Detection Of Liver Cancer Research Articles

Assessment of liver cancer biomarkers.

Liver cancer is the third cause of cancer-related deaths in the world. It is primarily divides into two main types, namely hepatocellular carcinoma (HC) and cholangiocarcinoma (IC). Due to the increasing number of patients with liver cancer and the high mortality rate, early diagnosis of the disease can be helpful in treatment, but most patients are diagnosed atlate stages of HC. The aim of this study is to screen and provide an overview on candidate biomarkers related to primary liver cancer to introduce the critical ones. In this study, various biomarkers related to the diagnosis of primary liver cancer have been studied. Accordingly, biomarkers are divided into different groups as blood biomarkers classified as serum and plasma biomarkers, tissue biomarkers, microRNA biomarkers, proteomic biomarkers and altered genes. Previous researches have focused on liver cells and bile ducts, the surround cellular environment, how cells differentiate, and the types of genes expressed in liver cancer. Some even have focused on the origin of tumor cells and how they differentiate and develop. In all these studies, the expression of specific proteins and genes in liver cancer has been considered. Based on available sources, biomarkers can be considered as candidates to diagnose and prognosis of various types of primary liver cancer, from sources such as blood, tissue, mic-RNA, proteome and genes. However, more investigations are required to introduce a biomarker for precise detection of early liver cancer.

Three-dimensional visualization combined with indocyanine green fluorescence imaging in diagnosis and treatment of primary hepatocellular carcinoma

To explore the value of three-dimensional visualization technology (3DVT) combined with indocyanine green (ICG) fluorescence imaging in the diagnosis and treatment of primary hepatocellular carcinoma (HCC). We retrospectively analyzed the clinical data of 154 patients with HCC admitted to the Department of Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University between January, 2016 and November, 2018. In 57 of the patients (3DVT group), preoperative CT and Gd-EOB-DTPA-enhanced MRI were performed and 3D visualization and surgical planning was carried out before the operation; intraoperative ICG florescence imaging was performed for real-time detection of the tumor location and demarcation, intrahepatic satellite lesions and metastases. According to the intraoperative fluorescent signals and 3D visualization-based surgical planning, the final surgical plan was determined. In the other 97 patients (control group), conventional surgical assessment and surgical resection of the tumor was carried out. The preoperative imaging findings, intraoperative tumor detection, postoperative laboratory results, pathological reports, and follow-up data of the patients were analyzed. In 3DVT group, 63 and 70 lesions were detected by preoperative CT and MRI, respectively; compared with CT examination, intraoperative ICG florescence imaging revealed additional 17 lesions, among which 10 were pathologically confirmed as HCC and 7 as cirrhosis nodules. The median volume of bleeding was 300 mL in 3DVT group, significantly less than that in the control group (400 mL; Z=2.291, P=0.022). In both groups, serious complications or perioperative death occurred in none of the patients. The incidence of postoperative complications was significantly lowed in 3DVT group than in the control group [21% (12/57) vs 48.4% (47/97); χ2=11.406, P=0.001]. The overall disease-free survival rate at 2 years after the operation was significantly higher in 3DVT group than in the control group (74.9% vs 28.9%, P=0.022). 3DVT combined with ICG fluorescence imaging allows precise preoperative diagnosis, surgical planning and implementation, intraoperative detection of small liver cancers and precise navigation for HCC treatment, thereby helping to reduce postoperative complications and improve the disease-free survival rate of the patients.

Prior Detection of a Person’s Risk Level of Getting Suffered from Liver Cancer

Liver Cancer becomes a major problem globally because it is responsible for the majority of death. Identification of various Risk factors which can be either genetic or environmental becomes necessary for early detection of Liver Cancer. To solve this problem, the data has been collected from various diagnostic centres, the data consists of 100 cancerous and 100 non-cancerous/healthy persons, then significant frequent patterns are identified by using the Apriori Algorithm. By employing significant frequent pattern, a prediction technique has been developed which identify the person susceptibility toward Liver Cancer by using the Risk Factors. Most peoples do not know that they are suffering from Liver Cancer at the early stage of cancer, and when they knew, that time it becomes impossible to cure. So, for proper cure Early detection of Liver Cancer acquires a necessary step in its diagnosis process, and for making a powerful preventive approach. This prediction system can effectively and successfully predict the Liver Cancer Risk, and it has been predicted in the form of three stages that is low Risk, medium Risk or high Risk. This approach can be used in hospitals for diagnosis of Liver Cancer patients.

