Jing Hang

468 total citations
21 papers, 323 citations indexed

About

Jing Hang is a scholar working on Endocrinology, Diabetes and Metabolism, Pulmonary and Respiratory Medicine and Oncology. According to data from OpenAlex, Jing Hang has authored 21 papers receiving a total of 323 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Endocrinology, Diabetes and Metabolism, 5 papers in Pulmonary and Respiratory Medicine and 5 papers in Oncology. Recurrent topics in Jing Hang's work include Radiomics and Machine Learning in Medical Imaging (5 papers), Thyroid Cancer Diagnosis and Treatment (4 papers) and AI in cancer detection (3 papers). Jing Hang is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (5 papers), Thyroid Cancer Diagnosis and Treatment (4 papers) and AI in cancer detection (3 papers). Jing Hang collaborates with scholars based in China. Jing Hang's co-authors include Xinhua Ye, Jing Deng, Feihong Yu, Jianxiang Wang, Yun Liu, Ming Wang, Wei Wei, Yuan Cao, Chen Liang and Jiaheng Xie and has published in prestigious journals such as Scientific Reports, Frontiers in Immunology and Redox Biology.

In The Last Decade

Jing Hang

19 papers receiving 320 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jing Hang China 10 177 101 62 55 47 21 323
Jieun Koh South Korea 13 189 1.1× 70 0.7× 64 1.0× 68 1.2× 27 0.6× 25 394
Ping Xing China 10 150 0.8× 69 0.7× 87 1.4× 33 0.6× 106 2.3× 17 387
Youichi Machida Japan 14 193 1.1× 44 0.4× 50 0.8× 89 1.6× 28 0.6× 37 370
Toshiyuki Ishiba Japan 9 163 0.9× 123 1.2× 75 1.2× 68 1.2× 38 0.8× 35 331
Luca Basso Italy 11 181 1.0× 33 0.3× 36 0.6× 80 1.5× 22 0.5× 25 282
Qiyu Zhao China 10 200 1.1× 136 1.3× 20 0.3× 44 0.8× 14 0.3× 18 329
Qiugen Hu China 11 188 1.1× 50 0.5× 36 0.6× 136 2.5× 37 0.8× 35 477
Kristine E. Fasmer Norway 14 293 1.7× 33 0.3× 80 1.3× 79 1.4× 70 1.5× 40 588
Shaoqi Fan United States 9 132 0.7× 142 1.4× 69 1.1× 47 0.9× 40 0.9× 17 318

Countries citing papers authored by Jing Hang

Since Specialization
Citations

This map shows the geographic impact of Jing Hang'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 Hang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jing Hang more than expected).

Fields of papers citing papers by Jing Hang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jing Hang. 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 Hang. The network helps show where Jing Hang may publish in the future.

