William Y. Song

2.6k total citations
119 papers, 1.9k citations indexed

About

William Y. Song is a scholar working on Radiation, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine. According to data from OpenAlex, William Y. Song has authored 119 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 91 papers in Radiation, 71 papers in Radiology, Nuclear Medicine and Imaging and 48 papers in Pulmonary and Respiratory Medicine. Recurrent topics in William Y. Song's work include Advanced Radiotherapy Techniques (91 papers), Medical Imaging Techniques and Applications (39 papers) and Radiation Therapy and Dosimetry (23 papers). William Y. Song is often cited by papers focused on Advanced Radiotherapy Techniques (91 papers), Medical Imaging Techniques and Applications (39 papers) and Radiation Therapy and Dosimetry (23 papers). William Y. Song collaborates with scholars based in United States, Canada and South Korea. William Y. Song's co-authors include Jerry Battista, Jake Van Dyk, Justin C. Park, Habib Safigholi, Steve Jiang, Glenn Bauman, Bongyong Song, Ruijiang Li, John H. Lewis and Ajay Sandhu and has published in prestigious journals such as PLoS ONE, Optics Letters and International Journal of Radiation Oncology*Biology*Physics.

In The Last Decade

William Y. Song

112 papers receiving 1.9k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
William Y. Song United States 26 1.4k 1.3k 782 540 211 119 1.9k
Jack Venselaar Netherlands 22 1.6k 1.1× 1.0k 0.8× 1.2k 1.5× 426 0.8× 142 0.7× 44 2.0k
Frank‐André Siebert Germany 22 1.2k 0.8× 700 0.5× 881 1.1× 247 0.5× 121 0.6× 77 1.6k
D. Brabbins United States 18 1.2k 0.8× 587 0.5× 1.1k 1.4× 269 0.5× 263 1.2× 41 1.7k
Noriyuki Kadoya Japan 24 1.2k 0.8× 1.1k 0.9× 941 1.2× 475 0.9× 42 0.2× 142 1.8k
Rajat J. Kudchadker United States 28 1.9k 1.3× 1.0k 0.8× 1.7k 2.2× 415 0.8× 59 0.3× 158 2.5k
Robin L. Stern United States 21 1.4k 1.0× 1.1k 0.8× 922 1.2× 376 0.7× 73 0.3× 62 1.8k
Ananth Ravi Canada 25 987 0.7× 442 0.3× 1.0k 1.3× 179 0.3× 218 1.0× 143 1.7k
Anne Richter Germany 24 1.9k 1.4× 1.5k 1.1× 1.4k 1.8× 413 0.8× 44 0.2× 61 2.2k
Bhudatt R. Paliwal United States 24 2.5k 1.8× 1.9k 1.5× 1.6k 2.1× 587 1.1× 63 0.3× 90 3.0k
Lakshmi Santanam United States 24 1.2k 0.9× 1.0k 0.8× 783 1.0× 222 0.4× 55 0.3× 72 1.5k

Countries citing papers authored by William Y. Song

Since Specialization
Citations

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

Fields of papers citing papers by William Y. Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by William Y. Song. 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 William Y. Song. The network helps show where William Y. Song may publish in the future.