The Split Primer Ligation‐triggered 8‐17 DNAzyme Assisted Cascade Rolling Circle Amplification for High Specific Detection of Liver Cancer‐involved mRNAs: TK1 and c‐myc

AbstractSimultaneous detection of various intracellular biomarkers is promising for early diagnosis and treatment of cancer. Herein, we develop a novel method for high specific and ultrasensitive detection of liver cancer cell‐involved mRNAs: TK1 and c‐myc based on the split primer ligation‐triggered 8‐17 DNAzyme assisted cascade rolling circle amplification. Only two targets exist simultaneously, can trigger the rolling circle amplification to improve the accuracy and sensitivity. Meanwhile, an electrochemical molecular beacon, based on the host‐guest recognition between ferrocene groups and cucurbit urils [7] (CB[7]/Fc‐MB), is used to cause a “turn‐off” electrochemical signal which is decreased by disrupting its hairpin structure. Under the optimal conditions, the detection limit of TK1 and c‐myc mRNA is as low as 0.06 nM. Moreover, this method can be used to detect the TK1 and c‐myc mRNA in HepG2 cells and distinguish between cancer cells and their normal cells, proving that the method has the potential to detect the variation of biomarkers in vivo.

The positive correlation of TIPRL with LC3 and CD133 contributes to cancer aggressiveness: potential biomarkers for early liver cancer

Studies have reported dysregulation of TIPRL, LC3 and CD133 in liver cancer tissues. However, their respective relationships to liver cancer and roles as biomarkers for prognosis and diagnosis of liver cancer have never been studied. Here we report that the level of TIPRL is significantly correlated with levels of LC3 (Spearman r = 0.9) and CD133 (r = 0.7) in liver cancer tissues. We observed significant upregulations of TIPRL, LC3 and CD133 in hepatocellular carcinomas (HCCs) compared with adjacent normal tissues. Importantly, TIPRL, tested among additional variables, showed a significant impact on the prognosis of HCC patients. TIPRL knockdown significantly reduced expressions of LC3, CD133, stemness-related genes, as well as viability and stemness of liver cancer cell-lines, which were promoted by ectopic TIPRL expression. Either alone or as a combination, TIPRL, LC3 and CD133 showed significant values of area under the curve (AUC) and sensitivity/specificity in early liver cancer tissues. Furthermore, the statistical association and the diagnostic efficacies of TIPRL, LC3 and CD133 in HCC tissues were confirmed in a different IHC cohort. This data demonstrates that the complex involvement of TIPRL/LC3/CD133 in liver cancer aggressiveness can together or individually serve as potential biomarkers for the early detection of liver cancer.

GALAD Score Detects Early Hepatocellular Carcinoma in an International Cohort of Patients With Nonalcoholic Steatohepatitis

