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This study aims to describe the clinical and subclinical characteristics of patients with castration-resistant prostate cancer (CRPC). The retrospective study was conducted on 46 patients with CRPC in Huu Nghi Hospital. The results indicated that the main clinical symptoms were bone pain (63.0%), hematuria (34.0%), and lower urinary tract symptoms (32.6%). The highest and lowest PSA concentrations were 611.3 ng/mL and 0.01 ng/mL, respectively. The most common sites of metastasis were bone (82.6%), lymph node (50.0%), and lung (19.6%). These patients were all at the late stage, so most of them had symptoms of metastasis and high PSA. Keywords: Prostate cancer, castration-resistant, clinical characteristics, subclinical characteristics.

Radiofrequency ablation (RFA) of liver cancer under computer tomography (CT) guidance is a minimally invasive procedure in which CT images are utilized to guide the physician in introducing the needle into the target lesion. However, the adequate visualization of the needle and anatomy is hampered by the 2D slide based-view used in the current clinical practice. Thus, due to the lack of 3D information, the physician requires high experience and more interaction with the guidance systems to envision the needle's position in the liver, which is inconvenient in clinical practice. In this study, we propose a method for robust needle segmentation using CT images to improve the visualization of the needle during the intervention. The method utilizes a convolutional neural network (CNN) to detect the needle in orthogonal 2D projections of the CT image to construct the needle volume of interest (VOI). Subsequently, a patch-based 3D CNN is applied to segment the needle. We evaluate the method's accuracy using Dice score (DSC), Hausdorff distance (HD), the needle shaft error Eshaft, and needle tip error Etip.. The results show that the proposed method achieves the means of DSC, HD, Etip, Eshaft and processing time of 0.89, 3.3 mm, 0.9 mm, 0.43 mm, and 2.6 seconds, respectively. We conclude that the proposed method is feasible for improving needle visualization in the interventional room.

This paper aims to determine the rate of epidermal growth factor receptor (EGFR) gene mutations in advanced and metastatic squamous cell lung cancer in Vietnamese patients. EGFR mutation testing was performed on 559 formalin-fixed paraffin-embedded tissue samples from advanced and metastatic squamous cell lung cancer patients by real-time PCR. The results showed that EGFR mutations were found in 8.4%. Most of them were exon 19 deletions, approximately 51.1%. Multivariate analysis showed that EGFR mutations were more prevalent in gender (p