Co-authorship network of co-authors of William Y. Song

This figure shows the co-authorship network connecting the top 25 collaborators of William Y. Song. A scholar is included among the top collaborators of William Y. Song 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 William Y. Song. William Y. Song 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.
Gautam, Shiva, et al.. (2024). Attention 3D UNET for dose distribution prediction of high‐dose‐rate brachytherapy of cervical cancer: Intracavitary applicators. Journal of Applied Clinical Medical Physics. 26(2). e14568–e14568. 2 indexed citations
2.
Palta, Jatinder, L. Yuan, J. Baker, et al.. (2024). Optimization of treatment workflow for 0.35T MR‐Linac system. Journal of Applied Clinical Medical Physics. 25(8). 1 indexed citations
3.
4.
Lee, Brandon, et al.. (2024). Automated daily dose accumulation workflow for treatment quality assurance during online adaptive radiotherapy with a 0.35T MR‐linac. Journal of Applied Clinical Medical Physics. 26(3). e14594–e14594.
5.
Manandhar, Bikash, Somayeh Gholami, Sarwar Alam, et al.. (2023). Direction Modulated Brachytherapy Tandem Model Applicators for Treatment Planning of Multi-Institutional Cervical Cancer Cases: Removing Needles in Intracavitary-Interstitial Techniques. International Journal of Radiation Oncology*Biology*Physics. 117(2). e529–e530. 3 indexed citations
6.
Gholami, Somayeh, Suman Gautam, Daniel J. Scanderbeg, et al.. (2023). PO19. Brachytherapy. 22(5). S73–S74. 2 indexed citations
7.
8.
Oh, Seungjong, et al.. (2019). Dynamic Modulated Brachytherapy (DMBT) Balloon Applicator for Accelerated Partial Breast Irradiation. International Journal of Radiation Oncology*Biology*Physics. 104(4). 953–961. 2 indexed citations
9.
Domchek, Susan M., Yung‐Jue Bang, George Coukos, et al.. (2016). MEDIOLA: A phase I/II, open-label trial of olaparib in combination with durvalumab (MEDI4736) in patients (pts) with advanced solid tumours. Annals of Oncology. 27. vi377–vi377. 6 indexed citations
10.
Bowers, Michael R., S.P. Robertson, Joseph Moore, et al.. (2015). SU‐E‐P‐26: Oncospace: A Shared Radiation Oncology Database System Designed for Personalized Medicine, Decision Support, and Research. Medical Physics. 42(6Part4). 3232–3232. 3 indexed citations
11.
Safigholi, Habib, William Y. Song, & Ali S. Meigooni. (2015). Optimum radiation source for radiation therapy of skin cancer. Journal of Applied Clinical Medical Physics. 16(5). 219–227. 25 indexed citations
12.
Murphy, James D., Jona A. Hattangadi‐Gluth, William Y. Song, et al.. (2014). Liver toxicity prediction with stereotactic body radiation therapy: The impact of accounting for fraction size. Practical Radiation Oncology. 4(6). 372–377. 3 indexed citations
13.
Paravati, Anthony J., Erin Healy, James D. Murphy, William Y. Song, & Jona A. Hattangadi‐Gluth. (2013). Stereotactic body radiation therapy for primary hepatic malignancies and metastases to liver: a technical and literature review. Translational Cancer Research. 2(6). 507–520. 3 indexed citations
14.
Sandhu, Ajay, Steven Lau, Douglas A. Rahn, et al.. (2013). Stereotactic Body Radiation Therapy in Octogenarians With Stage I Lung Cancer. Clinical Lung Cancer. 15(2). 131–135. 36 indexed citations
15.
Dević, Slobodan, T. Vuong, Dae Yup Han, et al.. (2012). Dynamic modulated brachytherapy (DMBT) for rectal cancer. Medical Physics. 40(1). 11718–11718. 41 indexed citations
16.
17.
Kim, Jin Sung, et al.. (2010). Optimizing Imaging Conditions in Digital Tomosynthesis for Image-Guided Radiation Therapy. 21(3). 281–290. 3 indexed citations
18.
Moiseenko, Vitali, William Y. Song, Loren K. Mell, & Niranjan Bhandare. (2010). MO‐EE‐A2‐03: Comparison of Four NTCP Models to Describe Dose‐Response for Radiation‐Induced Optic Neuropathy and Retinopathy. Medical Physics. 37(6Part26). 3348–3348. 1 indexed citations
19.
Schaly, B, Glenn Bauman, William Y. Song, Jerry Battista, & Jake Van Dyk. (2005). Dosimetric impact of image-guided 3D conformal radiation therapy of prostate cancer. Physics in Medicine and Biology. 50(13). 3083–3101. 54 indexed citations
20.
Song, William Y. & Peter Dunscombe. (2004). EUD‐based margin selection in the presence of set‐up uncertainties. Medical Physics. 31(4). 849–859. 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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