The prevalence of nonalcoholic steatohepatitis (NASH) associated hepatocellular carcinoma (HCC) is increasing. However, strategies for detection of early-stage HCC in patients with NASH have limitations. We assessed the ability of the GALAD score, which determines risk of HCC based on patient sex; age; and serum levels of α-fetoprotein (AFP), AFP isoform L3 (AFP-L3), and des-gamma-carboxy prothrombin (DCP), to detect HCC in patients with NASH. We performed a case-control study of 125 patients with HCC (20% within Milan Criteria) and 231 patients without HCC (NASH controls) from 8 centers in Germany. We compared the performance of serum AFP, AFP-L3, or DCP vs GALAD score to identify patients with HCC using receiver operating characteristic curves and corresponding area under the curve (AUC) analyses. We also analyzed data from 389 patients with NASH under surveillance for HCC in Japan, followed for a median of 167 months. During the 5-year screening period, 26 patients developed HCC. To compensate for irregular intervals of data points, we performed locally weighted scatterplot smoothing, linear regression, and a non-linear curve fit to assess development of GALAD before HCC development. The GALAD score identified patients with any stage HCC with an AUC of 0.96 - significantly greater than values for serum levels of AFP (AUC, 0.88), AFP-L3 (AUC, 0.86) or DCP (AUC, 0.87). AUC values for the GALAD score were consistent in patients with cirrhosis (AUC, 0.93) and without cirrhosis (AUC, 0.98). For detection of HCC within Milan Criteria, the GALAD score achieved an AUC of 0.91, with a sensitivity of 68% and specificity of 95% at a cutoff of -0.63. In a pilot Japanese cohort study, the mean GALAD score was higher in patients with NASH who developed HCC than in those who did not develop HCC as early as 1.5 years before HCC diagnosis. GALAD scores were above -0.63 approximately 200 days before the diagnosis of HCC. In a case-control study performed in Germany and a pilot cohort study in Japan, we found the GALAD score may detect HCC with high levels of accuracy in patients with NASH, with and without cirrhosis. The GALAD score can detect patients with early-stage HCC, and might facilitate surveillance of patients with NASH, who are often obese, which limits the sensitivity of detection of liver cancer by ultrasound.

Automatic Liver Cancer Segmentation using active contour model and RF Classifier

In this paper, we presented the new method for liver cancer detection. Computed Tomography (CT) has becomes important tool for diagnosis of liver cancer. The proposed method used in this paper is Random Forest (RF) classifier algorithm for the detection of cancer in the liver. For the automatic segmentation, here we use active contour method to segment the liver and liver cancer to rectify the manual segmentation problem. It is fully automatic and the proposed classifier will successfully classifies whether it is malignant or benign liver cancer tumor. Manual identification is not accurate and also time consuming task. The new method proposed in this paper will segment the liver cancer from the CT image of liver automatically. It is highly accurate and less computation time. The experiment results show the accuracy of the proposed method. Random Forest classifier has 91% accuracy rate and less error rate and achieved excellent test result.

Precision detection of liver metastasis by collagen-targeted protein MRI contrast agent

The Liver is the most common organ for metastasis for various cancers, including uveal melanoma, the most common primary intraocular tumor. Uveal melanoma metastasizes to the liver in ~90% of patients, and results in death in almost all cases due to late detection and lack of effective treatment. There is a pressing unmet medical need to develop MRI contrast agents and imaging methodologies with desired sensitivity and specificity to overcome the high heterogeneous background and in vivo properties as well as reduced toxicity. Herein, we report the development of a collagen targeting protein contrast agent (ProCA32.collagen1), since collagen is a diagnostic biomarker and therapeutic target for many types of primary and metastatic cancers and the tumor microenvironment. In addition to a strong affinity to collagen I, ProCA32.collagen1 possesses high relaxivities (r1 and r2 are 68.0 ± 0.25 and 100.0 ± 0.32 mM−1 s−1 at 1.4 T, respectively, and 42.6 ± 1.0 and 217 ± 2.4 mM−1s−1 at 7.0 T per particle). ProCA32.collagen1 also has strong serum stability against degradation, resistance to transmetallation, and 102 and 1013-fold higher metal selectivity for Gd3+ over Ca2+ and Zn2+, respectively, compared to clinical contrast agents. ProCA32.collagen1 does not exhibit any cell toxicity for various cell lines. Sensitive detection of liver lesions in animal models can be achieved using multiple imaging methodologies, taking advantage of the dual relaxation property of ProCA32.collagen1. ProCA32.collagen1 enables sensitive and early stage detection of hepatic micrometastasis as small as 0.144 mm2 and two different tumor growth patterns. Further development of ProCA32.collagen1 has the potential to greatly facilitate non-invasive, early detection and staging of primary and metastatic liver cancers, and devising effective treatments.

