Igor Pronin

1.3k total citations
151 papers, 835 citations indexed

About

Igor Pronin is a scholar working on Radiology, Nuclear Medicine and Imaging, Genetics and Neurology. According to data from OpenAlex, Igor Pronin has authored 151 papers receiving a total of 835 indexed citations (citations by other indexed papers that have themselves been cited), including 64 papers in Radiology, Nuclear Medicine and Imaging, 61 papers in Genetics and 45 papers in Neurology. Recurrent topics in Igor Pronin's work include Glioma Diagnosis and Treatment (61 papers), Advanced MRI Techniques and Applications (28 papers) and Advanced Neuroimaging Techniques and Applications (27 papers). Igor Pronin is often cited by papers focused on Glioma Diagnosis and Treatment (61 papers), Advanced MRI Techniques and Applications (28 papers) and Advanced Neuroimaging Techniques and Applications (27 papers). Igor Pronin collaborates with scholars based in Russia, United States and Germany. Igor Pronin's co-authors include Andrei I. Holodny, Andrey Golanov, Andrey Korshunov, Regina Sycheva, Potapov Aa, Н. Е. Захарова, А. И. Баталов, Ivan I. Maximov, Valery Kornienko and Galina Pavlova and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and International Journal of Molecular Sciences.

In The Last Decade

Igor Pronin

123 papers receiving 811 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Igor Pronin Russia 14 341 281 207 164 122 151 835
Jean‐Michel Lemée France 19 238 0.7× 358 1.3× 249 1.2× 296 1.8× 98 0.8× 53 1.1k
Erik Magnus Berntsen Norway 17 476 1.4× 533 1.9× 132 0.6× 214 1.3× 77 0.6× 50 973
Fengping Zhu China 16 208 0.6× 182 0.6× 143 0.7× 137 0.8× 162 1.3× 40 701
Daniel Delev Germany 19 135 0.4× 356 1.3× 134 0.6× 127 0.8× 129 1.1× 64 879
Vibeke Andrée Larsen Denmark 13 186 0.5× 214 0.8× 129 0.6× 102 0.6× 79 0.6× 43 795
Marco Conti Nibali Italy 17 272 0.8× 428 1.5× 105 0.5× 247 1.5× 51 0.4× 40 824
Dongxiao Zhuang China 18 422 1.2× 455 1.6× 98 0.5× 232 1.4× 83 0.7× 50 894
Mary Catherine Mayo United States 6 196 0.6× 411 1.5× 258 1.2× 129 0.8× 121 1.0× 10 842
Philippe Paquis France 17 200 0.6× 281 1.0× 350 1.7× 77 0.5× 173 1.4× 36 1.1k
Leiming Wang China 15 141 0.4× 318 1.1× 152 0.7× 94 0.6× 145 1.2× 62 733

Countries citing papers authored by Igor Pronin

Since Specialization
Citations

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

Fields of papers citing papers by Igor Pronin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Igor Pronin

This figure shows the co-authorship network connecting the top 25 collaborators of Igor Pronin. A scholar is included among the top collaborators of Igor Pronin 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 Igor Pronin. Igor Pronin 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.
Романишкин, И. Д., et al.. (2025). Machine Learning and Artificial Intelligence Systems Based on the Optical Spectral Analysis in Neuro-Oncology. Photonics. 12(1). 37–37. 4 indexed citations
2.
Романишкин, И. Д., et al.. (2024). Machine learning methods for spectrally-resolved imaging analysis in neuro-oncology. SHILAP Revista de lepidopterología. 13(4). 40–54. 2 indexed citations
3.
Ryzhova, Marina, et al.. (2023). Factors influencing peritumoral edema in meningiomas: CT- and MRI-based quantitative assessment. Burdenko s Journal of Neurosurgery. 87(4). 17–17. 1 indexed citations
4.
Kozlov, Andrew, et al.. (2023). Perifocal edema and glymphatic system dysfunction: quantitative assessment based on diffusion tensor magnetic resonance imaging. Burdenko s Journal of Neurosurgery. 87(5). 45–45.
5.
Konovalov, An N, et al.. (2023). Spontaneous dural CSF fistula as a cause of intracranial hypotension syndrome. Case report and literature review. Burdenko s Journal of Neurosurgery. 87(2). 63–63.
6.
Danilov, Gleb, et al.. (2023). Radiomics Enhances Diagnostic and Prognostic Value of Diffusion Kurtosis Imaging in Diffuse Axonal Injury. Studies in health technology and informatics. 309. 287–291. 2 indexed citations
7.
8.
Романишкин, И. Д., et al.. (2022). Multimodal method of tissue differentiation in neurooncology using Raman spectroscopy, fluorescence and diffuse reflectance spectroscopy. Burdenko s Journal of Neurosurgery. 86(5). 5–5. 3 indexed citations
9.
Баталов, А. И., et al.. (2022). Radiological biomarkers of brain gliomas. Burdenko s Journal of Neurosurgery. 86(6). 121–121. 1 indexed citations
10.
Konovalov, An N, et al.. (2022). Spinal CSF-venous fistula: case report and literature review. Burdenko s Journal of Neurosurgery. 86(3). 41–41. 2 indexed citations
11.
Danilov, Gleb, et al.. (2022). MR-guided non-invasive typing of brain gliomas using machine learning. Burdenko s Journal of Neurosurgery. 86(6). 36–36. 1 indexed citations
12.
13.
Баталов, А. И., et al.. (2021). Automatic Algorithm of Magnetic Resonance Morphometry in the Diagnosis of Focal Cortical Dysplasia. 63–76. 1 indexed citations
14.
Захарова, Н. Е., et al.. (2021). Magnetic resonance tractography based on the constrained spherical deconvolution in patients with gliomas of the optic pathway. SHILAP Revista de lepidopterología. 49(1). 11–20. 1 indexed citations
15.
Баталов, А. И., et al.. (2021). 11C-METHIONINE AND 18F- FLUORODEOXYGLUCOSE PET/CT IN DIAGNOSIS OF BRAIN GLIOMAS HETEROGENEITY. Russian Electronic Journal of Radiology. 11(2). 68–82. 1 indexed citations
16.
Pronin, Igor, et al.. (2020). CT-perfusion in assessment of the malignant gliomas hemodynamics. Medical Visualization. 24(2). 105–118. 2 indexed citations
17.
Kornienko, Valery, et al.. (2020). fMRI resting state networks visualization in patients with severe traumatic brain injury. Medical Visualization. 24(1). 68–84. 2 indexed citations
18.
Pronin, Igor, et al.. (2017). COMPUTED ANGIOGRAPHY IN DEDICATED ASSESSMENT OF OCCLUSIVE CAROTID DISEASE FOR RECONSTRUCTIVE SURGERY PLANNING. SHILAP Revista de lepidopterología. 98(2). 69–77.
19.
Pronin, Igor, et al.. (2008). Diffusion tensor imaging and diffusion tensor tractography. SHILAP Revista de lepidopterología. 2(1). 2 indexed citations
20.
Korshunov, Andrey, Regina Sycheva, Andrey Golanov, & Igor Pronin. (2007). Gains at the 1p36 Chromosomal Region Are Associated With Symptomatic Leptomeningeal Dissemination of Supratentorial Glioblastomas. American Journal of Clinical Pathology. 127(4). 585–590. 20 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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