of mutations, alterations of microsatellites and the methylation of genes in patient blood samples. However, few well-designed trials have been carried out to translate these findings effectively. In this review, we have assessed the clinical utility of cfDNA in pancreatic, liver and upper gastrointestinal malignancies.

Mn2+-doped silica nanoparticles for hepatocyte-targeted detection of liver cancer in T1-weighted MRI

With an aim to examine the possibility of developing a liver-specific MRI contrast agent that takes advantages of brightly enhanced MR images by Mn2+ whilst making up the limitations of the pre-developed contrast agent, the Mn2+-doped SiO2 nanoparticles (Mn–SiO2) were synthesized and their characteristics as MR contrast agents were investigated. The in vitro and in vivo investigations showed that Mn–SiO2 has unique MR contrast-enhancing characteristics that activate positive contrast enhancement in T1-weighted MR images only under low pH conditions by liberating Mn2+ ions from MR inactive nanoparticles. The administration of Mn–SiO2 to an orthotopic xenograft model of human hepatocellular carcinoma (HCC) resulted in a differentiation of enhancement periods between HCC and normal parenchyma tissues on T1-weighted MR images and consequently presented the duplicates of the highly contrast-enhanced liver image with an equal liver-to-HCC contrast ratio but opposite contrast. The Mn–SiO2-enhanced MR imaging therefore allowed for the repetitive detection of the HCC within a single MR imaging session, which can help us to achieve more reliable diagnosis and characterization of liver lesions than is possible with any currently used Mn2+-based contrast agent. In addition, the in vivo biodistribution study also supported the effectiveness of Mn–SiO2 nanoparticles as a liver-specific MRI contrast agent, which efficiently delivers and releases the T1-contrasting Mn2+ ions to targeted hepatocytes.

Water soluble fluorescence quantum dot probe labeling liver cancer cells

Water soluble quantum dots (QDs) have been prepared by hydrothermal method and characterized by ultraviolet irradiation, XRD, TEM, UV-Vis absorption spectrometer and fluorescence spectrometer. Then the QD-antibody-AFP probes (QD-Ab-AFP) were synthesized by chemical process and specifically labeled AFP antigen in PLC/PRF/5 liver cancer cells. The results showed that the QDs were cubic structure and had excellent optical properties. Moreover, the QD-Ab-AFP with good stability could specifically label liver cancer cells. This work provides strong foundation for further studying and developing new approach to detect liver cancer at early stage.

Predictive factors of risk of hepatocellular carcinoma in chronic hepatitis C

ObjectiveTo identify risk factors associated with the development of HCC and develop a score to identify high risk subgroups. MethodsWe conducted a follow-up study, with biannual ultrasound and alpha-fetoprotein screening, in an unselected cohort of patients with chronic hepatitis C referred for evaluation from the primary care. Results863 patients with chronic hepatitis C were followed for an average of 82months. 58 patients have developed HCC (7%). 34 (4%) patients were excluded from analysis due to detection of liver cancer at first evaluation. The demographic and clinical variables collected during the first 6months of evaluation were analyzed retrospectively. Cox proportional multivariate regression analysis identified four independent factors related with HCC risk; age, alpha-fetoprotein level, gammaglobulin level and platelet count below 150,000/ml. A risk score formula (HCC-4) was constructed which lets us identify patients with low (annual incidence of 0.05%), intermediate (annual incidence of 0.6%) and high (annual incidence 2.6%) risk of HCC development with an area under the curve of 0.802. ConclusionThe application of the score to the cohort let us identify a high-risk subgroup of patients with an annual HCC incidence of 2.6%, in which the screening would be cost-effective.