Abstract 837: Circulating cell-free DNA methylation assay: Towards early detection of multiple cancer types

Abstract Background: Alterations in DNA methylation play a key role in tumor initiation, and methylation markers are well established for detecting various cancer types. Cancer-specific DNA methylation patterns of cell-free DNA (cfDNA) isolated from blood samples is a non-invasive method to obtain representative epigenetic information from solid tumors. In the present study, we identified and validated cancer-specific methylation markers for diagnosis of liver, breast and colorectal cancers with high sensitivity and specificity. Methods: Three blinded validation studies were performed in order to evaluate individual panels of DNA methylation markers developed for the detection of liver, breast or colorectal cancers. For the liver cancer panel study, samples drawn from 154 subjects were analyzed, including healthy donors (30) and subjects diagnosed with benign liver disease (10), liver cancer stage I-IV (60), or another cancer type (30). For the breast cancer panel study, samples drawn from 151 subjects were analyzed, including healthy donors (39) and subjects diagnosed with benign breast disease (15), breast cancer stage I-IV (65), or another cancer type (32). For the colorectal cancer panel study, samples drawn from 154 subjects were analyzed, including healthy donors (42) and subjects diagnosed with benign colorectal disease (14), colorectal cancer stage I-IV (68), or another cancer type (30). Cell-free DNA was extracted from all samples, bisulfite converted, and DNA methylation was quantified at target sites by using the IvyGene Platform for each of the three cancer-specific marker panel. Results: By quantifying DNA methylation at the target sites, the cancer-specific markers were able to differentiate subjects diagnosed with cancer from both healthy donors and subjects with benign diseases. The liver cancer cfDNA methylation panel showed an overall sensitivity of 95% and specificity of 97.5%. Whereas, the breast cancer cfDNA methylation panel correctly identified the breast cancer samples for an overall calculated sensitivity of 89% and specificity of 96%. The colorectal cancer cfDNA methylation panel also showed high overall sensitivity of 93% and specificity of 100%. Conclusions: These results demonstrate the high diagnostic potential of cfDNA methylation markers isolated from blood for the detection of multiple cancers at various stages with high sensitivity and specificity. In addition, a quantitative analysis of cfDNA methylation signature provides an opportunity for monitoring of disease. The benefits of a sensitive and specific assay to identify and monitor cancer is anticipated to aid disease management and ultimately, improve patient outcomes. Citation Format: Dhruvajyoti Roy, David Taggart, Lianghong Zheng, Dan Liu, Gen Li, Mingzhen Li, Kang Zhang, Richard A. Van Etten. Circulating cell-free DNA methylation assay: Towards early detection of multiple cancer types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 837.

Abstract 3156: Pathway Specific Functional Biomarkers for the Early Detection of Liver Cancer

Abstract In its early stages, HCC is curable; once advanced, HCC is associated with high mortality. HCC is associated with alterations in key driving pathways, including the Wnt pathway, TGF-β pathway, the p53 pathway, and pathways that regulate Myc activity. Members of the TGF-β superfamily regulate liver inflammation and can have tumor-suppressing or tumor-promoting activities that have been demonstrated to play key roles in liver and GI cancers through mouse models and human genomics. Yet, specific populations that are targetable with altered TGF-β remain unclear. Based upon our functional preclinical models and pilot studies in human liver disease samples, our hypothesis that aberrant expression of TGF-β pathways can lead to early detection of HCC and risk stratification. Methods and Results: Through preclinical studies that integrate analysis of human genomic data from The Cancer Genome Atlas (TCGA), mouse models, and human tissue/cell line studies, we sought to determine whether specific high-risk populations may be identified through a functional TGF-β pathway related biomarkers alterations. We observe the following: • Altered expression of TGF-β-Smad signaling genes is common in HCC (40%) • Somatic mutations in at least one gene encoding a member of the TGF-β-Smad pathway are common in HCC (38%) with the SMAD3 adaptor, β2SP (encoded by SPTBN1) having the highest frequency of mutation (6%), regardless of etiology, HBV or HCV infection or non-alcoholic steatohepatitis (NASH) • Disruption of the TGF-β-Smad pathway is associated with dysregulation of potentially targetable oncogenes that include MDM2, Telomerase, IGF2 and others. • TGF-β-Smad pathway activity clusters HCC patients into 4 groups. • HCC patients with a signature indicative of “inactivated” TGF-β-Smad signaling had shorter survival times than HCC patients with a signature indicative of “activated” TGF-β-Smad signaling reflecting a tumor suppressor role of TGF-β signaling in HCC. • TGF-β-Smad pathway activity correlates with genes in the DNA damage response and sirtuins. • Analysis of liver samples from normal subjects, patients with alcohol-induced cirrhosis, and alcohol-associated HCC showed that in the cirrhotic tissue TGF-β-Smad3 signaling and FANCD2 were highest and that in the HCC tissue these were lowest, suggesting a loss of this tumor-suppressing pathway in HCC • Mice deficient in either sirtuin activity (Sirt6-/-) or compromised TGF-β signaling (Sptbn+/-/Smad3+/-) are susceptible to liver injury, steatosis and spontaneously develop HCC • TGF-β-Smad pathway activity correlates with genes associated with hepatic fibrosis, immune cells, and the tumor microenvironment. Conclusions: Our data indicate that TGF-β-Smad members, particularly Smad3 and β2SP, and markers of DNA repair processes, particularly FANCD2 are potentially useful biomarkers of the loss from tumor-suppression by TGF-β signaling, which may be an early indicator of HCC. Citation Format: Kirti Shetty, Chu-Xia Deng, Wilma S. Jogunoori, Richard Amdur, Linda S. Resar, Patricia Latham, Raja Mazumder, Anelia Horvath, Bao-Ngoc Nguyen, Shulin Li, Xifeng Wu, Herbert Yu, Linda L. Wong, Jon White, Sylvia Silver, Asif Rashid, Vikas Kundra, Xin Wei Wang, Lopa Mishra. Pathway Specific Functional Biomarkers for the Early Detection of Liver Cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3156.

