Jing Ou
About
Jing Ou is a scholar working on Radiology, Nuclear Medicine and Imaging, Surgery and Pulmonary and Respiratory Medicine.
According to data from OpenAlex, Jing Ou has authored 27 papers receiving a total of 239 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Radiology, Nuclear Medicine and Imaging, 15 papers in Surgery and 6 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Jing Ou's work include Radiomics and Machine Learning in Medical Imaging (17 papers), Esophageal Cancer Research and Treatment (13 papers) and Pancreatic and Hepatic Oncology Research (6 papers). Jing Ou is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (17 papers), Esophageal Cancer Research and Treatment (13 papers) and Pancreatic and Hepatic Oncology Research (6 papers). Jing Ou collaborates with scholars based in China, United States and France. Jing Ou's co-authors include Tian‐wu Chen, Rui Li, Xiao Ming Zhang, Yu Xu, Zezhang Tao, Wendan Shi, Yonggang Kong, Jiani Hu, Shiming Chen and Jun Wang and has published in prestigious journals such as Scientific Reports, Medicine and European Radiology.
In The Last Decade
Jing Ou
26 papers receiving 237 citations
Peers
Jing Ou
S Namikawa Japan
Karin Söderkvist Sweden
Yue Du China
Masaya Uchida Japan
William Mullally Ireland
Finbar Slevin United Kingdom
Maree O'Brien Australia
Ricardo García‐Luján Spain
Zhitian Wang China
Shoichi Onoda Japan
Citations per year, relative to Jing Ou Jing Ou (= 1×)
peers
S Namikawa
Countries citing papers authored by Jing Ou
Since
Specialization
Citations
This map shows the geographic impact of Jing Ou's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jing Ou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jing Ou more than expected).
Fields of papers citing papers by Jing Ou
Since
Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences
This network shows the impact of papers produced by Jing Ou. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jing Ou. The network helps show where Jing Ou may publish in the future.
Co-authorship network of co-authors of Jing Ou
This figure shows the co-authorship network connecting the top 25 collaborators of Jing Ou. A scholar is included among the top collaborators of Jing Ou based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jing Ou. Jing Ou is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
1.
Chen, Tian‐wu, et al.. (2024). A Systematic Review and Meta-Analysis of MRI Radiomics for PredictingMicrovascular Invasion in Patients with Hepatocellular Carcinoma. Current Medical Imaging Formerly Current Medical Imaging Reviews. 20. e15734056256824–e15734056256824.
2.
Ding, Xuemei, et al.. (2024). CT radiomics based on the peritumoral adipose region of gastric adenocarcinoma for preoperative prediction of lymph node metastasis. European Journal of Radiology. 175. 111479–111479.
3.
Ou, Jing, et al.. (2024). Baseline CT radiomics features to predict pathological complete response of advanced esophageal squamous cell carcinoma treated with neoadjuvant chemotherapy using paclitaxel and cisplatin. European Journal of Radiology. 181. 111763–111763.
4.
Gao, Dan, Xiaoqian Chen, Jing Ou, et al.. (2024). Contrast-enhanced CT radiomics features to preoperatively identify differences between tumor and proximal tumor-adjacent and tumor-distant tissues of resectable esophageal squamous cell carcinoma. Cancer Imaging. 24(1). 11–11.
5.
Zhou, Haiying, Wenwen Guo, Jing Ou, et al.. (2023). A CT-based novel model to predict pathological complete response of locally advanced esophageal squamous cell carcinoma to neoadjuvant PD-1 blockade in combination with chemotherapy. European Journal of Radiology. 167. 111065–111065.
6.
Tang, Zhao, Jing Ou, Haiying Zhou, et al.. (2023). An improved model based on quantitative features of right liver lobe, maximum varices, and portal vein system measured on magnetic resonance imaging to predict oesophagogastric variceal haemorrhage secondary to hepatitis B-related cirrhosis. Quantitative Imaging in Medicine and Surgery. 13(12). 7741–7752.
