Soonmee Cha

20.3k total citations · 4 hit papers
168 papers, 12.5k citations indexed

About

Soonmee Cha is a scholar working on Radiology, Nuclear Medicine and Imaging, Genetics and Neurology. According to data from OpenAlex, Soonmee Cha has authored 168 papers receiving a total of 12.5k indexed citations (citations by other indexed papers that have themselves been cited), including 117 papers in Radiology, Nuclear Medicine and Imaging, 111 papers in Genetics and 30 papers in Neurology. Recurrent topics in Soonmee Cha's work include Glioma Diagnosis and Treatment (111 papers), MRI in cancer diagnosis (76 papers) and Advanced MRI Techniques and Applications (62 papers). Soonmee Cha is often cited by papers focused on Glioma Diagnosis and Treatment (111 papers), MRI in cancer diagnosis (76 papers) and Advanced MRI Techniques and Applications (62 papers). Soonmee Cha collaborates with scholars based in United States, France and South Korea. Soonmee Cha's co-authors include Glyn Johnson, Mitchel S. Berger, Edmond A. Knopp, Ramon Barajas, David Zagzag, Michael W. McDermott, Sarah J. Nelson, Meng Law, Javier Villanueva-Meyer and Marc C. Mabray and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.

In The Last Decade

Soonmee Cha

166 papers receiving 12.3k citations

Hit Papers

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What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Role of Extent of Resection in the Long-Term Outcome of Low-Grade Hemispheric Gliomas

2008 904 citations Susan M. Chang, Michael D. Prados et al. profile →
Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging

2003 803 citations Meng Law, Stanley Yang et al. American Journal of Neuroradiology profile →
Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas

2018 325 citations Soonmee Cha et al. American Journal of Neuroradiology profile →
Current Clinical Brain Tumor Imaging

2017 278 citations Javier Villanueva-Meyer, Soonmee Cha et al. Neurosurgery profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Soonmee Cha	7.7k	7.7k	1.9k	1.8k	1.8k	168	12.5k
Larry Junck	3.6k 0.5×	3.9k 0.5×	2.0k 1.1×	1.8k 1.0×	872 0.5×	180	9.4k
Glyn Johnson	7.6k 1.0×	3.3k 0.4×	917 0.5×	1.6k 0.9×	1.3k 0.7×	141	10.8k
Michael A. Vogelbaum	3.2k 0.4×	8.1k 1.1×	4.1k 2.2×	1.6k 0.9×	2.5k 1.4×	239	12.8k
Michael W. McDermott	3.1k 0.4×	6.4k 0.8×	3.0k 1.6×	2.1k 1.2×	3.0k 1.7×	137	10.3k
Thomas L. Chenevert	12.0k 1.5×	2.5k 0.3×	2.9k 1.5×	1.1k 0.6×	1.5k 0.9×	290	17.4k
Benjamin M. Ellingson	4.8k 0.6×	4.4k 0.6×	1.2k 0.6×	602 0.3×	632 0.4×	304	8.3k
David R. Macdonald	2.8k 0.4×	8.1k 1.1×	3.2k 1.7×	1.6k 0.9×	1.7k 0.9×	77	9.7k
Jong Hee Chang	2.1k 0.3×	3.5k 0.5×	1.2k 0.7×	1.6k 0.9×	1.4k 0.8×	320	7.8k
Philip H. Gutin	3.1k 0.4×	7.1k 0.9×	5.0k 2.7×	2.5k 1.4×	3.5k 2.0×	240	14.5k
Christina Tsien	4.3k 0.6×	5.0k 0.7×	2.8k 1.5×	553 0.3×	1.1k 0.6×	156	9.4k

Countries citing papers authored by Soonmee Cha

Since Specialization

Citations

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

Fields of papers citing papers by Soonmee Cha

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Soonmee Cha

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

All Works

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20 of 20 papers shown

Farrell, Edward J., et al.. (2025). Persistent MRI Abnormalities After Successful Treatment of Subarachnoid and Intraventricular Neurocysticercosis. Neurology Clinical Practice. 15(6). e200553–e200553.