This study aims to describe EGFR mutation status detected in plasma samples and analyze some related factors in non-small cell lung cancer patients after treatment with the generation I and II Tyrosine Kinase inhibitors at Bach Mai Hospital. Patients and methods: A retrospective descriptive study on 75 patients with non-small cell lung cancer tested for EGFR mutations in plasma after treatment with 1st or 2nd EGFR-TKIs in 2019-2020. Results: EGFR mutations were detected in 97.3% plasma samples; the T790M mutation accounted for 37.3% and is accompanied by a sensitive mutation TKI; The T790M mutations ratio after treatment was higher than before treatment with TKIs (p < 0.001); the median total duration of EGFR-TKI treatment was significantly longer in patients with T790M mutation than in those without (16.8 months vs 12.5 months, p = 0.005); the T790M mutation ratio was higher in patients with a deletion on exon 19 than in L858R mutation on exon 21 (p=0.028), in Erlotinib treatment group higher than in Gefitinib and Afatinib treatment group (51.4% vs. with 20.0% and 33.3%, p=0.039). Conclusions: EGFR mutation plasma test helps to monitor disease progression and detect resistance mutations T790M in NSCLC patients after TKI generation I and II. Keywords: non-small cell lung cancer, EGFR mutations, T790M mutations, TKIs.. References [1] H. Sung et al., Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers In 185 Countries, CA: A Cancer Journal for Clinicians. Vol. 71, No. 3, 2021, pp. 209-249, https://doi.org/10.3322/caac.21660.[2] N. V. Hieu, Oncology, Medical Publisher, 2015.[3] P. N. Ding et al., Plasma Pre-treatment T790M Relative Allelic Frequency in Patients with Advanced EGFR-mutated Non-small Cell Lung Cancer Predicts Treatment Response to Subsequent-Line Osimertinib, Translational Lung Cancer Research, Vol. 10, No. 4, 2021, pp. 1623-1634, https://doi.org/10.1186/s40169-020-0269-y.[4] N. Yamamoto et al., Erlotinib Plus Bevacizumab Vs Erlotinib Monotherapy as First-line Treatment for Advanced EGFR Mutation-positive Non-squamous Non-small-cell Lung Cancer: Survival Follow-up Results of the Randomized JO25567 Study, Lung Cancer, Vol. 151, 2021, pp.20-24, https://doi.org/10.1016/j.lungcan.2020.11.020.[5] D. S. Ettinger et al., NCCN Guidelines Insights: Non–Small Cell Lung Cancer, Version 2, 2021: Featured Updates to the NCCN Guidelines", Journal of the National Comprehensive Cancer Network, Vol. 19, No. 3, 2021, pp. 254-266, https://doi.org/10.6004/jnccn.2021.0013.[6] K. Usui et al., Plasma ctDNA Monitoring During Epidermal Growth Factor Receptor (EGFR)-Tyrosine Kinase Inhibitor Treatment in Patients With EGFR-mutant Non-small Cell Lung Cancer (JP-CLEAR trial), Japanese Journal of Clinical Oncology, Vol. 49, No. 6, 2021, pp. 554-558, https://doi.org/10.1093/jjco/hyz023.[7] M. Nagasaka et al., Liquid Biopsy for Therapy Monitoring in Early-stage Non-small Cell Lung Cancer, Molecular Cancer, Vol. 20, No. 1, 2021, pp. 82, https://doi.org/10.1186/s12943-021-01371-1.[8] M. D. Re et al., Incidence of T790M in Patients with NSCLC Progressed to Gefitinib, Erlotinib, and Afatinib: A Study on Circulating Cell-free DNA, Clinical Lung Cancer, Vol. 21, No. 3, 2020, pp. 232-237, https://doi.org/10.1016/j.cllc.2019.10.003.[9] S. Jenkins et al., Plasma ctDNA Analysis for Detection of the EGFR T790M Mutation in Patients with Advanced Non–small Cell Lung Cancer", Journal of Thoracic Oncology, Vol. 12, No. 7, 2017, pp. 1061-1070, https://doi.org/10.1016/j.jtho.2017.04.003.[10] R. Ko et al., Frequency of EGFR T790M Mutation and Multimutational Profiles of Rebiopsy Samples from Non-small Cell Lung Cancer Developing Acquired Resistance to EGFR Tyrosine Kinase Inhibitors in Japanese Patients, BMC Cancer, Vol. 16, No. 1, 2016, pp. 864, https://doi.org/10.1186/s12885-016-2902-0.[11] N. Matsuo et al., Association of EGFR Exon 19 Deletion and EGFR-TKI Treatment Duration with Frequency of T790M Mutation in EGFR-Mutant Lung Cancer Patients, Scientific Reports, Vol. 6, No. 1, 2016, pp. 36458 , https://doi.org/10.1038/srep36458.[12] W. Fanget al., EGFR Exon 20 Insertion Mutations and Response to Osimertinib in Non-small-cell Lung Cancer, BMC Cancer, Vol. 19, No. 1, 2019, pp. 1-9, https://doi.org/10.1186/s12885-019-5820-0.[13] D. T. Hien, P. C. Phuong, Analysis of EGFR Mutations in Paraffin-covered Tissue Samples and some Related Factors from Non-small Cell Lung Cancer Patients Over 60 Years Old, Science and Technology Vietnam, Vol. 62, No. 7, 2020, pp. 1-5[14] S. Hong et al., Concomitant Genetic Alterations with Response to Treatment and Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors in Patients with EGFR-Mutant Advanced Non–small Cell Lung Cancer, JAMA Oncology, Vol. 4, No. 5, 2018, pp. 739-742, https://doi.org/10.1001/jamaoncol.2018.0049.

