Ritesh Parajuli

1.2k total citations
27 papers, 529 citations indexed

About

Ritesh Parajuli is a scholar working on Oncology, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Ritesh Parajuli has authored 27 papers receiving a total of 529 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Oncology, 10 papers in Radiology, Nuclear Medicine and Imaging and 8 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Ritesh Parajuli's work include Radiomics and Machine Learning in Medical Imaging (6 papers), MRI in cancer diagnosis (6 papers) and AI in cancer detection (5 papers). Ritesh Parajuli is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (6 papers), MRI in cancer diagnosis (6 papers) and AI in cancer detection (5 papers). Ritesh Parajuli collaborates with scholars based in United States, Taiwan and South Korea. Ritesh Parajuli's co-authors include Rita S. Mehta, Jeon‐Hor Chen, Min‐Ying Su, Yang Zhang, Sanket H. Shah, Ram H. Datar, Dorraya El‐Ashry, Siddarth Rawal, Philip C. Miller and Richard J. Côté and has published in prestigious journals such as Journal of Clinical Oncology, Cancer Research and Scientific Reports.

In The Last Decade

Ritesh Parajuli

23 papers receiving 518 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ritesh Parajuli United States 11 195 190 172 93 92 27 529
Xiaotang Yang China 9 154 0.8× 146 0.8× 83 0.5× 66 0.7× 52 0.6× 26 364
Zixiao Lu China 10 70 0.4× 380 2.0× 265 1.5× 142 1.5× 107 1.2× 20 602
Qitao Huang China 11 167 0.9× 201 1.1× 226 1.3× 172 1.8× 152 1.7× 25 613
Salendra Singh United States 11 116 0.6× 409 2.2× 91 0.5× 178 1.9× 121 1.3× 23 671
Eirini Arvaniti Switzerland 6 73 0.4× 163 0.9× 246 1.4× 148 1.6× 44 0.5× 9 558
Meriem Sefta France 5 157 0.8× 305 1.6× 347 2.0× 143 1.5× 155 1.7× 6 688
Kevin Boehm United States 5 56 0.3× 164 0.9× 128 0.7× 135 1.5× 89 1.0× 8 433
Nathan Ing United States 6 80 0.4× 165 0.9× 217 1.3× 107 1.2× 57 0.6× 11 411
Robert Kornegoor Netherlands 12 83 0.4× 162 0.9× 285 1.7× 111 1.2× 96 1.0× 18 678
Daehoon Park Norway 8 135 0.7× 135 0.7× 84 0.5× 85 0.9× 100 1.1× 14 358

