Nai‐Ming Cheng

495 total citations
18 papers, 225 citations indexed

About

Nai‐Ming Cheng is a scholar working on Otorhinolaryngology, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Nai‐Ming Cheng has authored 18 papers receiving a total of 225 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Otorhinolaryngology, 11 papers in Radiology, Nuclear Medicine and Imaging and 9 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Nai‐Ming Cheng's work include Radiomics and Machine Learning in Medical Imaging (11 papers), Head and Neck Cancer Studies (11 papers) and Medical Imaging Techniques and Applications (5 papers). Nai‐Ming Cheng is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (11 papers), Head and Neck Cancer Studies (11 papers) and Medical Imaging Techniques and Applications (5 papers). Nai‐Ming Cheng collaborates with scholars based in Taiwan, United States and China. Nai‐Ming Cheng's co-authors include Tzu‐Chen Yen, Shu‐Hang Ng, Chun‐Ta Liao, Chien‐Yu Lin, Hung‐Ming Wang, Din‐Li Tsan, Cheng–Lung Hsu, Joseph Tung‐Chieh Chang, Sheng-Chieh Chan and Chien-Yu Lin and has published in prestigious journals such as Clinical Cancer Research, International Journal of Cancer and Medicine.

In The Last Decade

Nai‐Ming Cheng

15 papers receiving 225 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nai‐Ming Cheng Taiwan 10 169 108 72 61 33 18 225
Aasheesh Kanwar United States 4 148 0.9× 78 0.7× 53 0.7× 38 0.6× 36 1.1× 7 190
Silvia Rosellini France 4 80 0.5× 61 0.6× 35 0.5× 54 0.9× 43 1.3× 5 144
Max Rohde Denmark 10 104 0.6× 161 1.5× 74 1.0× 90 1.5× 74 2.2× 23 275
Linda Kašaová Czechia 9 94 0.6× 43 0.4× 108 1.5× 87 1.4× 35 1.1× 22 256
Imran Petkar United Kingdom 8 80 0.5× 137 1.3× 122 1.7× 82 1.3× 50 1.5× 17 280
Zongrui Ma China 8 192 1.1× 27 0.3× 79 1.1× 32 0.5× 25 0.8× 13 236
Roelof J. Beukinga Netherlands 7 328 1.9× 47 0.4× 140 1.9× 154 2.5× 90 2.7× 9 378
Ce Han China 12 232 1.4× 33 0.3× 169 2.3× 72 1.2× 32 1.0× 23 346
Dechun Zheng China 11 340 2.0× 167 1.5× 58 0.8× 56 0.9× 84 2.5× 25 412
Terunaga Inage Japan 10 83 0.5× 56 0.5× 282 3.9× 65 1.1× 37 1.1× 47 366

Countries citing papers authored by Nai‐Ming Cheng

Since Specialization
Citations

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

Fields of papers citing papers by Nai‐Ming Cheng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nai‐Ming Cheng

