J
Junfeng Lu
Researcher at Fudan University
Publications - 47
Citations - 985
Junfeng Lu is an academic researcher from Fudan University. The author has contributed to research in topics: Glioma & Resting state fMRI. The author has an hindex of 15, co-authored 47 publications receiving 567 citations.
Papers
More filters
Book ChapterDOI
Pre-operative Overall Survival Time Prediction for Glioblastoma Patients Using Deep Learning on Both Imaging Phenotype and Genotype
Zhenyu Tang,Yuyun Xu,Zhicheng Jiao,Junfeng Lu,Lei Jin,Abudumijiti Aibaidula,Jinsong Wu,Qian Wang,Han Zhang,Dinggang Shen +9 more
TL;DR: A new deep learning based method that can derive genotype related features from pre-operative multimodal MR images of brain tumor patients to guide OS time prediction is proposed and it is concluded that the multi-task learning can effectively improve the accuracy of predicting OS time in personalized prognosis.
Journal ArticleDOI
Long-Term Functional and Oncologic Outcomes of Glioma Surgery with and without Intraoperative Neurophysiologic Monitoring: A Retrospective Cohort Study in a Single Center
Nan Zhang,Zhengda Yu,N U Farrukh Hameed,Geng Xu,Yan-Yan Song,Bin Wu,Jie Zhang,Tianming Qiu,Dongxiao Zhuang,Junfeng Lu,Jinsong Wu +10 more
TL;DR: Application of IONM is beneficial to long-term functional and oncologic outcomes of patients with glioma after intraoperative neurophysiologic monitoring (IONM) application.
Book ChapterDOI
Multi-label Inductive Matrix Completion for Joint MGMT and IDH1 Status Prediction for Glioma Patients
TL;DR: This paper proposes a novel Multi-label Inductive Matrix Completion (MIMC) model, highlighted by the online inductive learning strategy, to jointly predict both MGMT and IDH1 statuses, and shows the promise of utilizing imaging-derived brain connectome phenotypes for prognosis of HGG in a non-invasive manner.
Journal Article
[Awake craniotomy and intraoperative language cortical mapping for eloquent cerebral glioma resection: preliminary clinical practice in 3.0 T intraoperative magnetic resonance imaging integrated surgical suite].
Junfeng Lu,Jie Zhang,Jinsong Wu,Chengjun Yao,Dongxiao Zhuang,Tianming Qiu,Xiu Gong,Geng Xu,Ying Mao,Liangfu Zhou +9 more
TL;DR: The combination of high-field iMRI and awake craniotomy may facilitate safe removal of eloquent glioma and can be performed safely and effectively within a 3.0 T iMRI suite.
Journal ArticleDOI
Multivariate machine learning-based language mapping in glioma patients based on lesion topography.
TL;DR: In this paper, lesion topography data from 137 preoperative patients with left cerebral language-network gliomas (81 low-grade and 56 high-grade glioma) were adopted for multivariate machine-learning-based lesion-language mapping analysis.