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Management of Hepatocellular Carcinoma Patients with Portal Vein Thrombosis Using Three-Dimensional Conformal Radiotherapy

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Journal of Cancer Therapy 2017年第6期8卷 579-590页

作者： Ahmed Abo Gabal Mohamed Lotayef Hoda Abd Elbaky Maha Hassan Heba Elzawahry ekram hamed Department of Radiation Oncology National Cancer Institute Cairo University Cairo Egypt Department of Medical Oncology National Cancer Institute Cairo University Cairo Egypt Department of Radio-Diagnosis National Cancer Institute Cairo University Cairo Egypt

Objective: To determine the possible therapeutic gain of using three-dimensional conformal radiotherapy (3D-CRT) as a treatment option for portal vein tumor thrombus (PVTT) in patients with hepatocellular carcinoma (HCC) and to evaluate the tolerance and toxicity of using such treatment. Materials and methods: Sixty two patients were enrolled in this prospective study between June 2013 and August 2015. The clinical target volume (CTV) was the PVTT and the prescribed dose was 50 Gy/25 fractions. The median follow-up time was 7.4 months. Results: The thrombus crude response rate was 40.4% and the only significant prognostic factor for response was the thrombus size. Responders had significant better survival compared to non-responders with a median survival of 12.5 and 8 months respectively (P Conclusions: The results of this study suggest that radiotherapy should be considered as a safe treatment option for HCC patients with PVTT. It is effective not only for PVTT local control but also for survival, although prospective randomized trials are needed to confirm these results.

关键词： Hepatocellular Carcinoma Portal Vein Thrombosis 3D Conformal Radiotherapy Portal Vein Tumor Thrombus

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Deep Learning Methods Used in Remote Sensing Images: A Review

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Journal of Environmental & Earth Sciences 2023年第1期5卷 33-64页

作者： ekram M.Rewhel Jianqiang Li Amal A.hamed Hatem M.Keshk Amira S.Mahmoud Sayed A.Sayed Ehab Samir Hind H.Zeyada Sayed A.Mohamed Marwa S.Moustafa Ayman H.Nasr Ashraf K.Helmy School of Software Engineering Beijing University of Science and TechnologyBeijing100083China Data Reception Analysis and Receiving Station DivisionNational Authority for Remote Sensing and Space ScienceCairo1564Egypt

Undeniably,Deep Learning(DL)has rapidly eroded traditional machine learning in Remote Sensing(RS)and geoscience domains with applications such as scene understanding,material identification,extreme weather detection,oil spill identification,among many *** machine learning algorithms are given less and less attention in the era of big ***,a substantial amount of work aimed at developing image classification approaches based on the DL model’s success in computer *** number of relevant articles has nearly doubled every year since *** in remote sensing technology,as well as the rapidly expanding volume of publicly available satellite imagery on a worldwide scale,have opened up the possibilities for a wide range of modern ***,there are some challenges related to the availability of annotated data,the complex nature of data,and model parameterization,which strongly impact *** this article,a comprehensive review of the literature encompassing a broad spectrum of pioneer work in remote sensing image classification is presented including network architectures(vintage Convolutional Neural Network,CNN;Fully Convolutional Networks,FCN;encoder-decoder,recurrent networks;attention models,and generative adversarial models).The characteristics,capabilities,and limitations of current DL models were examined,and potential research directions were discussed.

关键词： Deep Learning(DL) Satellite imaging Image classification Segmentation and object detection

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