COMPARATIVE ANALYSIS OF EDGE BASED AND REGION BASED ACTIVE CONTOUR USING LEVEL SETS AND ITS APPLICATION ON CT IMAGES

Detection of Liver cancer from CT images is an exigent task due to the reason that cancer impression in CT images are of very low in contrast and have indistinguishable edges. Indistinguishable edges are edges in which foreground and background are almost same. Image segmentation is used to extract desired anatomical structure from image. Image segmentation is the process of dividing the image into multiple regions. These regions are sometimes called region of interest (ROI). These ROI’s are used as informative inputs to further image processing e.g. feature extraction, selection and ultimately the classification of a disease. Thus an effective image segmentation is utmost important in medical images. In case of indistinguishable edges most segmentation techniques fails to detect edges. Edge based and region based active contour methods are most prevalent for image segmentation. Edge based technique works using gradient function and ultimately stopping function to detect edges , while region based uses average information inside and outside regions to control evolution and termination at edges. In this paper comparative analysis of edge based and region based active contour using level sets is done. When applying these methods on the CT images with an impression of liver cancer, it has been found that the edge based contour able to locate the desired edges more accurately. The quantitative and qualitative results comparison between two techniques has also been done. Results shows that edge based methods performs comparatively better than region based active contour using level set if number of iterations are controlled properly.

Synthesis of the PLA-PEG-PLL gadolinium loaded nanoprobe targeted to vascular endothelial growth factor used for the molecular imaging of micro-hepatic carcinoma

Objective This study aims to synthesize a novel gadolinium loaded nanoprobe targeted to vascular endothelial growth factor (VEGF) and assess its clinical value for imaging micro hepatic carcinoma.Methods A carrier was made by the biocompatible polymer material polylactic acid-polyethylene glycol-poly-L-lysinenanoparticles (PLA-PEG-PLLs).The targeted nanoprobe was obtained with anti-VEGF antibody and gadolinium (Gd) bonding to the surface of the carrier.MRI in vitro determined the T1 relaxivity of the nanoprobe.A live cancer model enhanced MR scan was performed by injecting targeted nanoprobes into the tail vein of grafted H22 tumor mice.The enhanced characteristics of the subcutaneous tumors and micro-heatic carcinoma were then reviewed.Results The particle size of the VEGF-targeted PLA-PEG-PLL gadolinium loaded nanoprobe was 85.8±7.2 nm with a zeta potential of 21.63±2.4 mV.The R1 relaxivity of the targeted nanoprobe was 18.394 mmol/s at 3.0 T when its gadolinium concentration was 8.0 μmol/ml.The enhanced MR scan using targeted probes showed that the big and micro-subcutaneous cancer exhibited a specifically delayed enhancement with an enhanced peak value at 2 or 3 hours,rather than the enhancement of the tumor using the nontargeted nanoparticles.Conclusion In conclusion,the VEGF targeted PLA-PEG-PLL gadolinium loaded nanoprobe was synthesized successfully,showed a high relaxivity,achieved targeted imaging of the micro-hepatic carcinoma,and exhibits a promising potential in the detection of this liver cancer. Key words: Nanoprobe; Vascular endothelial growth factor; Micro-hepatic carcinoma; Targeted imaging; Gadolinum

An automatic computer-aided diagnosis system for liver tumours on computed tomography images

Liver cancer, one of the more common cancer diseases that cause a large number of deaths every year, can be reduced by early detection and diagnosis. Computer-Aided Diagnosis (CAD) can play a key role in the early detection and diagnosis of liver cancer. This paper develops a novel computer-aided diagnosis system focussing on the discriminating power of statistical texture descriptors in characterizing hepatocellular (malignant) from hemangioma (benign) liver tumours. The CAD system consists of three stages: (i) automatic tumour segmentation, (ii) texture feature extraction and (iii) tumour characterization using a classifier. Specifically, four features sets, the original gray level; co-occurrence of gray level; wavelet coefficient statistics and contourlet coefficient statistics are extracted from the tumour region of interest. A probabilistic neural network classifier is used to investigate the ability of each feature set in differentiating malignant from benign tissues. The performance of the CAD system evaluated using a database of images indicates that the highest accuracy achieved is 96.7% and the highest sensitivity and specificity are 97.3% and 96%, respectively that had been obtained with the contourlet coefficient co-occurrence features. The experimental results suggest that the developed CAD system has great potential and promise in the automatic diagnosis of both benign and malignant tumours of liver.

