J
Jing Li
Researcher at Zhengzhou University
Publications - 42
Citations - 656
Jing Li is an academic researcher from Zhengzhou University. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 8, co-authored 27 publications receiving 240 citations. Previous affiliations of Jing Li include Brigham and Women's Hospital.
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Journal ArticleDOI
Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study.
Di Dong,Mengjie Fang,Lei Tang,Xiuhong Shan,Jianbo Gao,Francesco Giganti,Francesco Giganti,R.-P. Wang,X. Chen,Xiaoxiao Wang,Diego Palumbo,Jia Fu,W.-C. Li,Jing Li,Lianzhen Zhong,F. De Cobelli,Jiafu Ji,Zaiyi Liu,Jie Tian,Jie Tian,Jie Tian +20 more
TL;DR: A deep learning-based radiomic nomogram built based on the images from multi-phase computed tomography for preoperatively determining the number of lymph node metastasis in locally advanced gastric cancer (LAGC) had good predictive value for LNM in LAGC.
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Dual-energy CT–based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer
TL;DR: The DECT-based deep learning radiomics nomogram showed good performance in predicting LNM in gastric cancer and was significantly associated with patients’ prognosis.
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Deep-Learning Detection of Cancer Metastases to the Brain on MRI
Min Zhang,Geoffrey S. Young,Huai Chen,Huai Chen,Jing Li,Jing Li,Lei Qin,J Ricardo McFaline-Figueroa,David A. Reardon,Xinhua Cao,Xian Wu,Xiaoyin Xu +11 more
TL;DR: Progress in tumor treatment now requires detection of new or growing metastases at the small subcentimeter size, when these therapies are most effective.
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2D and 3D CT Radiomic Features Performance Comparison in Characterization of Gastric Cancer: A Multi-Center Study
TL;DR: This work comprehensively compared 2D and 3D radiomic features’ representation and discrimination capacity regarding GC, via three tasks involving lymph node metastasis’ prediction, lymphovascular invasion and modalities’ performances when resampling spacing different.
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Diagnostic accuracy of dual-energy CT-based nomograms to predict lymph node metastasis in gastric cancer
TL;DR: This study first developed and internally validated a dual-energy CT-based nomogram to predict lymph node metastasis in patients with gastric cancer and exhibited a significant prognostic ability for progression-free and overall survival.