Suyash Mohan

7.4k total citations
153 papers, 2.8k citations indexed

About

Suyash Mohan is a scholar working on Radiology, Nuclear Medicine and Imaging, Genetics and Surgery. According to data from OpenAlex, Suyash Mohan has authored 153 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 67 papers in Radiology, Nuclear Medicine and Imaging, 49 papers in Genetics and 32 papers in Surgery. Recurrent topics in Suyash Mohan's work include Glioma Diagnosis and Treatment (49 papers), Radiomics and Machine Learning in Medical Imaging (31 papers) and MRI in cancer diagnosis (23 papers). Suyash Mohan is often cited by papers focused on Glioma Diagnosis and Treatment (49 papers), Radiomics and Machine Learning in Medical Imaging (31 papers) and MRI in cancer diagnosis (23 papers). Suyash Mohan collaborates with scholars based in United States, India and United Kingdom. Suyash Mohan's co-authors include Andreas M. Rauschecker, Jeffrey D. Rudie, C. C. Tchoyoson Lim, Amogh N. Hegde, R. Nick Bryan, Narayan Lath, Sanjeev Chawla, Christos Davatzikos, Michael Tran Duong and Harish Poptani and has published in prestigious journals such as Journal of Clinical Oncology, Neurology and Cancer.

In The Last Decade

Suyash Mohan

144 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
Suyash Mohan United States 29 1.2k 734 517 370 357 153 2.8k
Otto Rapalino United States 26 916 0.8× 375 0.5× 451 0.9× 267 0.7× 363 1.0× 92 2.6k
Kristen W. Yeom United States 34 2.1k 1.8× 1.0k 1.4× 637 1.2× 320 0.9× 756 2.1× 164 4.3k
Daniel Chow United States 28 1.4k 1.2× 627 0.9× 309 0.6× 183 0.5× 447 1.3× 95 2.6k
Tae Jin Yun South Korea 32 1.8k 1.5× 1.0k 1.4× 452 0.9× 488 1.3× 564 1.6× 144 3.3k
Roh‐Eul Yoo South Korea 26 1.2k 1.0× 571 0.8× 328 0.6× 260 0.7× 445 1.2× 123 2.2k
Daniel P. Barboriak United States 35 1.9k 1.6× 812 1.1× 623 1.2× 238 0.6× 578 1.6× 82 3.6k
Michael Iv United States 21 838 0.7× 424 0.6× 359 0.7× 147 0.4× 385 1.1× 85 1.8k
María Martínez-Lage United States 32 772 0.6× 1.3k 1.8× 1.2k 2.4× 143 0.4× 388 1.1× 84 3.4k
Kader Karlı Oğuz Türkiye 27 545 0.4× 310 0.4× 675 1.3× 302 0.8× 261 0.7× 225 3.0k
Zoran Rumboldt United States 27 891 0.7× 389 0.5× 554 1.1× 470 1.3× 585 1.6× 99 2.5k

Countries citing papers authored by Suyash Mohan

Since Specialization
Citations

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

Fields of papers citing papers by Suyash Mohan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Suyash Mohan