Near‐infrared surface‐enhanced Raman spectroscopy (NIR‐SERS) studies on oxyheamoglobin (OxyHb) of liver cancer based on PVA‐Ag nanofilm

A near‐infrared surface‐enhanced Raman spectroscopy (NIR‐SERS) method was employed for oxyheamoglobin (OxyHb) detection to develop a simple blood test for liver cancer detection. Polyvinyl alcohol protected silver nanofilm (PVA‐Ag nanofilm) used as the NIR‐SERS active substrate to enhance the Raman scattering signals of OxyHb. High quality NIR‐SERS spectrum from OxyHb adsorbed on PVA‐Ag nanofilm can be obtained within 16 s using a portable Raman spectrometer. NIR‐SERS measurements were performed on OxyHb samples of healthy volunteers (control subjects, n = 30), patients (n = 40) with confirmed liver cancer (stage I, II and III) and the liver cancer patients after surgery (n = 30). Meanwhile, the tentative assignments of the Raman bands in the measured NIR‐SERS spectra were performed, and the results suggested cancer specific changes on molecule level, including a decrease in the relative concentrations and the percentage of aromatic amino acids of OxyHb, changes of the vibration modes of the CaHm group and pyrrole ring of OxyHb of liver cancer patients. In this paper, principal component analysis (PCA) combined with independent sample T test analysis of the measured NIR‐SERS spectra separated the spectral features of the two groups into two distinct clusters with the sensitivity of 95.0% and the specificity of 85.7%. Meanwhile, the recovery situations of the liver cancer patients after surgery were also assessed using the method of discriminant analysis‐predicting group membership based on PCA. The results show that 26.7% surgeried liver cancer patients were distinguished as the normal subjects and 63.3% were distinguished into the cancer. Our study demonstrated great potentials for developing NIR‐SERS OxyHb analysis into a novel clinical tool for non‐invasive detection of liver cancers. Copyright © 2013 John Wiley &amp; Sons, Ltd.

Elevated expression of complement C3 protein in chemically induced hepatotumorogenesis in Wistar rats: A correlative proteomics and histopathological study

Liver cancer remains the leading cause of cancer-related mortality worldwide. Early detection of liver cancer is problematic due to the lack of a marker with high diagnosis sensitivity and specificity. The present study was designed to determine the differently expressed proteins at early stage in the serum of animals with liver cancer vis-à-vis controls and figure out the function of the proteins. One-dimensional electrophoresis (1D), two-dimensional electrophoresis (2DE) and liquid chromatography mass spectrometry (LC-MS/MS) were used to screen the serum proteins of liver cancer induced in animals by diethyl nitrosamine (DEN)+2-acetyl amino fluorine (2-AAF). From optimized 2DE image and computer assisted PD Quest analysis were found to be differentially expressed spots when the serum from normal and treated animals were compared. Among these, one spot was selected whose expression level was higher in DEN+2-AAF treated animal sera than in adjacent normal animal sera. The target spot was excised from the 2D gel of liver cancer sera and the peptide mass fingerprinting as obtained LC-MS/MS analysis after digesting the chosen protein spot. This was identified to be complement C3 protein. The changes in complement C3 expression level were validated by Western blot analysis. We reported that the changes in complement C3 concentration start at very early stage of tumorogenesis. The fully grown tumors were developed at 120 days and hepatotumorogenesis was confirmed by histopathological examination. This protein may therefore represent a powerful tool in search for candidate biomarkers for HCC.

Detection of mouse liver cancer via a parallel iterative shrinkage method in hybrid optical/microcomputed tomography imaging

Liver cancer is one of the most common malignant tumors worldwide. In order to enable the noninvasive detection of small liver tumors in mice, we present a parallel iterative shrinkage (PIS) algorithm for dual-modality tomography. It takes advantage of microcomputed tomography and multiview bioluminescence imaging, providing anatomical structure and bioluminescence intensity information to reconstruct the size and location of tumors. By incorporating prior knowledge of signal sparsity, we associate some mathematical strategies including specific smooth convex approximation, an iterative shrinkage operator, and affine subspace with the PIS method, which guarantees the accuracy, efficiency, and reliability for three-dimensional reconstruction. Then an in vivo experiment on the bead-implanted mouse has been performed to validate the feasibility of this method. The findings indicate that a tiny lesion less than 3 mm in diameter can be localized with a position bias no more than 1 mm; the computational efficiency is one to three orders of magnitude faster than the existing algorithms; this approach is robust to the different regularization parameters and the lp norms. Finally, we have applied this algorithm to another in vivo experiment on an HCCLM3 orthotopic xenograft mouse model, which suggests the PIS method holds the promise for practical applications of whole-body cancer detection.