Co-authorship network of co-authors of Jing Hang

This figure shows the co-authorship network connecting the top 25 collaborators of Jing Hang. A scholar is included among the top collaborators of Jing Hang 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 Hang. Jing Hang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Yu, Jian, Hong Wang, Meijing Zhou, et al.. (2025). Impact of ultrasound‐diagnosed lipohypertrophy subtypes on insulin regimen adjustments in patients with T1DM. Diabetic Medicine. 42(6). e70034–e70034.
2.
Liu, Chao, Jing Yuan, Qiaoling Wang, et al.. (2023). The Effect of Diabetes Management Shared Care Clinic on Glycated Hemoglobin A1c Compliance and Self-Management Abilities in Patients with Type 2 Diabetes Mellitus. International Journal of Clinical Practice. 2023. 1–10. 3 indexed citations
3.
4.
Xie, Jiaheng, Dan Wu, Yuan Cao, et al.. (2023). Transcriptome analysis reveals tumor antigen and immune subtypes of melanoma. Oncology Research Featuring Preclinical and Clinical Cancer Therapeutics. 31(3). 389–403. 3 indexed citations
5.
Chen, Xin, Danni Chen, Li Jiang, et al.. (2023). The clinical value of serum xanthine oxidase levels in patients with acute ischemic stroke. Redox Biology. 60. 102623–102623. 26 indexed citations
6.
Yu, Feihong, Shumei Miao, Cuiying Li, et al.. (2023). Pretreatment ultrasound-based deep learning radiomics model for the early prediction of pathologic response to neoadjuvant chemotherapy in breast cancer. European Radiology. 33(8). 5634–5644. 25 indexed citations
7.
Xie, Jiaheng, Chen Liang, Wei Wei, et al.. (2022). A Necroptosis-Related Prognostic Model of Uveal Melanoma Was Constructed by Single-Cell Sequencing Analysis and Weighted Co-Expression Network Analysis Based on Public Databases. Frontiers in Immunology. 13. 847624–847624. 55 indexed citations
8.
Yu, Hailong, Beilei Chen, Jing Hang, et al.. (2022). Correlation Between Plasma Levels of RIP3 and Acute Ischemic Stroke with Large-Artery Atherosclerosis. Current Neurovascular Research. 19(1). 30–37. 4 indexed citations
9.
10.
Wu, Mengjie, et al.. (2021). Qualitative and Quantitative Contrast-Enhanced Ultrasound Combined with Conventional Ultrasound for Predicting the Malignancy of Soft Tissue Tumors. Ultrasound in Medicine & Biology. 48(2). 237–247. 10 indexed citations
11.
Wu, Mengjie, et al.. (2021). Nomogram Based on Ultrasonography and Clinical Features for Predicting Malignancy in Soft Tissue Tumors. Cancer Management and Research. Volume 13. 2143–2152. 6 indexed citations
12.
Ye, Heng, Jing Hang, Meimei Zhang, et al.. (2021). Automatic identification of triple negative breast cancer in ultrasonography using a deep convolutional neural network. Scientific Reports. 11(1). 20474–20474. 14 indexed citations
13.
Yu, Feihong, Jing Hang, Jing Deng, et al.. (2021). Radiomics features on ultrasound imaging for the prediction of disease-free survival in triple negative breast cancer: a multi-institutional study. British Journal of Radiology. 94(1126). 20210188–20210188. 31 indexed citations
14.
Hang, Jing, Jie Chen, Weixin Zhang, et al.. (2021). Correlation between elastic modulus and clinical severity of pathological scars: a cross-sectional study. Scientific Reports. 11(1). 23324–23324. 9 indexed citations
15.
Ye, Heng, Jing Hang, Xiaowei Chen, et al.. (2020). An intelligent platform for ultrasound diagnosis of thyroid nodules. Scientific Reports. 10(1). 13223–13223. 18 indexed citations
16.
Xu, Ting, Jing Hang, Jianxiang Wang, et al.. (2019). Diagnostic value of pyruvate kinase M2 gene for papillary thyroid carcinoma in fine-needle aspiration specimens. Zhonghua neifenmi daixie zazhi. 35(4). 276–281. 1 indexed citations
17.
Yu, Feihong, et al.. (2019). Ultrasound-based radiomics nomogram: A potential biomarker to predict axillary lymph node metastasis in early-stage invasive breast cancer. European Journal of Radiology. 119. 108658–108658. 84 indexed citations
18.
Hang, Jing, Fan Li, Xiao-Hui Qiao, et al.. (2018). Combination of Maximum Shear Wave Elasticity Modulus and TIRADS Improves the Diagnostic Specificity in Characterizing Thyroid Nodules: A Retrospective Study. International Journal of Endocrinology. 2018. 1–8. 19 indexed citations
19.
20.
Hang, Jing, et al.. (2017). Investigation of the maximum Young's modulus of thyroid nodules using two-dimensional shear wave elastography in thyroid nodules. Biomedical Research-tokyo. 28(8). 3537–3544. 2 indexed citations

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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