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

All Works

18 of 18 papers shown
1.
Chan, Sheng-Chieh, Shu‐Hang Ng, Chih‐Hua Yeh, et al.. (2025). Prognostic utility of 18F-FDG PET/MRI with intravoxel incoherent motion imaging in nasopharyngeal carcinoma. European Journal of Nuclear Medicine and Molecular Imaging. 53(1). 338–349. 1 indexed citations
2.
Chen, Po‐Lin, Nai‐Ming Cheng, Chien‐Yu Lin, et al.. (2024). Postradiotherapy Response Assessment Using 18F-FDG PET/CT in Salivary Gland Carcinoma—A Multicenter Study. Clinical Nuclear Medicine. 50(2). e68–e79.
3.
4.
Cheng, Nai‐Ming, Chien‐Yu Lin, Chun‐Ta Liao, et al.. (2023). The added values of 18F-FDG PET/CT in differentiating cancer recurrence and osteoradionecrosis of mandible in patients with treated oral squamous cell carcinoma. EJNMMI Research. 13(1). 25–25. 2 indexed citations
5.
Hsieh, Cheng‐En, Yung‐Chih Chou, Chia‐Yen Hung, et al.. (2023). A multicenter retrospective analysis of patients with salivary gland carcinoma treated with postoperative radiotherapy alone or chemoradiotherapy. Radiotherapy and Oncology. 188. 109891–109891. 5 indexed citations
6.
Wang, Hung‐Ming, Pei‐Wei Huang, Chien-Yu Lin, et al.. (2023). Cetuximab plus methotrexate in recurrent and/or metastatic head-and-neck squamous cell carcinoma. Journal of Cancer Research and Practice. 10(3). 101–101.
7.
8.
Shen, Eric Yi-Liang, Tsung‐Min Hung, Din‐Li Tsan, et al.. (2022). Utilization of the lymph node-to-primary tumor ratio of PET standardized uptake value and circulating Epstein–Barr virus DNA to predict distant metastasis in nasopharyngeal carcinoma. Radiotherapy and Oncology. 177. 1–8. 7 indexed citations
9.
Cheng, Nai‐Ming, Jiawen Yao, Jinzheng Cai, et al.. (2021). Deep Learning for Fully Automated Prediction of Overall Survival in Patients with Oropharyngeal Cancer Using FDG-PET Imaging. Clinical Cancer Research. 27(14). 3948–3959. 36 indexed citations
10.
Chan, Sheng-Chieh, Nai‐Ming Cheng, Chun‐Ta Liao, et al.. (2020). Pretreatment 18F-FDG PET/CT texture parameters provide complementary information to Epstein-Barr virus DNA titers in patients with metastatic nasopharyngeal carcinoma. Oral Oncology. 104. 104628–104628. 10 indexed citations
11.
Cheng, Nai‐Ming, Cheng‐En Hsieh, Yu-Hua Fang, et al.. (2020). Development and validation of a prognostic model incorporating [18F]FDG PET/CT radiomics for patients with minor salivary gland carcinoma. EJNMMI Research. 10(1). 74–74. 9 indexed citations
12.
Chan, Sheng-Chieh, Shu‐Hang Ng, Chia‐Hsun Hsieh, et al.. (2019). Textural features on 18F-FDG PET/CT and dynamic contrast-enhanced MR imaging for predicting treatment response and survival of patients with hypopharyngeal carcinoma. Medicine. 98(33). e16608–e16608. 11 indexed citations
13.
Hsieh, Cheng‐En, Nai‐Ming Cheng, Wen‐Chi Chou, et al.. (2018). Pretreatment Primary Tumor and Nodal SUVmax Values on 18F-FDG PET/CT Images Predict Prognosis in Patients With Salivary Gland Carcinoma. Clinical Nuclear Medicine. 43(12). 869–879. 16 indexed citations
14.
15.
Wang, Hung‐Ming, Nai‐Ming Cheng, Li‐Yu Lee, et al.. (2015). Heterogeneity of 18F‐FDG PET combined with expression of EGFR may improve the prognostic stratification of advanced oropharyngeal carcinoma. International Journal of Cancer. 138(3). 731–738. 19 indexed citations
16.
Cheng, Nai‐Ming, Joseph Tung‐Chieh Chang, Chung‐Guei Huang, et al.. (2012). Prognostic value of pretreatment 18F-FDG PET/CT and human papillomavirus type 16 testing in locally advanced oropharyngeal squamous cell carcinoma. European Journal of Nuclear Medicine and Molecular Imaging. 39(11). 1673–1684. 43 indexed citations
17.
Cheng, Nai‐Ming, Kung‐Chu Ho, Shu‐Hang Ng, et al.. (2011). False Positive F-18 FDG PET/CT in Neck and Mediastinum Lymph Nodes Due to Anthracosis in a Buccal Cancer Patient. Clinical Nuclear Medicine. 36(10). 963–964. 12 indexed citations
18.
Cheng, Nai‐Ming, Chih‐Teng Yu, Kung‐Chu Ho, et al.. (2008). Respiration-averaged CT for attenuation correction in non-small-cell lung cancer. European Journal of Nuclear Medicine and Molecular Imaging. 36(4). 607–615. 16 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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