Sadegh Riyahi

406 total citations
9 papers, 257 citations indexed

About

Sadegh Riyahi is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Biomedical Engineering. According to data from OpenAlex, Sadegh Riyahi has authored 9 papers receiving a total of 257 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Radiology, Nuclear Medicine and Imaging, 5 papers in Pulmonary and Respiratory Medicine and 3 papers in Biomedical Engineering. Recurrent topics in Sadegh Riyahi's work include Radiomics and Machine Learning in Medical Imaging (8 papers), Lung Cancer Diagnosis and Treatment (5 papers) and MRI in cancer diagnosis (4 papers). Sadegh Riyahi is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (8 papers), Lung Cancer Diagnosis and Treatment (5 papers) and MRI in cancer diagnosis (4 papers). Sadegh Riyahi collaborates with scholars based in United States and Morocco. Sadegh Riyahi's co-authors include Joseph O. Deasy, Wookjin Choi, Wei Lü, Chia‐Ju Liu, James Mechalakos, Andreas Rimner, Neelam Tyagi, Jue Jiang, Wengen Chen and Jung Hun Oh and has published in prestigious journals such as International Journal of Radiation Oncology*Biology*Physics, Physics in Medicine and Biology and Medical Physics.

In The Last Decade

Sadegh Riyahi

9 papers receiving 252 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sadegh Riyahi United States 5 217 119 62 58 41 9 257
Haonan Xiao Hong Kong 11 227 1.0× 61 0.5× 93 1.5× 70 1.2× 41 1.0× 23 302
Kristin A. Plichta United States 8 279 1.3× 148 1.2× 45 0.7× 61 1.1× 46 1.1× 20 347
Rita Simões Netherlands 10 223 1.0× 87 0.7× 52 0.8× 60 1.0× 38 0.9× 30 309
Xinzhi Teng Hong Kong 12 281 1.3× 98 0.8× 78 1.3× 76 1.3× 49 1.2× 41 356
Joshua Giambattista Canada 6 217 1.0× 87 0.7× 184 3.0× 81 1.4× 51 1.2× 16 308
James I. Monroe United States 9 180 0.8× 128 1.1× 194 3.1× 77 1.3× 21 0.5× 23 321
Yushi Chang United States 12 265 1.2× 103 0.9× 108 1.7× 87 1.5× 48 1.2× 21 316
Filipa Guerreiro Netherlands 9 219 1.0× 125 1.1× 217 3.5× 70 1.2× 24 0.6× 13 313
Xudong Xue China 9 155 0.7× 46 0.4× 65 1.0× 65 1.1× 54 1.3× 26 245
Dao Lam United States 7 247 1.1× 114 1.0× 213 3.4× 122 2.1× 40 1.0× 9 339

Countries citing papers authored by Sadegh Riyahi

Since Specialization
Citations

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

Fields of papers citing papers by Sadegh Riyahi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sadegh Riyahi

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

All Works

9 of 9 papers shown
1.
Wang, Chuang, Andreas Rimner, Yu‐Chi Hu, et al.. (2019). Toward predicting the evolution of lung tumors during radiotherapy observed on a longitudinal MR imaging study via a deep learning algorithm. Medical Physics. 46(10). 4699–4707. 37 indexed citations
2.
Klages, Peter, Sadegh Riyahi, Jue Jiang, et al.. (2019). Patch‐based generative adversarial neural network models for head and neck MR‐only planning. Medical Physics. 47(2). 626–642. 68 indexed citations
3.
Choi, Wookjin, Sadegh Riyahi, Seth Kligerman, et al.. (2018). Technical Note: Identification of CT Texture Features Robust to Tumor Size Variations for Normal Lung Texture Analysis. International Journal of Medical Physics Clinical Engineering and Radiation Oncology. 7(3). 330–338. 9 indexed citations
4.
Riyahi, Sadegh, Wookjin Choi, Chia‐Ju Liu, et al.. (2018). Quantifying local tumor morphological changes with Jacobian map for prediction of pathologic tumor response to chemo-radiotherapy in locally advanced esophageal cancer. Physics in Medicine and Biology. 63(14). 145020–145020. 29 indexed citations
5.
Choi, Wookjin, Jung Hun Oh, Sadegh Riyahi, et al.. (2018). Radiomics analysis of pulmonary nodules in low‐dose CT for early detection of lung cancer. Medical Physics. 45(4). 1537–1549. 108 indexed citations
6.
Choi, Wookjin, et al.. (2017). Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induced Lung Disease. International Journal of Radiation Oncology*Biology*Physics. 99(2). S196–S197. 2 indexed citations
7.
Choi, Wookjin, Sadegh Riyahi, & Wei Lü. (2016). SU-F-R-31: Identification of Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induced Lung Disease. Medical Physics. 43(6Part6). 3379–3380. 1 indexed citations
8.
Riyahi, Sadegh, et al.. (2016). SU‐C‐207B‐06: Comparison of Registration Methods for Modeling Pathologic Response of Esophageal Cancer to Chemoradiation Therapy. Medical Physics. 43(6Part3). 3331–3331. 1 indexed citations
9.
Riyahi, Sadegh, et al.. (2008). Les abcès cérébraux (à propos de 80 cas). Neurochirurgie. 55(1). 40–44. 2 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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