Chunhao Wang

1.7k total citations
59 papers, 1.1k citations indexed

About

Chunhao Wang is a scholar working on Radiology, Nuclear Medicine and Imaging, Radiation and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Chunhao Wang has authored 59 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Radiology, Nuclear Medicine and Imaging, 20 papers in Radiation and 14 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Chunhao Wang's work include Radiomics and Machine Learning in Medical Imaging (30 papers), Medical Imaging Techniques and Applications (20 papers) and Advanced Radiotherapy Techniques (20 papers). Chunhao Wang is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (30 papers), Medical Imaging Techniques and Applications (20 papers) and Advanced Radiotherapy Techniques (20 papers). Chunhao Wang collaborates with scholars based in United States, China and Hong Kong. Chunhao Wang's co-authors include F Yin, Julian C. Hong, Kyle J. Lafata, Dandan Zheng, Xiaofeng Zhu, Yushi Chang, Yang Sheng, Jiahan Zhang, Zheng Chang and Yaorong Ge and has published in prestigious journals such as PLoS ONE, International Journal of Radiation Oncology*Biology*Physics and Magnetic Resonance in Medicine.

In The Last Decade

Chunhao Wang

58 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chunhao Wang United States 20 653 396 284 217 163 59 1.1k
Yong Yin China 21 1.1k 1.7× 608 1.5× 799 2.8× 255 1.2× 145 0.9× 195 1.8k
Avishek Chatterjee Netherlands 17 815 1.2× 121 0.3× 283 1.0× 247 1.1× 340 2.1× 38 1.2k
Belal Ahmad Canada 18 251 0.4× 267 0.7× 415 1.5× 95 0.4× 210 1.3× 46 1.1k
Michele Avanzo Italy 22 1.3k 2.0× 418 1.1× 719 2.5× 400 1.8× 253 1.6× 63 1.8k
Thomas Wendler Germany 20 373 0.6× 181 0.5× 148 0.5× 401 1.8× 82 0.5× 75 1.1k
Tyler Bradshaw United States 19 1.1k 1.7× 179 0.5× 276 1.0× 372 1.7× 119 0.7× 63 1.4k
Ana María Barragán Montero Belgium 18 587 0.9× 613 1.5× 482 1.7× 164 0.8× 149 0.9× 47 1.1k
Lise Wei United States 13 627 1.0× 53 0.1× 218 0.8× 197 0.9× 176 1.1× 24 860
Michael Jameson Australia 19 938 1.4× 882 2.2× 482 1.7× 210 1.0× 99 0.6× 76 1.3k
Jiawei Sun China 17 410 0.6× 68 0.2× 128 0.5× 146 0.7× 194 1.2× 54 747