Liver segmentation is relevant for several clinical applications. Automatic liver segmentation using convolutional neural networks (CNNs) has been recently investigated. In this paper, we propose a new approach of combining a largest connected component (LCC) algorithm, as a post-processing step, with CNN approaches to improve liver segmentation accuracy. Specifically, in this study, the algorithm is combined with three well-known CNNs for liver segmentation: FCN-CRF, DRIU and V-net. We perform the experiment on a variety of liver CT images, ranging from non-contrast enhanced CT images to low-dose contrast enhanced CT images. The methods are evaluated using Dice score, Haudorff distance, mean surface distance, and false positive rate between the liver segmentation and the ground truth. The quantitative results demonstrate that the LCC algorithm statistically significantly improves results of the liver segmentation on non-contrast enhanced and low-dose images for all three CNNs. The combination with V-net shows the best performance in Dice score (higher than 90%), while the DRIU network achieves the smallest computation time (2 to 6 seconds) for a single segmentation on average. The source code of this study is publicly available at https://github.com/kennyha85/Liver-segmentation. Keywords: Liver segmentations, CNNs, Connected Components, Post processing Reference [1] K.A. McGlynn, J.L. Petrick, W.T. London, Global epidemiology of hepatocellular carcinoma: An emphasis on demographic and regional variability. Clinics in liver disease 19(2) (2015) 223-238. [2] M. Mohammadian, N. Mahdavifar, A. Mohammadian-Hafshejani, H. Salehiniya, Liver cancer in the world: epidemiology, incidence, mortality and risk factors, World Cancer Res J. 5(2) (2018) e1082. [3] T.T. Hong, N. Phuong Hoa, S.M. Walker, P.S. Hill, C. Rao, Completeness and reliability of mortality data in Viet Nam: Implications for the national routine health management information system, PloS one 13(1) 2018) e0190755. https://doi.org/10.1371/journal.pone.0190755. [4] T. Pham, L. Bui, G. Kim, D. Hoang, T. Tran, M. Hoang, Cancers in Vietnam-Burden and Control Efforts: A Narrative Scoping Review. Cancer Control 26(1) (2019) 1073274819863802. [5] M. Borner, M. Castiglione, J. Triller, H.U. Baer, M. Soucek, L. Blumgart, K. Brunner, Arena: Considerable side effects of chemoembolization for colorectal carcinoma metastatic to the liver, Annals of oncology 3(2) (1992) 113-115. [6] K. Memon, R.J. Lewandowski, L. Kulik, A. Riaz, M.F. Mulcahy, R. Salem, Radioembolization for primary and metastatic liver cancer, In Seminars in radiation oncology, WB Saunders. 21(4) (2011) 294-302. [7] I. Gory, M. Fink, S. Bell, P. Gow, A. Nicoll, V. Knight, W. Kemp, Radiofrequency ablation versus resection for the treatment of early stage hepatocellular carcinoma: A multicenter Australian study, Scandinavian journal of gastroenterology 50(5) (2015) 567-576. [8] H.M. Luu, C. Klink, W. Niessen, A. Moelker, T. Van Walsum, Non-rigid registration of liver CT images for CT-guided ablation of liver tumors. PloS one, 11(9) 92016) e0161600. [9] G. Gunay, M.H. Luu, A. Moelker, T. Van Walsum, S. Klein, Semiautomated registration of pre‐and intraoperative CT for image‐guided percutaneous liver tumor ablation interventions, Medical physics 44(7) (2017) 3718-3725. [10] A. Gotra, L. Sivakumaran, G. Chartrand, N. Vu, F. Vandenbroucke-Menu, C. Kauffmann, A. Tang, Liver segmentation: Indications, techniques and future directions, Insights into imaging 8(4) (2017) 377-392. https://doi.org/10.1007/s13244-017-0558-1. [11] T. Heimann, B. Van Ginneken, M.A. Styner, Y. Arzhaeva, V. Aurich, C. Bauer, F. Bello, Comparison and evaluation of methods for liver segmentation from CT datasets, IEEE transactions on medical imaging 28(8) (2009) 1251-1265. [12] O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation, In International Conference on Medical image computing and computer-assisted intervention, Springer, Cham, 2015, pp. 234-241. [13] F. Milletari, N. Navab, S.A. Ahmadi, October, V-net: Fully convolutional neural networks for volumetric medical image segmentation, In 2016 Fourth International Conference on 3D Vision (3DV) IEEE, 2016, pp. 565-571. [14] P.F. Christ, F. Ettlinger, F. Grün, M.A. Elshaera, J. Lipkova, S. Schlecht, M. Rempfler, Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks, arXiv preprint arXiv:1702.05970, 2017. [15] P.F. Christ, M.E.A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, H. Sommer, Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields. In International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, 2016, pp. 415-423. [16] H. Meine, G. Chlebus, M. Ghafoorian, I. Endo, A. Schenk, Comparison of U-net-based Convolutional Neural Networks for Liver Segmentation in CT. arXiv preprint arXiv, 2018, pp. 1810.04017. [17] X. Li, H. Chen, X. Qi, Q. Dou, C.W. Fu, P.A. Heng, H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes, IEEE transactions on medical imaging, 37(12) (2018) 2663-2674. [18] M. Bellver, K.K. Maninis, J. Pont-Tuset, X. Giró-i-Nieto, J. Torres, L. Van Gool, Detection-aided liver lesion segmentation using deep learning, ArXiv preprint arXiv:1711.11069, 2017. [19] H.S. Hoang, C.P. Pham, D. Franklin, T. Van Walsum, M.H. Luu, An Evaluation of CNN-based Liver Segmentation Methods using Multi-types of CT Abdominal Images from Multiple Medical Centers, In 2019 19th International Symposium on Communications and Information Technologies (ISCIT), IEEE, September, 2019, pp. 20-25. [20] H. Samet, M. Tamminen, Efficient component labeling of images of arbitrary dimension represented by linear bintrees, IEEE Transactions on Pattern Analysis and Machine Intelligence, 10(4) (1988) 579-586. [21] P. Bilic, P.F. Christ, E. Vorontsov, G. Chlebus, H. Chen, Q. Dou, S. Kadoury, The liver tumor segmentation benchmark (lits), ArXiv preprint arXiv, 2019, 1901.04056. [22] H.M. Luu, A. Moelker, S. Klein, W. Niessen, T. Van Walsum, Quantification of nonrigid liver deformation in radiofrequency ablation interventions using image registration, Physics in Medicine & Biology 63(17) (2018) 175005. [23] A.A. Novikov, D. Major, M. Wimmer, D. Lenis, K. Bühler, Deep Sequential Segmentation of Organs in Volumetric Medical Scans, IEEE transactions on medical imaging, 2018. [24] Y. Huo, J.G. Terry, J. Wang, S. Nair, A. Lasko, B.I. Freedman, B.A. Landman, Fully Automatic Liver Attenuation Estimation combing CNN Segmentation and Morphological Operations, Medical physics, 2019. [25] N. Gruber, S. Antholzer, W. Jaschke, C. Kremser, M. Haltmeier, A Joint Deep Learning Approach for Automated Liver and Tumor Segmentation, ArXiv preprint arXiv, 2019, pp. 1902.07971. [26] S. Chen, K. Ma, Y. Zheng, Med3D: Transfer Learning for 3D Medical Image Analysis, ArXiv preprint arXiv, 2019, pp. 1904.00625. [27] W. Tang, D. Zou, S. Yang, J. Shi, DSL: Automatic Liver Segmentation with Faster R-CNN and DeepLab, In International Conference on Artificial Neural Networks, Springer, Cham, 2018, pp. 137-147.