Abstract 417: Urine as an alternative to blood for germline genomics and cancer genetics

Abstract Currently, blood is the body fluid of choice to provide comprehensive germline genomics via Peripheral Blood Mononuclear Cells (PBMC) and cancer genetics via the detection of circulating tumor DNA (ctDNA, liquid biopsy) for precision medicine. In this study, we examined if urine can be an alternative to blood for comprehensive germline genomics and for liquid biopsy. We have previously shown that human urine contains DNA derived both from sloughed-off cell debris of the urinary tract (greater than 1 KB/High Molecular weight or HMW) and trans-renal DNA from the circulation (less than 1 KB/Low molecular weight or LMW). We performed whole genome sequencing (WGS) to compare the quality and comprehensiveness of the genomic data between 3 sets of matched PBMC and HMW urine DNA. There was no statistically significant difference between the NGS data obtained from PBMC and urine DNA upon comparing the number of reads, average coverage, percent aligned reads, percent reads passing Phred score Q30 (p value less than 0.05, paired t-test) and coverage of human genome. Next, we compared the detectability of ctDNA in the matching LMW urine and plasma DNA obtained from 44 HCC patients by employing a panel of HCC biomarkers including genetic mutations at TP53 codon 249 [G:C to T:A transversion (TP53 249T)], CTNNB1 exon 3 regions 32-37 (CTNNB1 32-37), and hTERT promoter region position 124 (hTERT 124) and aberrant methylation of RASSF1A (mRASSF1A) promoter. In this study, a systematic analysis of this HCC biomarker panel demonstrated that urine can complement blood for liver cancer screening. A combination of the plasma and urine biomarkers with serum AFP the current most widely used biomarker for HCC can provide higher sensitivity (up to 30% more) for liver cancer detection. In order to characterize and determine if LMW urine DNA is derived from plasma DNA, we performed WGS to compare the size profiles and coverage of these two sources of cell-free DNA in 3 sets of matching HCC urine and plasma samples. Overall shorter read lengths were obtained from urine DNA as compared to the corresponding plasma DNA. A series of peaks occurring at 10 bp periodicity was displayed in both sources of cell-free DNA Further analysis regarding the genome coverage, overlapping fraction and variant detection is ongoing. In conclusion, our data suggests that (i) HMW urine DNA can replace PBMC DNA for providing comprehensive genomic sequencing data and (ii) urine can complement blood for liver cancer liquid biopsy, precision medicine, and potential applications to other cancers. Citation Format: Surbhi Jain, Tai-Jung Lee, Adam Zhang, Jonathan Cheng, Jamin D. Steffen, Chi-Tan Hu, James P. Hamilton, Harry Luu, Hie-Won Hann, Fwu-Shan Shieh, Selena Y. Lin, Wei Song, Ying-Hsiu Su. Urine as an alternative to blood for germline genomics and cancer genetics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 417.