Countries citing papers authored by Ritesh Parajuli

Since Specialization
Citations

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

Fields of papers citing papers by Ritesh Parajuli

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ritesh Parajuli

This figure shows the co-authorship network connecting the top 25 collaborators of Ritesh Parajuli. A scholar is included among the top collaborators of Ritesh Parajuli 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 Ritesh Parajuli. Ritesh Parajuli 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.
Hamilton, Erika, Hyo S. Han, Kevin Kalinsky, et al.. (2025). Initial phase 1 dose escalation data for emiltatug ledadotin (Emi-Le), a novel B7-H4-directed dolasynthen antibody-drug conjugate.. Journal of Clinical Oncology. 43(16_suppl). 3009–3009. 1 indexed citations
2.
Acharya, Munjal M., Paul Coluzzi, Sanghoon Lee, et al.. (2023). Electroacupuncture for the management of symptom clusters in cancer patients and survivors (EAST). BMC Complementary Medicine and Therapies. 23(1). 92–92. 7 indexed citations
3.
Parajuli, Ritesh, et al.. (2023). Multiorgan Failure From Nivolumab and Ipilimumab: A Case Report and Literature Review. Cureus. 15(7). e41781–e41781. 4 indexed citations
4.
Zhang, Yang, Yi Liu, Ke Nie, et al.. (2023). Deep Learning-based Automatic Diagnosis of Breast Cancer on MRI Using Mask R-CNN for Detection Followed by ResNet50 for Classification. Academic Radiology. 30. S161–S171. 52 indexed citations
5.
Hamilton, Erika, Alexander I. Spira, Sylvia Adams, et al.. (2023). XMT-1660: A Phase 1b trial of a B7-H4 targeting antibody drug conjugate (ADC) in endometrial, ovarian, and breast cancers (1250). Gynecologic Oncology. 176. S158–S158.
6.
Hamilton, Erika, Arvind Chaudhry, Alexander I. Spira, et al.. (2023). XMT-1660: A phase 1b trial of a B7-H4 targeted antibody drug conjugate (ADC) in breast, endometrial, and ovarian cancers.. Journal of Clinical Oncology. 41(16_suppl). TPS3154–TPS3154. 6 indexed citations
7.
8.
Hamilton, Erika, Arvind Chaudhry, Alexander I. Spira, et al.. (2022). TP012/#1420 XMT-1660: a phase 1B trial of a B7-H4 targeting antibody drug conjugate (ADC) in endometrial, ovarian, and breast cancers. A228.2–A229. 1 indexed citations
9.
10.
Zhang, Yang, Si‐Wa Chan, Jeon‐Hor Chen, et al.. (2021). Development of U-Net Breast Density Segmentation Method for Fat-Sat MR Images Using Transfer Learning Based on Non-Fat-Sat Model. Journal of Digital Imaging. 34(4). 877–887. 12 indexed citations
11.
Chen, Zhongwei, Yang Zhang, Jiejie Zhou, et al.. (2021). Diagnosis of Breast Cancer Using Radiomics Models Built Based on Dynamic Contrast Enhanced MRI Combined With Mammography. Frontiers in Oncology. 11. 774248–774248. 10 indexed citations
12.
Zhang, Yang, Si‐Wa Chan, Vivian Youngjean Park, et al.. (2020). Automatic Detection and Segmentation of Breast Cancer on MRI Using Mask R-CNN Trained on Non–Fat-Sat Images and Tested on Fat-Sat Images. Academic Radiology. 29. S135–S144. 50 indexed citations
13.
Zhang, Yang, Jeon‐Hor Chen, Yezhi Lin, et al.. (2020). Prediction of breast cancer molecular subtypes on DCE-MRI using convolutional neural network with transfer learning between two centers. European Radiology. 31(4). 2559–2567. 102 indexed citations
14.
Jiang, Ruoyu, Sudhanshu Agrawal, Mohammad Aghaamoo, et al.. (2020). Rapid isolation of circulating cancer associated fibroblasts by acoustic microstreaming for assessing metastatic propensity of breast cancer patients. Lab on a Chip. 21(5). 875–887. 28 indexed citations
15.
Mehta, Rita S., et al.. (2019). Benign Metastasizing Leiomyoma to the Lung and Spine: A Case Report and Literature Review. Case Reports in Oncology. 12(1). 218–223. 14 indexed citations
16.
17.
Lyou, Yung, et al.. (2018). Radiation-Associated Angiosarcoma of the Breast: A Case Report and Literature Review. Case Reports in Oncology. 11(1). 216–220. 7 indexed citations
18.
Lyou, Yung, et al.. (2018). Orbital Metastases from Breast Cancer with BRCA2 Mutation: A Case Report and Literature Review. Case Reports in Oncology. 11(2). 360–364. 2 indexed citations
19.
Parajuli, Ritesh, et al.. (2010). Rituximab‐induced acute severe thrombocytopenia. British Journal of Haematology. 149(6). 804–804. 10 indexed citations
20.
Araki, Hiroto, Sudhakar Baluchamy, Benjamin J. Petro, et al.. (2009). Cord blood stem cell expansion is permissive to epigenetic regulation and environmental cues. Experimental Hematology. 37(9). 1084–1095. 18 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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