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  • Open Access


    Efficacy and Safety of Drug-Eluting Beads Transarterial Chemoembolization by CalliSpheres® in 275 Hepatocellular Carcinoma Patients: Results From the Chinese CalliSpheres® Transarterial Chemoembolization in Liver Cancer (CTILC) Study

    Junhui Sun*1, Guanhui Zhou*1, Xiaoxi Xie, Wenjiang Gu, Jing Huang§, Dedong Zhu, Wenhao Hu#, Qinming Hou**, Changsheng Shi††, Tiefeng Li‡‡ , Xin Zhang§§, Wenbin Ji¶¶, Shihong Ying§§, Zhiyi Peng§§, Jian Zhou##, Zhihai Yu***, Jiansong Ji†††, Haijun Du‡‡‡, Xiaohua Guo§§§, Jian Fang¶¶¶, Jun Han###, Huanhai Xu****, Zhichao Sun††††, Wenqiang Yu‡‡‡‡, Guoliang Shao§§§§, Xia Wu¶¶¶¶ ,Hongjie Hu¶¶¶¶ , Ling Li#, Jiaping Zheng§§§§, Jun Luo§§§§, Yutang Chen§§§§, Guohong Cao####, Tingyang Hu‡‡‡‡

    Oncology Research, Vol.28, No.1, pp. 75-94, 2020, DOI:10.3727/096504019X15662966719585

    Abstract The purpose of this study was to investigate the efficacy and safety of drug-eluting beads transarterial chemoembolization (DEB-TACE) treatment in Chinese hepatocellular carcinoma (HCC) patients and the prognostic factors for treatment response as well as survival. A total of 275 HCC patients were included in this prospective study. Treatment response was assessed by modified Response Evaluation Criteria in Solid Tumors (mRECIST), and progression-free survival (PFS) as well as overall survival (OS) were determined. Liver function and adverse events (AEs) were assessed before and after DEB-TACE operation. Complete response (CR), partial response (PR), and objective response rate (ORR) were 22.9%, 60.7%,… More >

  • Open Access


    The Comprehensive Analysis of Efficacy and Safety of CalliSpheres® Drug-Eluting Beads Transarterial Chemoembolization in 367 Liver Cancer Patients: A Multiple-Center, Cohort Study

    Zhiyi Peng*1, Guohong Cao†1, Qinming Hou, Ling Li§, Shihong Ying*, Junhui Sun, Guanhui Zhou, Jian Zhou#, Xin Zhang*, Wenbin Ji**, Zhihai Yu††, Tiefeng Li‡‡, Dedong Zhu§, Wenhao Hu§§, Jiansong Ji¶¶, Haijun Du##, Changsheng Shi***, Xiaohua Guo†††, Jian Fang‡‡‡, Jun Han§§§, Wenjiang Gu¶¶¶, Xiaoxi Xie###, Zhichao Sun****, Huanhai Xu††††, Xia Wu‡‡‡‡, Tingyang Hu§§§§, Jing Huang¶¶¶¶, Hongjie Hu‡‡‡‡, Jiaping Zheng####, Jun Luo####, Yutang Chen####, Wenqiang Yu§§§§, Guoliang Shao####

    Oncology Research, Vol.28, No.3, pp. 249-271, 2020, DOI:10.3727/096504019X15766663541105

    Abstract This study aimed to investigate the efficacy, safety, and prognostic factors of drug-eluting beads transarterial chemoembolization (DEB-TACE) in treating Chinese patients with liver cancer. A total of 367 liver cancer patients from 24 medical centers were consecutively enrolled in this multiple-center, prospective cohort study, including 275 hepatocellular carcinoma (HCC) cases, 37 intrahepatic cholangiocarcinoma (ICC) cases, and 55 secondary liver cancer cases. All the patients received CalliSpheres® DEB-TACE treatment. Treatment response, overall survival (OS), change of liver function, and adverse events (AEs) were assessed. DEB-TACE treatment achieved 19.9% complete response (CR) and 79.6% objective response rate (ORR), with mean OS of… More >

  • Open Access


    Drug Response Prediction of Liver Cancer Cell Line Using Deep Learning

    Mehdi Hassan1,*, Safdar Ali2, Muhammad Sanaullah3, Khuram Shahzad4, Sadaf Mushtaq5,6, Rashda Abbasi6, Zulqurnain Ali4, Hani Alquhayz7

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2743-2760, 2022, DOI:10.32604/cmc.2022.020055

    Abstract Cancer is the second deadliest human disease worldwide with high mortality rate. Rehabilitation and treatment of this disease requires precise and automatic assessment of effective drug response and control system. Prediction of treated and untreated cancerous cell line is one of the most challenging problems for precise and targeted drug delivery and response. A novel approach is proposed for prediction of drug treated and untreated cancer cell line automatically by employing modified Deep neural networks. Human hepatocellular carcinoma (HepG2) cells are exposed to anticancer drug functionalized CFO@BTO nanoparticles developed by our lab. Prediction models are developed by modifying ResNet101 and… More >

  • Open Access


    Value of Mir-1271 and GPC3 in Prognosis Evaluation of Liver Cancer Patients after Liver Transarterial Chemoembolization

    Xin Chang1,2,#,*, Jin Wang2,#, Caifang Ni1

    Oncologie, Vol.23, No.1, pp. 119-130, 2021, DOI:10.32604/oncologie.2021.014152

    Abstract Objective: This research was designed to observe the value of miR-1271 and GPC3 in evaluating the prognosis of liver cancer (LC) patients after liver transarterial chemoembolization (TACE). Methods: A total of 80 patients diagnosed as LC in our hospital from January 2018 to April 2019 were included in the LC group (LCG), and then assigned into a survival group (SG) and a death group (DG) based on prognosis. Seventy healthy subjects undergoing physical examination simultaneously were included in the normal group (NG). miR-1271 and GPC3 in serum of two groups of subjects were tested via qRT-PCR. ROC curve was drawn.… More >

  • Open Access


    Modeling Liver Cancer and Leukemia Data Using Arcsine-Gaussian Distribution

    Farouq Mohammad A. Alam1, Sharifah Alrajhi1, Mazen Nassar1,2, Ahmed Z. Afify3,*

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2185-2202, 2021, DOI:10.32604/cmc.2021.015089

    Abstract The main objective of this paper is to discuss a general family of distributions generated from the symmetrical arcsine distribution. The considered family includes various asymmetrical and symmetrical probability distributions as special cases. A particular case of a symmetrical probability distribution from this family is the Arcsine–Gaussian distribution. Key statistical properties of this distribution including quantile, mean residual life, order statistics and moments are derived. The Arcsine–Gaussian parameters are estimated using two classical estimation methods called moments and maximum likelihood methods. A simulation study which provides asymptotic distribution of all considered point estimators, 90% and 95% asymptotic confidence intervals are… More >

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