Ming‐Huang Hong

9.8k total citations · 1 hit paper
79 papers, 3.5k citations indexed

About

Ming‐Huang Hong is a scholar working on Otorhinolaryngology, Oncology and Surgery. According to data from OpenAlex, Ming‐Huang Hong has authored 79 papers receiving a total of 3.5k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Otorhinolaryngology, 36 papers in Oncology and 29 papers in Surgery. Recurrent topics in Ming‐Huang Hong's work include Head and Neck Cancer Studies (47 papers), Head and Neck Surgical Oncology (22 papers) and Viral-associated cancers and disorders (14 papers). Ming‐Huang Hong is often cited by papers focused on Head and Neck Cancer Studies (47 papers), Head and Neck Surgical Oncology (22 papers) and Viral-associated cancers and disorders (14 papers). Ming‐Huang Hong collaborates with scholars based in China, United States and Sweden. Ming‐Huang Hong's co-authors include Hai-Qiang Mai, Jun Ma, Xiang Guo, Ming‐Yuan Chen, Hua-Qing Min, Ling Guo, Hao‐Yuan Mo, Yi-Jun Hua, Pei‐Yu Huang and Tai-Xiang Lu and has published in prestigious journals such as Journal of Clinical Oncology, PLoS ONE and JNCI Journal of the National Cancer Institute.

In The Last Decade

Ming‐Huang Hong

77 papers receiving 3.5k citations

Hit Papers

Intensity-modulated radiotherapy prolongs the survival of... 2015 2026 2018 2022 2015 50 100 150 200

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ming‐Huang Hong China 31 2.1k 1.6k 1.4k 848 703 79 3.5k
Hai-Qiang Mai China 29 1.4k 0.7× 1.3k 0.8× 922 0.7× 627 0.7× 753 1.1× 74 2.9k
Fady Geara Lebanon 30 1.1k 0.5× 1.3k 0.8× 938 0.7× 964 1.1× 571 0.8× 103 3.4k
Ai-Hua Lin China 30 2.0k 1.0× 1.4k 0.9× 1.3k 1.0× 1.0k 1.2× 284 0.4× 75 3.2k
Alexandre Bozec France 29 867 0.4× 845 0.5× 1.1k 0.8× 833 1.0× 702 1.0× 158 2.9k
Bon Seok Koo South Korea 32 864 0.4× 899 0.5× 1.5k 1.1× 321 0.4× 783 1.1× 156 3.1k
Peter Rhŷs‐Evans United Kingdom 32 839 0.4× 1.0k 0.6× 657 0.5× 747 0.9× 679 1.0× 68 2.7k
Sana D. Karam United States 32 703 0.3× 1.7k 1.0× 826 0.6× 888 1.0× 668 1.0× 162 3.3k
Akihiro Homma Japan 30 1.6k 0.8× 963 0.6× 1.3k 1.0× 725 0.9× 319 0.5× 270 3.1k
Stuart J. Wong United States 28 1.1k 0.5× 1.4k 0.9× 880 0.6× 1.2k 1.4× 476 0.7× 99 2.8k
Hendrik A. Wolff Germany 31 573 0.3× 1.2k 0.7× 828 0.6× 835 1.0× 632 0.9× 106 2.6k

Countries citing papers authored by Ming‐Huang Hong

Since Specialization
Citations

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

Fields of papers citing papers by Ming‐Huang Hong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ming‐Huang Hong. 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 Ming‐Huang Hong. The network helps show where Ming‐Huang Hong may publish in the future.