Survey of the serum hepatocellular carcinoma marker Golgi glycoprotein (GP73) among Qidong residents and correlation analysis to outcome at two-year follow-up

To survey the levels of Golgi glycoprotein (GP73), a hepatocellular carcinoma (HCC) marker, in residents of Qidong and determine the correlation of detected GP73 concentration ranges with outcome at two-year follow-up. A total of 12,378 individuals (age range: 35-69 years old) from Qidong were enrolled in the study. All participants were tested for hepatitis B virus (HBV) by detecting hepatitis B surface antigen (HBsAg) in serum. One-tenth of the participants were assigned to a stratified-random sample group (those with identification numbers ending with "0") to represent a "subgroup of the natural population" (HBsAgPop, n = 1227). All HBsAg carriers were stratified as a "subgroup of positivity" (HBsAgPve, n = 1025). One-tenth of all HBsAg-negative individuals were assigned to a stratified-random sample group to represent a "subgroup of negativity" (HBsAgNve, n = 1132). Enzyme-linked immunoassay was used to measure the serum GP73 and alpha-fetoprotein (AFP) levels; the distribution, medians (50th percentile), and 95th percentiles of GP73 were determined for the three subgroups. A two-year follow-up was carried out to observe the differential incidence of HCC between the HBsAgPve and HBsAgNve subgroups. A positively skewed distribution of the GP73 values was observed for all three subgroups. The medians for HBsAgPve, HBsAgNve, and HBsAgPop were 67 mug/L, 54 mug/L, and 55 mug/L; the 95th percentiles were 174 mug/L, 108 mug/L, and 114 mug/L, respectively. The AFP positivity rates were 7.23% (37/512) for carriers whose GP73 values were above the median level and 0.78% (4/513) for carriers with GP73 values below the median level, with a highly significant difference between the two (P less than 0.01). A the two-year follow-up, 23 (4.49%) of the 512 carriers with GP73 more than or equal to 67 mug/L had developed HCC, while only one patient (0.19%) of the 513 carriers with GP73 less than 67 mug/L developed HCC, which yielded a relative risk value of 23.6. In the non-carriers, no HCC cases had occurred, regardless of serum GP73 level. Serum GP73 has a higher potential as a diagnostic/prognostic marker of HCC in individuals with HBsAg positivity. In follow-up of HBsAg carriers, GP73 may help in the early detection of liver cancer.

Multiple Target-Specific Molecular Imaging Agents Detect Liver Cancer in a Preclinical Model

Liver cancer is the fifth most common cause of cancer deaths worldwide. Noninvasive diagnosis is difficult and the disease heterogeneity reduces the accuracy of pathological assays. Improvement in diagnostic imaging of specific molecular disease markers has provided hope for accurate and early noninvasive detection of liver cancer. However, all current imaging technologies, including ultrasonography, computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging, are not specific targets for detection of liver cancer. The aim of this study was to test the feasibility of injecting a cocktail of specific molecular imaging agents to noninvasively image liver cancer. The target-specific cocktail contained agents for imaging the neovasculature (RGD peptide), matrix metalloproteinase (MMP), and glucose transport (18F-fluorodeoxyglucose [18F-FDG]). Imaging studies were performed in liver cancer cells and xenograft models. The distribution of MMP at the intracellular level was imaged by confocal microscopy. RGD, MMP, and 18F-FDG were imaged on tumor-bearing mice using PET, CT, X-ray, and multi-wavelength optical imaging modalities. Image data demonstrated that each agent bound to a specific disease target component. The same liver cancer xenograft contained multiple disease markers. Those disease markers were heterogenetically distributed in the same tumor nodule. The molecular imaging agents had different distributions in the whole body and inside the tumor nodule. All target-specific agents yielded high tumor-to-background ratios after injection. In conclusion, target-specific molecular imaging agents can be used to study liver cancer in vitro and in vivo. Noninvasive multimodal/multi-target-specific molecular imaging agents could provide tools to simultaneously study multiple liver cancer components.