Countries citing papers authored by Chunhao Wang

Since Specialization
Citations

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

Fields of papers citing papers by Chunhao Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chunhao Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Chunhao Wang. A scholar is included among the top collaborators of Chunhao Wang 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 Chunhao Wang. Chunhao Wang 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.
Gao, Yuan, Chunhao Wang, Yvonne M. Mowery, et al.. (2024). Radiomics on spatial‐temporal manifolds via Fokker–Planck dynamics. Medical Physics. 51(5). 3334–3347. 3 indexed citations
2.
Chen, Min‐Bin, Weiwei Sang, Lu Ke, et al.. (2024). A dual-radiomics model for overall survival prediction in early-stage NSCLC patient using pre-treatment CT images. Frontiers in Oncology. 14. 1419621–1419621. 1 indexed citations
3.
Wang, Lili, Hongying Zhang, Yinhui Liu, et al.. (2023). Microbiome Structure and Mucosal Morphology of Jejunum Appendix and Colon of Rats in Health and Dysbiosis. Current Microbiology. 80(4). 127–127. 1 indexed citations
4.
Yang, Zhenyu, Hangjie Ji, Kyle J. Lafata, et al.. (2023). A neural ordinary differential equation model for visualizing deep neural network behaviors in multi‐parametric MRI‐based glioma segmentation. Medical Physics. 50(8). 4825–4838. 14 indexed citations
5.
Lafata, Kyle J., Betty C. Tong, Tomi Akinyemiju, et al.. (2023). Lung Cancer Screening in Clinical Practice: A 5-Year Review of Frequency and Predictors of Lung Cancer in the Screened Population. Journal of the American College of Radiology. 21(5). 767–777. 3 indexed citations
6.
Yang, Zhenyu, et al.. (2023). A radiomics-incorporated deep ensemble learning model for multi-parametric MRI-based glioma segmentation. Physics in Medicine and Biology. 68(18). 185025–185025. 3 indexed citations
7.
Adamson, Justus, Chunhao Wang, Yunfeng Cui, et al.. (2023). Generation, validation, and benchmarking of a commercial independent Monte Carlo calculation beam model for multi-target SRS. Zeitschrift für Medizinische Physik. 35(3). 248–258. 7 indexed citations
8.
Yang, Zhenyu, Kyle J. Lafata, Eugene Vaios, et al.. (2023). Quantifying U‐Net uncertainty in multi‐parametric MRI‐based glioma segmentation by spherical image projection. Medical Physics. 51(3). 1931–1943. 10 indexed citations
9.
Vaios, Eugene, Peter G. Hendrickson, Donna Niedzwiecki, et al.. (2023). Long-Term Intracranial Outcomes With Combination Dual Immune-Checkpoint Blockade and Stereotactic Radiosurgery in Patients With Melanoma and Non-Small Cell Lung Cancer Brain Metastases. International Journal of Radiation Oncology*Biology*Physics. 118(5). 1507–1518. 9 indexed citations
10.
Yang, Zhenyu, Chunhao Wang, Yuqi Wang, et al.. (2023). Development of a multi-feature-combined model: proof-of-concept with application to local failure prediction of post-SBRT or surgery early-stage NSCLC patients. Frontiers in Oncology. 13. 1185771–1185771. 3 indexed citations
11.
Ruan, Li, et al.. (2022). Cloud Workload Turning Points Prediction via Cloud Feature-Enhanced Deep Learning. IEEE Transactions on Cloud Computing. 11(2). 1719–1732. 17 indexed citations
12.
Ji, Hangjie, Kyle J. Lafata, Yvonne M. Mowery, et al.. (2022). Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application. Frontiers in Oncology. 12. 895544–895544. 10 indexed citations
13.
Ge, Yaorong, et al.. (2022). Input feature design and its impact on the performance of deep learning models for predicting fluence maps in intensity-modulated radiation therapy. Physics in Medicine and Biology. 67(21). 215009–215009. 1 indexed citations
14.
Lafata, Kyle J., Michael N. Corradetti, Junheng Gao, et al.. (2021). Radiogenomic Analysis of Locally Advanced Lung Cancer Based on CT Imaging and Intratreatment Changes in Cell-Free DNA. Radiology Imaging Cancer. 3(4). e200157–e200157. 35 indexed citations
15.
Chang, Yushi, et al.. (2020). Digital phantoms for characterizing inconsistencies among radiomics extraction toolboxes. Biomedical Physics & Engineering Express. 6(2). 25016–25016. 20 indexed citations
16.
Wang, Chunhao, Chenyang Liu, Yushi Chang, et al.. (2020). Dose-Distribution-Driven PET Image-Based Outcome Prediction (DDD-PIOP): A Deep Learning Study for Oropharyngeal Cancer IMRT Application. Frontiers in Oncology. 10. 1592–1592. 23 indexed citations
17.
Chang, Yushi, Kyle J. Lafata, Wenzheng Sun, et al.. (2019). An investigation of machine learning methods in delta-radiomics feature analysis. PLoS ONE. 14(12). e0226348–e0226348. 41 indexed citations
18.
Wang, Chunhao, Ergys Subashi, F Yin, Zheng Chang, & Jing Cai. (2018). A Spatiotemporal-Constrained Sorting Method for Motion-Robust 4D-MRI: A Feasibility Study. International Journal of Radiation Oncology*Biology*Physics. 103(3). 758–766. 5 indexed citations
19.
Yin, F, et al.. (2017). Accelerating volumetric cine MRI (VC-MRI) using undersampling for real-time 3D target localization/tracking in radiation therapy: a feasibility study. Physics in Medicine and Biology. 63(1). 01NT01–01NT01. 19 indexed citations
20.
Horton, Janet K., Rachel Blitzblau, Sua Yoo, et al.. (2015). Preoperative Single-Fraction Partial Breast Radiation Therapy: A Novel Phase 1, Dose-Escalation Protocol With Radiation Response Biomarkers. International Journal of Radiation Oncology*Biology*Physics. 92(4). 846–855. 101 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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