详情页 - 四川省肿瘤医院知识库

资源类型：

文章类型：

作者：

; Lombardo, E.[1] ; Zschaek, S.[3] ; Weingaertner, J.[3] ; Holzgreve, A.[4] ; Albert, N.[4] ; Marschner, S.[1] ; Avanzo, M.[5] ; Fanetti, G.[6] ; Franchin, G.[6] ; Stancanello, J.[7] ; Walter, F.[8] ; Corradini, S.[1] ; Niyazi, M.[1] ; Belka, C.[1] ; Riboldi, M.[9] ; Kurz, C.[1] ; Landry, G.[1] ;

机构： [1]University Hospital, LMU Munich, Radiation Oncology, Munich, Germany [2]Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology, Chengdu, China [3]Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Radiation Oncology, Berlin, Germany [4]University Hospital, LMU Munich, Nuclear Medicine, Munich, Germany [5]Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Medical Physics, Aviano, Italy [6]Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Radiation Oncology, Aviano, Italy [7]ELEKTA SAS, Clinical Applications Development, Boulogne-Billancourt, France [8]University Hospital, LMU Munich, Radiation Oncology, Munich, Germany [9]Faculty of Physics, Ludwig-Maximilians-Universität München, Medical Physics, Garching, Germany

出处：

ISSN：

语种：

WOS：

中科院(CAS)分区：

出版当年[2022]版：

大类 | 1 区

医学

小类 | 2 区核医学 2 区肿瘤学

最新[2023]版：

大类 | 1 区

医学

小类 | 2 区肿瘤学 2 区核医学

JCR分区：

出版当年[2022]版：

Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Q2 ONCOLOGY

最新[2023]版：

Q1 ONCOLOGY Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

第一作者：

第一作者机构： [1]University Hospital, LMU Munich, Radiation Oncology, Munich, Germany [2]Sichuan Cancer Hospital, School of Medicine, University of Electronic Science and Technology of China, Radiation Oncology, Chengdu, China

推荐引用方式(GB/T 7714)：

APA：

MLA：

Deep learning based time to event analysis with PET, CT and joint PET/CT for H&N cancer prognosis

文献详情