Xiaoguang Cheng

4.2k total citations
140 papers, 2.7k citations indexed

About

Xiaoguang Cheng is a scholar working on Orthopedics and Sports Medicine, Surgery and Physiology. According to data from OpenAlex, Xiaoguang Cheng has authored 140 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 86 papers in Orthopedics and Sports Medicine, 64 papers in Surgery and 26 papers in Physiology. Recurrent topics in Xiaoguang Cheng's work include Bone health and osteoporosis research (74 papers), Hip and Femur Fractures (37 papers) and Bone and Joint Diseases (29 papers). Xiaoguang Cheng is often cited by papers focused on Bone health and osteoporosis research (74 papers), Hip and Femur Fractures (37 papers) and Bone and Joint Diseases (29 papers). Xiaoguang Cheng collaborates with scholars based in China, United Kingdom and United States. Xiaoguang Cheng's co-authors include Margaret S. Steffensen, Wei Shen, Wei Tian, Thomas Lang, Hongjuan Fang, Elizabeth Berg, Na Li, Joyce H. Keyak, Zhe Guo and Yongbin Su and has published in prestigious journals such as Scientific Reports, Radiology and Spine.

In The Last Decade

Xiaoguang Cheng

131 papers receiving 2.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiaoguang Cheng China 29 1.2k 957 560 507 413 140 2.7k
Mikael Boesen Denmark 34 1.0k 0.8× 1.2k 1.3× 267 0.5× 711 1.4× 309 0.7× 209 3.6k
Karen Knapp United Kingdom 26 655 0.5× 409 0.4× 359 0.6× 295 0.6× 295 0.7× 111 1.9k
Gary M. Kiebzak United States 34 1.7k 1.3× 1.5k 1.6× 350 0.6× 319 0.6× 112 0.3× 94 3.3k
Andrew J. Grainger United Kingdom 38 1.5k 1.2× 2.8k 2.9× 187 0.3× 849 1.7× 462 1.1× 150 6.0k
Edwin H. G. Oei Netherlands 36 1.5k 1.2× 2.1k 2.2× 149 0.3× 1.0k 2.0× 542 1.3× 172 4.3k
Tamara Vokes United States 25 956 0.8× 1.3k 1.3× 296 0.5× 213 0.4× 135 0.3× 90 2.8k
Marcello Henrique Nogueira‐Barbosa Brazil 28 564 0.5× 1.3k 1.3× 132 0.2× 536 1.1× 456 1.1× 195 2.6k
E. Michael Lewiecki United States 16 1.6k 1.3× 684 0.7× 398 0.7× 136 0.3× 110 0.3× 19 2.2k
I. Watt United Kingdom 38 872 0.7× 1.9k 2.0× 208 0.4× 552 1.1× 180 0.4× 114 5.1k
John Edmonds Australia 36 493 0.4× 972 1.0× 145 0.3× 308 0.6× 233 0.6× 105 4.8k

Countries citing papers authored by Xiaoguang Cheng

Since Specialization
Citations

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

Fields of papers citing papers by Xiaoguang Cheng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiaoguang Cheng