Objective(s): Copper (Cu) is an essential dietary supplement in animal feeds, which plays an important role in maintaining the balance of all living organisms. Copper nanoparticles (nCu) participate in catalysing activities of multiple antioxidant/defensive enzymes and exerts pro-inflammatory and pro-apoptotic effects on systemic organs and tissues. The present study explored whether nCu affects maize growth and yield and grain mineral nutrients as well as physiological functions in mice. Materials and Methods: Maize seeds were treated with nCu (20 mg/kg and 1000 mg/kg dry weight (DW)) and their grain productions were used for mouse feed. For testing of autoimmune response, mice were treated with nCu at concentration of 2 mg/l and 1000 mg/l and ultimately serum biochemical indicators, numbers and activation of immune cells infiltrated in mouse spleens were examined. Results: Treatment of maize seeds with nCu at dose of 20 mg/kg DW, but not 1000 mg/kg DW enhanced germination rate, plant growth and grain yield as well as grain mineral nutrients as compared to control group. Importantly, administration of mice with 1000 mg/l nCu resulted in their morphological change due to excessive accumulation of nCu in liver and blood, leading to inflammatory responses involved in upregulated expression of serum biochemical indicators of liver and kidney as well as increased infiltration and activation of splenic immune cells. Conclusion: nCu concentration at 20 mg/kg DW facilitated the morphological and functional development of maize plants, whose production was safe to feed mice.