Recent advances and investigation of efficient Computer Aided Diagnosis systems for CT images in Liver cancer detection

Recent advances and investigation of efficient Computer Aided Diagnosis systems for CT images in Liver cancer detection

A comprehensive review on nature inspired computing algorithms for the diagnosis of chronic disorders in human beings

Diabetes and cancer are two major life-threatening human chronic disorders that have a high rate of disability and mortality. These diseases have been diagnosed using different deterministic and nature inspired computing algorithms. Here, an effort is made to represent the role of five different insect-based nature inspired computing algorithms [ant colony optimization (ACO), artificial bee colony (ABC), glow-worm swarm optimization (GSO), firefly algorithm (FA) and antlion optimization (ALO)] used for the diagnosis of these two chronic disorders. Initially, the basic statistics of diabetes and cancer patients have been presented. The main intention of this study lies in exploring the usage and performance of ACO, ABC, GSO, FA and ALO in diagnosing different stage and types of diabetes and cancer. It has been revealed that most of the diabetes diagnosis work has been carried out using ACO followed by ABC. As far as cancer is concerned, the three insect-based algorithms, i.e. ACO, ABC and FA, have been also effectively employed for detection of breast, lung, liver, prostate and ovarian cancer. In general, most of the disease diagnostic work has been carried out using ACO, whereas GSO found to be least explored. The rate of predictive accuracy achieved using the hybridization of ACO and neural network is found to be more promising as compared to other individual or hybrid approaches. Likewise, for breast cancer, the amalgamated use of ABC and neural network is more productive. Similarly, the hybrid approach of ACO and neural network is also found useful for early prognosis of lung and gastric cancer. In general, the diagnostic results obtained using hybrid approaches are more promising than their individual use. However, several hybrid combinations are still needed to be explored for the diagnosis of diabetes and different types of cancer, viz. liver, gastric, ovarian, leukaemia as well as a brain tumour. Finally, there is also a scope to use and explore the efficiency of binary and chaotic variants of ACO, ABC, GSO, FA and ALO for the diagnosis of these two critical human disorders.

Mo1435 – Pathway Specific Functional Biomarkers for the Early Detection of Liver Cancer

Mo1435 – Pathway Specific Functional Biomarkers for the Early Detection of Liver Cancer

Detection of Liver Cancer Using Modified Fuzzy Clustering and Decision Tree Classifier in CT Images

Manual detection and characterization of liver cancer using computed tomography (CT) scan images is a challenging task. In this paper, we have presented an automatic approach that integrates the adaptive thresholding and spatial fuzzy clustering approach for detection of cancer region in CT scan images of liver. The algorithm was tested in a series of 123 real-time images collected from the different subjects at Institute of Medical Science and SUM Hospital, India. Initially the liver was separated from other parts of the body with adaptive thresholding and then the cancer affected lesions from liver was segmented with spatial fuzzy clustering. The informative features were extracted from segmented cancerous region and were classified into two types of liver cancers i.e., hepatocellular carcinoma (HCC) and metastatic carcinoma (MET) using multilayer perceptron (MLP) and C4.5 decision tree classifiers. The performance of the classifiers was evaluated using 10-fold cross validation process in terms of sensitivity, specificity, accuracy and dice similarity coefficient. The method was effectively detected the lesion with accuracy of 89.15% in MLP classifier and of 95.02% in C4.5 classifier. This results proves that the spatial fuzzy c-means (SFCM) based segmentation with C4.5 decision tree classifier is an effective approach for automatic recognition of the liver cancer.

Validation of R-2-[18F]Fluoropropionic Acid as a Potential Tracer for PET Imaging of Liver Cancer.