7.
Ou, Jing, et al.. (2023). Preoperative CT radiomics of esophageal squamous cell carcinoma and lymph node to predict nodal disease with a high diagnostic capability. European Journal of Radiology. 170. 111197–111197.
8.
Zhou, Haiying, et al.. (2023). CT radiomics for prediction of microvascular invasion in hepatocellular carcinoma: A systematic review and meta-analysis. Clinics. 78. 100264–100264.
9.
Ou, Jing, Haiying Zhou, Dan Gao, et al.. (2022). Gross tumor volume of adenocarcinoma of esophagogastric junction corresponding to cT and cN stages measured with computed tomography to quantitatively determine resectabiliy: A case control study. Frontiers in Oncology. 12. 1038135–1038135.
10.
Tang, Zhao, Jing Ou, Haiying Zhou, et al.. (2022). A novel model based on liver/spleen volumes and portal vein diameter on MRI to predict variceal bleeding in HBV cirrhosis. European Radiology. 33(2). 1378–1387.
11.
Ou, Jing, Changqiang Wu, Tian‐wu Chen, et al.. (2021). Application of contrast-enhanced CT radiomics in prediction of early recurrence of locally advanced oesophageal squamous cell carcinoma after trimodal therapy. Cancer Imaging. 21(1). 38–38.
12.
Ou, Jing, Rui Li, Changqiang Wu, et al.. (2020). CT radiomics features to predict lymph node metastasis in advanced esophageal squamous cell carcinoma and to discriminate between regional and non-regional lymph node metastasis: a case control study. Quantitative Imaging in Medicine and Surgery. 11(2). 628–640.
13.
Yang, Jianqiong, Jialong Wu, Liqin Yang, et al.. (2020). A radiomics model of liver CT to predict risk of hepatic encephalopathy secondary to hepatitis B related cirrhosis. European Journal of Radiology. 130. 109201–109201.
14.
Chen, Yanli, Yu Jiang, Tian‐wu Chen, et al.. (2019). Assessing Microcirculation in Resectable Oesophageal Squamous Cell Carcinoma with Dynamic Contrast-enhanced MRI for Identifying Primary tumour and Lymphatic Metastasis. Scientific Reports. 9(1). 124–124.
15.
Chen, Yanli, Rui Li, Tian‐wu Chen, et al.. (2019). Whole-tumour histogram analysis of pharmacokinetic parameters from dynamic contrast-enhanced MRI in resectable oesophageal squamous cell carcinoma can predict T-stage and regional lymph node metastasis. European Journal of Radiology. 112. 112–120.
16.
Ou, Jing, Rui Li, Changqiang Wu, et al.. (2019). CT radiomic features for predicting resectability of oesophageal squamous cell carcinoma as given by feature analysis: a case control study. Cancer Imaging. 19(1). 66–66.
17.
Yu, Jiang, Yanli Chen, Tian‐wu Chen, et al.. (2018). Is there association of gross tumor volume of adenocarcinoma of oesophagogastric junction measured on magnetic resonance imaging with N stage?. European Journal of Radiology. 110. 181–186.
18.
Li, Jie, et al.. (2018). Magnetic Resonance Diffusion Kurtosis Imaging for Evaluating Stage of Liver Fibrosis in a Rabbit Model. Academic Radiology. 26(6). e90–e97.
19.
Ou, Jing, Tian‐wu Chen, Rui Li, et al.. (2018). Tumour volume of resectable oesophageal squamous cell carcinoma measured with MRI correlates well with T category and lymphatic metastasis. European Radiology. 28(11). 4757–4765.
20.
Ou, Jing, Wendan Shi, Yu Xu, & Zezhang Tao. (2014). Intranasal immunization with DNA vaccine coexpressing Der p 1 and ubiquitin in an allergic rhinitis mouse model. Annals of Allergy Asthma & Immunology. 113(6). 658–665.e1.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
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