Co-authorship network of co-authors of Ming‐Huang Hong

This figure shows the co-authorship network connecting the top 25 collaborators of Ming‐Huang Hong. A scholar is included among the top collaborators of Ming‐Huang Hong 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 Ming‐Huang Hong. Ming‐Huang Hong 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
2.
Wu, Dawei, Meimei Chen, Jing Liang, et al.. (2022). Consensus on informed consent for participants in cancer clinical studies (2021 edition). Asia-Pacific Journal of Oncology Nursing. 9(11). 100130–100130. 4 indexed citations
3.
Yang, Qi, Meng-Xia Zhang, Xiong Zou, et al.. (2017). A Prognostic Bio-Model Based on SQSTM1 and N-Stage Identifies Nasopharyngeal Carcinoma Patients at High Risk of Metastasis for Additional Induction Chemotherapy. Clinical Cancer Research. 24(3). 648–658. 26 indexed citations
4.
Zhang, Lu, Huai Liu, Lin-Quan Tang, et al.. (2016). Prognostic effect of pregnancy on young female patients with nasopharyngeal carcinoma: results from a matched cohort analysis. Oncotarget. 7(16). 21913–21921. 4 indexed citations
5.
Ye, Yan-Fang, Yan‐Qun Xiang, Fang Fang, et al.. (2015). Hepatitis B Virus Infection and Risk of Nasopharyngeal Carcinoma in Southern China. Cancer Epidemiology Biomarkers & Prevention. 24(11). 1766–1773. 26 indexed citations
6.
Yang, Qi, Huan‐Xin Lin, Shu‐Biao Wu, et al.. (2015). Prostate Tumor Overexpressed 1 (PTOV1) Is a Novel Prognostic Marker for Nasopharyngeal Carcinoma Progression and Poor Survival Outcomes. PLoS ONE. 10(8). e0136448–e0136448. 16 indexed citations
7.
Lin, Xiao, Shang‐Hang Xie, Jie Guo, et al.. (2015). Secular trend analysis of lung cancer incidence in Sihui city, China between 1987 and 2011. Chinese Journal of Cancer. 34(3). 365–72. 8 indexed citations
8.
Huang, Pei‐Yu, Qi Zeng, Ka-Jia Cao, et al.. (2015). Ten-year outcomes of a randomised trial for locoregionally advanced nasopharyngeal carcinoma: A single-institution experience from an endemic area. European Journal of Cancer. 51(13). 1760–1770. 40 indexed citations
10.
Chen, Ming‐Yuan, Hai-Qiang Mai, Rui Sun, et al.. (2013). Clinical findings and imaging features of 67 nasopharyngeal carcinoma patients with postradiation nasopharyngeal necrosis. Chinese Journal of Cancer. 32(10). 533–538. 38 indexed citations
12.
Xia, Yu, et al.. (2011). Epidemiological study of epstein-barr virus infection for nasopharyngeal carcinoma screening in high-incidence areas. Clinical Oncology and Cancer Research. 38(24). 1551–1554. 1 indexed citations
13.
Cao, Su-Mei, Zhiwei Liu, Wei-Hua Jia, et al.. (2011). Fluctuations of Epstein-Barr Virus Serological Antibodies and Risk for Nasopharyngeal Carcinoma: A Prospective Screening Study with a 20-Year Follow-Up. PLoS ONE. 6(4). e19100–e19100. 123 indexed citations
14.
Liu, Xuekui, et al.. (2009). Long-term effect of submandibular salivary gland transfer on radiation-induced xerostomia in patients with nasopharyngeal carcinoma. Clinical Oncology and Cancer Research. 36(24). 1384–1387. 1 indexed citations
15.
Mao, Yan‐Ping, Wen-Fei Li, Lei Chen, et al.. (2009). A clinical verification of the Chinese 2008 staging system for nasopharyngeal carcinoma. Chinese Journal of Cancer. 28(10). 1022–1028. 23 indexed citations
16.
Hua, Yi‐Jun, Ming‐Huang Hong, Xiaowei Yang, et al.. (2006). Application of Support Vector Machine to Predict 5-year Survival Status of Patients with Nasopharyngeal Carcinoma after Treatment. The Chinese-German Journal of Clinical Oncology. 5(1). 8–12. 3 indexed citations
17.
Hong, Ming‐Huang. (2005). Shared Decision Making. Journal of Evidence-Based Medicine.
18.
Hua, Yi‐Jun, Ming‐Huang Hong, & Dong–Hua Luo. (2005). Multivariable analysis of prognostic predictors for 406 patients with nasopharyngeal carcinoma. Clinical Oncology and Cancer Research. 32(8). 435–438. 2 indexed citations
19.
Liu, Xuekui, et al.. (2005). Clinical Analysis of Xerostomia in Patients with Nasopharyngeal Carcinoma after Radiation Therapy. The Chinese-German Journal of Clinical Oncology. 4(3). 137–140. 5 indexed citations
20.
Qian, Chao‐Nan, Changqing Zhang, Xiang Guo, et al.. (2000). Elevation of serum vascular endothelial growth factor in male patients with metastatic nasopharyngeal carcinoma. Cancer. 88(2). 255–261. 69 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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