2-[18F]Fluoropropionic acid (RS-[18F]FPA) has shown potential value as a short-chain fatty acid positron emission tomography (PET) tracer for the detection of liver cancer. However, RS-[18F]FPA is a mixture of 2-R-[18F]fluoropropionic acid (R-[18F]FPA) and 2-S-[18F]fluoropropionic acid (S-[18F]FPA). The aim of this study is to validate the feasibility of R-[18F]FPA in preclinical PET imaging of liver cancer and to compare the use of R-[18F]FPA with that of RS-[18F]FPA and S-[18F]FPA. A comparative study of R-[18F]FPA, RS-[18F]FPA, S-[18F]FPA, and [18F]FDG micro-PET imaging was performed in HepG2 and SK-Hep-1 tumor-bearing mice. A comparison of R-[18F]FPA uptake with that of S-[18F]FPA by HepG2 and SK-Hep-1 cells was made at different time points. Additionally, in vivo blocking experiments in HepG2 and SK-Hep-1 tumor models were conducted with orlistat and 3-nitropropionic acid (3-NP). In vitro blocking experiments with orlistat or 3-NP were performed with HepG2 and SK-Hep-1 cells. The radioactivity uptake values of R-[18F]FPA were comparable to those of RS-[18F]FPA but were higher than those of S-[18F]FPA and 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) in HepG2 tumors. The radioactivity uptake values of R-[18F]FPA in large HepG2 tumors were lower than those of [18F]FDG (P < 0.05), while R-[18F]FPA PET was significantly superior to [18F]FDG PET in detecting small tumors (both SK-Hep-1 and HepG2 tumors). The in vivo PET imaging experiments showed that R-[18F]FPA uptake in HepG2 tumor-bearing mice was blocked by 19.3% and 31.8% after treatment with orlistat and 3-NP, respectively. The radioactivity uptake values of R-[18F]FPA in SK-Hep-1 tumor-bearing mice was blocked by 39.5% with orlistat. R-[18F]FPA seems to be more potential than S-[18F]FPA as an optically pure PET probe, with effective compensation for the deficiencies of [18F]FDG, particularly in PET imaging of small liver cancer. The uptake mechanism of [18F]FPA in liver cancer may be related to fatty acid synthesis and the tricarboxylic acid cycle. However, compared with the racemic RS-[18F]FPA, the possible advantages of R-enantiomer R-[18F]FPA still needs further research.

Editorial introductions

Current Opinion in Oncology was launched in 1989. It is one of a successful series of review journals whose unique format is designed to provide a systematic and critical assessment of the literature as presented in the many primary journals. The field of psychiatry is divided into 15 sections that are reviewed once a year. Each section is assigned a Section Editor, a leading authority in the area, who identifies the most important topics at that time. Here we are pleased to introduce the Journal's Section Editors for this issue. SECTION EDITORS Véronique del MarmolVéronique del MarmolProfessor Véronique del Marmol is the Head of the Department of Dermatology at Hôspital Erasme, Université Libre de Bruxelles, Belgium. Professor del Marmol graduated as a bachelor in philosophy in 1983 and as an MD in 1987 at the Université de Louvain, Belgium. She graduated as an MSc in molecular biology in 1991 and received her PhD in 1995 for her work on “Contribution to the study of the regulation of human melanogenesis”. She graduated as a specialist in dermato-venerology in 1996 at the Université Libre de Bruxelles. Skin cancer, skin pigmentation, melanoma and non-melanoma skin cancer and non-invasive imaging are the main areas of interest for Professor del Marmol. In recognition of her work in these fields, she has been awarded several scholarships and grants, including the Major Roche Dermatology Fundamental Research Award. She authored and co-authored more than 116 peer reviewed articles and seven books (H index 36), particularly in Journal of Investigative Dermatology, Experimental Dermatology, Journal of the European Academy of Dermatology and Venereology (JEADV), Nature and New England Journal of Medicine. She is a founding member for the European skin cancer prevention campaign EUROMELANOMA and since 2009 led its European campaign. She is board member of the European Dermatology Forum (EDF), the Academy of Dermatology and Venerology (EADV), the European Academy of Dermato-oncology (EADO) and the European Pigment Cell Society (ESPCR). She is vice-president of the Belgian Society of Dermatology and was elected in 2014 as member of the Belgian Royal Academy of Medicine. Pierre HainautPierre HainautPierre Hainaut (born 1958, Belgium) is PhD in Biology, University of Liège, Belgium, 1987. After postdocs in France and in the UK (1988–1994), he joined the International Agency for Research on Cancer (IARC, World Health Organization) in 1994, where he held the post of head of Molecular Carcinogenesis from 1999 to 2011. In 2012, he joined the International Prevention Research Institute (iPRI) as Research Director. Since 2014, he has been appointed as Professor of Cancer Biology and Chair of Excellence in Translational Research at Université Grenoble Alpes, France. Since October 2014, he is Director of the Institute of Advanced Biosciences (formerly Institut Albert Bonniot), a multi-thematic research center supported dedicated to epigenetics, chronic diseases and cancer (17 research groups). He is also responsible for Molecular Diagnosis of Cancer at the University Hospital (CHU) of Grenoble, France. His research focuses on TP53 mutations and on biomarkers of transition from chronic diseases to cancer. From 1994 to 2011, he led the development of the international IARC database of TP53 mutations, a source of information on the causes and consequences of mutations affecting the p53 suppressor protein in cancer. His current research focuses on gain-of-function effects in lung cancer and on biomarkers for early detection of liver and lung cancers, in particular in low-resource countries. Pierre Hainaut is author of over 400 publications and 50 book chapters (h index: 83, Google Scholar). He has co-edited two books on p53 (25 Years of p53 Research 2005, 2007, p53 in the Clinics, Springer) and a textbook of molecular epidemiology (Molecular Epidemiology: Principle and Practice, IARC Press, 2011).

