scholarly journals Generation of pediatric liver cancer patient-derived xenograft platforms for pediatric liver cancer: A critical stage in the development of anticancer treatments

Hepatology ◽  
2016 ◽  
Vol 64 (4) ◽  
pp. 1017-1019 ◽  
Author(s):  
Gregory Tiao ◽  
James Geller ◽  
Nikolai A. Timchenko
2014 ◽  
Vol 50 ◽  
pp. 25
Author(s):  
M. Fabre ◽  
D. Nicolle ◽  
A. Gorse ◽  
O. Déas ◽  
C. Mussini ◽  
...  

BMC Cancer ◽  
2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Sheng He ◽  
Bo Hu ◽  
Chao Li ◽  
Ping Lin ◽  
Wei-Guo Tang ◽  
...  

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e15568-e15568
Author(s):  
Guoqin Zheng ◽  
Yan Han ◽  
Jian Zhang ◽  
Bian Li ◽  
Dong Chen ◽  
...  

e15568 Background: To investigate the sustaining time of 99mTc labeled cytarabine in the tumor of liver cancer patients by ROS under ECT imaging instrument. Methods: A liver cancer patient with 2 lesions in the liver was enrolled in this study. After obtaining informed consent and excluding relevant contraindications, two tumors in the liver of the patient were guided by single photon emission computed tomography (ECT). Injecting the drug, the tumor was injected with 99m TC-labeled cytarabine solution as control, the tumor was injected with 99m TC-labeled cytarabine + hydrogen peroxide solution (ROS) as experimental, and the two tumor masses were detected by ECT imaging, with an intensity of 1h as background. The drug retention rate was calculated by caculated of deduction of the baseline (minus background) at 1h, 3h, and 23h. Results: The patient was detected by ECT on the right laid down position. The area of the region of interest was 679 account by Gama detector, and the background level of the liver was 494 account. The acquisition time was about 400 seconds as a standard acquisition. The ECT imaging showed that the labeling rate of 99mTC-labeled cytarabine was about 100%. The drug of 99mTC-labeled cytarabineretentionrates in tumor lesions with ROS at 1 hours, 3 hours, and 23 hours were 97.83%, 81.63%, and 60.72%, respectively; the drug retention rates in tumor lesions without ROS at 1 hours, 3 hours, and 23 hours were 44.74%, 15.87%, and 0, respectively. Conclusions: The ROS can hold drug in the tumor long time after the intratumoral injection of drugs with ROS whichoxidant with the extracellular matrix and interstitial of the tumor to cause degeneration and deformation so that drug of Ara-C sustaining in tumor for a long time, and the tumor cells are also degenerated and necrotic and died, so that high concentrations of cytarabine are embedded between the denatured tumor tissues and cells. The drug slowly released from the inside to the outside to kill tumor cells. IT is further confirmed that the ROS is a sustained release agent intratumorally injection and it can significantly increase the residence time of 99mTc-labeled cytarabine in the tumor, prolong the drug action time of cytarabine, and improve the effect of killing tumor cells.


2016 ◽  
Vol 65 (2) ◽  
pp. 325-333 ◽  
Author(s):  
Beatrice Bissig-Choisat ◽  
Claudia Kettlun-Leyton ◽  
Xavier D. Legras ◽  
Barry Zorman ◽  
Mercedes Barzi ◽  
...  

Oncotarget ◽  
2017 ◽  
Vol 8 (12) ◽  
pp. 20510-20515 ◽  
Author(s):  
Peisi Kou ◽  
Yan Zhang ◽  
Wenbo Shao ◽  
Hui Zhu ◽  
Jingze Zhang ◽  
...  

2021 ◽  
Vol 18 (9) ◽  
pp. Manuscript
Author(s):  
Orasa PATSADU ◽  
Pongsakorn TANGCHITWILAIKUN ◽  
Supanut LOWSUWANKUL

This paper proposes a model to detect liver cancer patients and estimate the abnormality level of livers using a classification method based on an Indian liver patient dataset. The dataset is prepared by 3 processes: preliminary study, data cleansing, and handling imbalanced class to build the model based on multiple-stages using hybrid classification methods. The 1st stage is liver cancer patient detection. The 2nd stage is abnormality level of liver estimation, as divided using the DeRitis Ratio. The abnormality level of livers is divided into 3 levels: low, medium, and high, called ALL framework. Machine learning method is used to build multiple classification stages, which consist of Multilayer Perceptron, Logistic Regression, and Random Forest. The experimental results demonstrate that the 1st model (stage I) can detect liver cancer patient with 78.88 % accuracy. The 2nd model (stage II) achieves accuracy of 99.83 % for abnormality level of liver estimation. In addition, we compare our proposed model with another dataset. Our proposed model also outperforms detection with 76.73 and 98.26 % accuracy in stage I and stage II, respectively. Our proposed model is a benefit for physicians to support diagnosis and treatment, especially in the case of physicians desiring an intelligent decision support system.


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