Aim: To create a simple survival score for patients with high-grade gliomas based on clinical symptoms and performance status. Patients and Methods: Thirty-six patients received neurosurgical intervention followed by radiochemotherapy for high-grade gliomas. Six pre-treatment symptoms were included in the score depending on their impairment of quality of life, scoring each between 1 and 3. For each patient, the points from the symptoms were added and another 4 points were added for Karnofsky performance status (KPS)

Breast cancer is the most common cancer in women worldwide. It is also the leading cause of death among women globally.

Aim To evaluate the value of radiosurgery with a rotating gamma-system (RGS) for cerebral cavernomas. Patients and methods Seventy-nine patients with symptomatic cerebral cavernomas underwent RGS radiosurgery at the Bach Mai Hospital, Hanoi, Vietnam. Median dose (single fraction) was 20 Gy (range=14-26 Gy). Endpoints included effect on headache, seizures and tumor size. Results Of 60 patients with headache, 17% had complete response, 82% partial response and 2% stable disease (best response). Of 39 patients with seizures, 31% had complete response, 64% partial response and 5% stable disease. Four patients developed recurrent seizures after 1 year. Regarding the size of cavernoma at 15 months, complete response was observed in 6%, partial response in 75%, stable disease in 15%, progression in 1% and pseudo-progression in 3% of patients. Bleeding within 2 years after RGS radiosurgery occurred in only five patients (6%). RGS dose had no significant impact on outcomes. Conclusion RGS radiosurgery provided very high rates of symptom relief in patients with cerebral cavernomas.

To determine the optimal dose of radiosurgery-alone for patients with 1-3 cerebral metastases from breast cancer.Patients receiving 20 Gy (n=20) were compared to those receiving 16-18.5 Gy (n=10) for local control, distant brain control and overall survival. Seven other variables were also evaluated.Radiosurgery dose achieved significance on univariate (p=0.002; log-rank and Wilcoxon test) and multivariate analysis (p=0.004) of local control. Twelve-month local control rates were 94% after 20 Gy and 48% after 16-18.5 Gy. On univariate analysis of distant brain control, radiosurgery dose was not a significant factor, with 12-month rates of 73% and 60%, respectively. Regarding overall survival, radiosurgery dose was of borderline significance (p=0.059; Wilcoxon test). Twelve-month overall survival rates were 75% and 40%, respectively. On Cox regression analysis, radiosurgery dose exhibited a trend for improving survival (p=0.10).Radiosurgery with 20 Gy resulted in significantly better local control and led to a trend towards improved overall survival compared to treatment with 16-18.5 Gy.