Metastatic Liver Cancer Detection by CNN Using Artificial Lesion Images

Metastatic Liver Cancer Detection by CNN Using Artificial Lesion Images

Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques

ObjectivesLiver cancer is one of the leading cause of death in all over the world. Detecting the cancer tissue manually is a difficult task and time consuming. Hence, a computer-aided diagnosis (CAD) is used in decision making process for accurate detection for appropriate therapy. Therefore the main objective of this work is to detect the liver cancer accurately using automated method. MethodsIn this work, we have proposed a new system called as watershed Gaussian based deep learning (WGDL) technique for effective delineate the cancer lesion in computed tomography (CT) images of the liver. A total of 225 images were used in this work to develop the proposed model. Initially, the liver was separated using marker controlled watershed segmentation process and finally the cancer affected lesion was segmented using the Gaussian mixture model (GMM) algorithm. After tumor segmentation, various texture features were extracted from the segmented region. These segmented features were fed to deep neural network (DNN) classifier for automated classification of three types of liver cancer i.e. hemangioma (HEM), hepatocellular carcinoma (HCC) and metastatic carcinoma (MET). ResultsWe have achieved a classification accuracy of 99.38%, Jaccard index of 98.18%, at 200 epochs using DNN classifier with a negligible validation loss of 0.062 during the classification process. ConclusionsOur developed system is ready to be tested with huge database and can aid the radiologist in detecting the liver cancer using CT images.

Label-free detection of nasopharyngeal and liver cancer using surface-enhanced Raman spectroscopy and partial lease squares combined with support vector machine.

In this paper, we investigated the feasibility of using surface enhanced Raman spectroscopy (SERS) and multivariate analysis method to discriminate liver cancer and nasopharyngeal cancer from healthy volunteers. SERS measurements were performed on serum protein samples from 104 liver cancer patients, 100 nasopharyngeal cancer patients, and 95 healthy volunteers. Two dimensionality reduction methods, principal component analysis (PCA) and partial least square (PLS) were compared, and the results indicated that the performance of PLS is superior to that of PCA. When the number of components was compressed to 3 by PLS, support vector machine (SVM) with a Gaussian radial basis function (RBF) was employed to classify various cancers simultaneously. Based on the PLS-SVM algorithm, high diagnostic accuracies of 95.09% and 90.67% were achieved from the training set and the unknown testing set, respectively. The results of this exploratory work demonstrate that serum protein SERS technology combined with PLS-SVM diagnostic algorithm has great potential for the noninvasive screening of cancer.