This figure shows the co-authorship network connecting the top 25 collaborators of Suyash Mohan. A scholar is included among the top collaborators of Suyash Mohan 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 Suyash Mohan. Suyash Mohan 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.
Wills, Carson A., Suyash Mohan, Ali Nabavizadeh, et al.. (2025). A single-arm phase 2 study of abemaciclib in adult patients with recurrent grade 3 oligodendroglioma. Neuro-Oncology Advances. 7(1). vdaf011–vdaf011.
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Malhotra, Ajay, et al.. (2025). Trends of Attrition and Migration in Academic Radiology in US Medical Schools. Journal of the American College of Radiology. 22(5). 600–605. 3 indexed citations
4.
Soni, Neetu, Amit Agarwal, Vivek Gupta, et al.. (2024). High-Grade Astrocytoma with Piloid Features: A Dual Institutional Review of Imaging Findings of a Novel Entity. American Journal of Neuroradiology. 45(4). 468–474. 10 indexed citations
5.
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Bathla, Girish, Neetu Soni, Ian T. Mark, et al.. (2024). Impact of SUSAN Denoising and ComBat Harmonization on Machine Learning Model Performance for Malignant Brain Neoplasms. American Journal of Neuroradiology. 45(9). 1291–1298. 1 indexed citations
7.
Verma, Gaurav, Donald M. O’Rourke, John Y. K. Lee, et al.. (2023). Non-Invasive Assessment of Isocitrate Dehydrogenase-Mutant Gliomas Using Optimized Proton Magnetic Resonance Spectroscopy on a Routine Clinical 3-Tesla MRI. Cancers. 15(18). 4453–4453. 6 indexed citations
8.
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Bathla, Girish, Neetu Soni, Nicholas B. Larson, et al.. (2023). AI-based classification of three common malignant tumors in neuro-oncology: A multi-institutional comparison of machine learning and deep learning methods. Journal of Neuroradiology. 51(3). 258–264. 19 indexed citations
10.
Hosseini, Seyyed Ali, Ghasem Hajianfar, Isaac Shiri, et al.. (2023). MRI-Based Radiomics Combined with Deep Learning for Distinguishing IDH-Mutant WHO Grade 4 Astrocytomas from IDH-Wild-Type Glioblastomas. Cancers. 15(3). 951–951. 15 indexed citations
11.
Schmitt, J. Eric, et al.. (2022). Spinal Cord Sarcoidosis Occurring at Sites of Spondylotic Stenosis, Mimicking Spondylotic Myelopathy: A Case Series and Review of the Literature. American Journal of Neuroradiology. 44(1). 105–110. 2 indexed citations
12.
Nasrallah, Ilya M., et al.. (2022). Artificial Intelligence-Powered Clinical Decision Support and Simulation Platform for Radiology Trainee Education. Journal of Digital Imaging. 36(1). 11–16. 23 indexed citations
13.
Masur, Jonathan, et al.. (2021). Am I Ready to Be an Independent Neuroradiologist? Objective Trends in Neuroradiology Fellows' Performance during the Fellowship Year. American Journal of Neuroradiology. 42(5). 815–823. 3 indexed citations
14.
Chawla, Sanjeev, Yulin Ge, Jens Wuerfel, et al.. (2020). Longitudinal ultra-high field MRI of brain lesions in neuromyelitis optica spectrum disorders. Multiple Sclerosis and Related Disorders. 42. 102066–102066. 4 indexed citations
15.
Masur, Jonathan, Colbey W. Freeman, & Suyash Mohan. (2020). A Double-Edged Sword: Neurologic Complications and Mortality in Extracorporeal Membrane Oxygenation Therapy for COVID-19–Related Severe Acute Respiratory Distress Syndrome at a Tertiary Care Center. American Journal of Neuroradiology. 41(11). 2009–2011. 18 indexed citations
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Duong, Michael Tran, Jeffrey D. Rudie, Jiancong Wang, et al.. (2019). Convolutional Neural Network for Automated FLAIR Lesion Segmentation on Clinical Brain MR Imaging. American Journal of Neuroradiology. 40(8). 1282–1290. 56 indexed citations
18.
Jurkiewicz, Michael T., C. C. Tchoyoson Lim, & Suyash Mohan. (2017). Clandestine charisma of the charm needles: a radiologist’s challenge. Emergency Radiology. 24(4). 427–430. 1 indexed citations
19.
Chawla, Sanjeev, Laurie A. Loevner, Sungheon Kim, et al.. (2017). Dynamic Contrast-Enhanced MRI–Derived Intracellular Water Lifetime (τi): A Prognostic Marker for Patients with Head and Neck Squamous Cell Carcinomas. American Journal of Neuroradiology. 39(1). 138–144. 22 indexed citations
20.
Bathla, Girish, et al.. (2017). Cerebrovascular Manifestations of Neurosarcoidosis: An Underrecognized Aspect of the Imaging Spectrum. American Journal of Neuroradiology. 39(7). 1194–1200. 37 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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