This figure shows the co-authorship network connecting the top 25 collaborators of Xiaoguang Cheng. A scholar is included among the top collaborators of Xiaoguang 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 Xiaoguang Cheng. Xiaoguang Cheng 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.
Ma, Yimin, Yufeng Ge, Zhe Guo, et al.. (2025). Hip structural analysis parameters are not associated with the risk of postmenopausal female second hip fracture: a retrospective study. BMC Musculoskeletal Disorders. 26(1). 233–233.
2.
Cheng, Xiaoguang, et al.. (2025). A convolutional neural networks with spatial configuration and attention mechanism for Tanner-Whitehouse 3 bone age assessment. Biomedical Signal Processing and Control. 112. 108728–108728.
3.
Wang, Ling, Yandong Liu, Kai Li, et al.. (2024). Age and BMI have different effects on subcutaneous, visceral, liver, bone marrow, and muscle adiposity, as measured by CT and MRI. Obesity. 32(7). 1339–1348. 2 indexed citations
4.
Liu, Yandong, et al.. (2024). Comparisons of Hounsfield units and volumetric bone density in discriminating vertebral fractures on lumbar CT scans. British Journal of Radiology. 97(1157). 1003–1009. 2 indexed citations
5.
6.
Yin, Lu, et al.. (2023). Site-Specific Differences in Bone Mineral Density of Proximal Femur Correlate with the Type of Hip Fracture. Diagnostics. 13(11). 1877–1877. 3 indexed citations
7.
Wang, Ling, Minghui Yang, Yufeng Ge, et al.. (2023). Muscle size and density are independently associated with death after hip fracture: A prospective cohort study. Journal of Cachexia Sarcopenia and Muscle. 14(4). 1824–1835. 20 indexed citations
8.
9.
Ge, Yufeng, Yandong Liu, Yi Yuan, et al.. (2023). Associations of Quantitative and Qualitative Muscle Parameters With Second Hip Fracture Risk in Older Women: A Prospective Cohort Study. JBMR Plus. 7(12). e10834–e10834. 3 indexed citations
10.
Yu, Aihong, Yandong Liu, Ling Wang, et al.. (2023). The lumbar spinal endplate lesions grades and association with lumbar disc disorders, and lumbar bone mineral density in a middle-young general Chinese population. BMC Musculoskeletal Disorders. 24(1). 258–258. 3 indexed citations
11.
Fu, Chen, Dong Yan, Ling Wang, et al.. (2023). High prevalence of sarcopenia and myosteatosis in patients undergoing hemodialysis. Frontiers in Endocrinology. 14. 1117438–1117438. 8 indexed citations
12.
Han, Xiao, Xiaodong Ma, Donghang Li, et al.. (2022). Application of Neurite Orientation Dispersion and Density Imaging to Evaluate and Predict the Surgical Outcome for Degenerative Cervical Myelopathy. Orthopaedic Surgery. 14(7). 1482–1488. 2 indexed citations
13.
Li, Xintong, et al.. (2022). Deep learning for automatic segmentation of paraspinal muscle on computed tomography. Acta Radiologica. 64(2). 596–604. 7 indexed citations
14.
Pan, Yaling, et al.. (2020). Automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening. European Radiology. 30(7). 4107–4116. 87 indexed citations
15.
Han, Xiao, et al.. (2020). The Evaluation and Prediction of Laminoplasty Surgery Outcome in Patients with Degenerative Cervical Myelopathy Using Diffusion Tensor MRI. American Journal of Neuroradiology. 41(9). 1745–1753. 15 indexed citations
16.
Yin, Lu, Kai Li, Bo Hu, et al.. (2020). Relationships between anthropometric adiposity indexes and bone mineral density in a cross-sectional Chinese study. The Spine Journal. 21(2). 332–342. 14 indexed citations
17.
Yin, Lu, et al.. (2020). Muscle density, but not size, correlates well with muscle performance. Bone Reports. 13. 100350–100350. 1 indexed citations
18.
Cheng, Xiaoguang, Aihong Yu, Ling Wang, et al.. (2017). Quantitative radiological evaluation of interaction of lumbar vertebral bone marrow fat, bone mineral density and age. Zhonghua fangshexian yixue zazhi. 51(10). 771–776. 1 indexed citations
19.
Guglielmi, Giuseppe, et al.. (2016). Bone densitometry: current status and future trends. 64(3). 97–103. 2 indexed citations
20.
Su, Yongbin, Xiaoguang Cheng, Ling Wang, & Yimin Ma. (2016). Predicting hip fracture type of elderly Asian patients with low-energy fall by volumetric BMD and femoral morphology from QCT. Bone Abstracts. 1 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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