Hu, Leland, Marion Smits, Timothy J. Kaufmann, et al.. (2024). Advanced Imaging in the Diagnosis and Response Assessment of High-Grade Glioma: AJR Expert Panel Narrative Review. American Journal of Roentgenology. 224(1). e2330612–e2330612. 4 indexed citations

Isikbay, Masis, M. Travis Caton, Jared Narvid, et al.. (2024). Deep learning segmentation-based bone removal from computed tomography of the brain improves subdural hematoma detection. Journal of Neuroradiology. 52(1). 101231–101231. 1 indexed citations

Kulaç, İbrahim, et al.. (2023). Pathological perspectives in pilocytic astrocytomas: Extent of resection as the sole critical factor for recurrence-free survival, and the challenge of evaluating conclusions derived from limited data. SHILAP Revista de lepidopterología. 4. 17–17. 2 indexed citations

Rudie, Jeffrey D., Evan Calabrese, David Weiß, et al.. (2022). Longitudinal Assessment of Posttreatment Diffuse Glioma Tissue Volumes with Three-dimensional Convolutional Neural Networks. Radiology Artificial Intelligence. 4(5). e210243–e210243. 14 indexed citations

Zeevi, Tal, MingDe Lin, Ichiro Ikuta, et al.. (2022). Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities. Cancers. 14(11). 2623–2623. 9 indexed citations

Calabrese, Evan, Jeffrey D. Rudie, Andreas M. Rauschecker, et al.. (2022). Combining radiomics and deep convolutional neural network features from preoperative MRI for predicting clinically relevant genetic biomarkers in glioblastoma. Neuro-Oncology Advances. 4(1). 56 indexed citations

Calabrese, Evan, Javier Villanueva-Meyer, & Soonmee Cha. (2020). A fully automated artificial intelligence method for non-invasive, imaging-based identification of genetic alterations in glioblastomas. Scientific Reports. 10(1). 11852–11852. 63 indexed citations

Panditharatna, Eshini, Lindsay Kilburn, Mariam Aboian, et al.. (2018). Clinically Relevant and Minimally Invasive Tumor Surveillance of Pediatric Diffuse Midline Gliomas Using Patient-Derived Liquid Biopsy. Clinical Cancer Research. 24(23). 5850–5859. 123 indexed citations

10.

Behr, Spencer C., Javier Villanueva-Meyer, Yan Li, et al.. (2018). Targeting iron metabolism in high-grade glioma with 68Ga-citrate PET/MR. JCI Insight. 3(21). 21 indexed citations

11.

Ehman, Eric C., Geoffrey B. Johnson, Javier Villanueva-Meyer, et al.. (2017). PET/MRI: Where might it replace PET/CT?. Journal of Magnetic Resonance Imaging. 46(5). 1247–1262. 180 indexed citations

12.

Barajas, Ramon, Javier Villanueva‐Meyer, Arie Perry, Mitchel S. Berger, & Soonmee Cha. (2017). Biologically aggressive regions within glioblastoma identified by spin-lock contrast T1 relaxation in the rotating frame (T1ρ) MRI. Radiology Case Reports. 12(4). 827–832. 5 indexed citations

13.

Kadoch, Cigall, Jing Li, Lingjing Chen, et al.. (2013). Complement Activation and Intraventricular Rituximab Distribution in Recurrent Central Nervous System Lymphoma. Clinical Cancer Research. 20(4). 1029–1041. 30 indexed citations

14.

Wieduwilt, Matthew J., Francisco Valles, Samar Issa, et al.. (2012). Immunochemotherapy with Intensive Consolidation for Primary CNS Lymphoma: A Pilot Study and Prognostic Assessment by Diffusion-Weighted MRI. Clinical Cancer Research. 18(4). 1146–1155. 99 indexed citations

15.

Barajas, Ramon, Arie Perry, Michael E. Sughrue, Manish K. Aghi, & Soonmee Cha. (2011). Intracranial subdural osteoma: A rare benign tumor that can be differentiated from other calcified intracranial lesions utilizing MR imaging. Journal of Neuroradiology. 39(4). 263–266. 11 indexed citations

16.

Diehn, Maximilian, Christine Nardini, David S. Wang, et al.. (2008). Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proceedings of the National Academy of Sciences. 105(13). 5213–5218. 345 indexed citations

17.

Ware, Marcus, Yuichi Hirose, Bernd W. Scheithauer, et al.. (2007). GENETIC ABERRATIONS IN GLIOMATOSIS CEREBRI. Neurosurgery. 60(1). 150–158. 23 indexed citations

18.

Li, Yan, Joseph A. Osorio, Esin Öztürk-Işık, et al.. (2006). Considerations in applying 3D PRESS H-1 brain MRSI with an eight-channel phased-array coil at 3 T. Magnetic Resonance Imaging. 24(10). 1295–1302. 26 indexed citations

19.

Law, Meng, Stanley Yang, Hao Wang, et al.. (2003). Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. American Journal of Neuroradiology. 24(10). 1989–1998. 803 indexed citations breakdown →

20.

Cha, Soonmee, Stanley Lu, Glyn Johnson, & Edmond A. Knopp. (2000). Dynamic susceptibility contrast MR imaging: Correlation of signal intensity changes with cerebral blood volume measurements. Journal of Magnetic Resonance Imaging. 11(2). 114–119. 57 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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