Manasa Vulchi
Impact in
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- Radiomics and Machine Learning in Medical Imaging
- MRI in cancer diagnosis
- Medical Imaging Techniques and Applications
Papers in
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- Radiomics and Machine Learning in Medical Imaging 3
- MRI in cancer diagnosis 1
- Medical Imaging Techniques and Applications 1
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- AI in cancer detection 2
- Co-authors
- Anant Madabhushi (4 shared papers)Paulette Turk (4 shared papers)Maryam Etesami (4 shared papers)Kaustav Bera (4 shared papers)Jame Abraham (4 shared papers)Nathaniel M. Braman (4 shared papers)Donna M. Plecha (4 shared papers)Prateek Prasanna (2 shared papers)
- Journals
- JAMA Network Open (1 paper)Nature Communications (1 paper)Clinical Cancer Research (1 paper)Journal of Community Hospital Internal Medicine Perspectives (1 paper)Repository of the University of Namur (1 paper)
- Partner nations
- United States
In The Last Decade
Manasa Vulchi
5 papers receiving 316 citations
Manasa Vulchi's Hit Papers
Peers
Comparison fields: 5 of 30
- Radiology, Nuclear Medicine and Imaging 188
- Health Informatics 6
- Cancer Research 29
- Oncology 39
- Artificial Intelligence 53
Countries citing papers authored by Manasa Vulchi
This map shows the geographic impact of Manasa Vulchi'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 Manasa Vulchi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Manasa Vulchi more than expected).
Fields of papers citing papers by Manasa Vulchi
This network shows the impact of papers produced by Manasa Vulchi. 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 Manasa Vulchi. The network helps show where Manasa Vulchi may publish in the future.
Co-authors
The 25 scholars most cited alongside Manasa Vulchi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2) –Positive Breast Cancer Hit paper breakdown → | 2019 | 268 |
| 2 | 2022 | 24 | |
| 3 | 2016 | 14 | |
| 4 | 2020 | 12 | |
| 5 | Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional validation study. | 2020 | 2 |
About Manasa Vulchi
Manasa Vulchi is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Insect Science, Renewable Energy, Sustainability and the Environment and Obstetrics and Gynecology, having authored 5 papers that have together received 320 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (3 papers), AI in cancer detection (2 papers), MRI in cancer diagnosis (1 paper) and Medical Imaging Techniques and Applications (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (188 citations), Health Informatics (6 citations), Cancer Research (29 citations), Oncology (39 citations) and Artificial Intelligence (53 citations). Manasa Vulchi has collaborated with scholars based in United States. Frequent co-authors include Anant Madabhushi, Paulette Turk, Maryam Etesami, Kaustav Bera, Jame Abraham, Nathaniel M. Braman, Donna M. Plecha, Prateek Prasanna, Vinay Varadan and Hannah L. Gilmore. Their work appears in journals such as JAMA Network Open, Nature Communications, Clinical Cancer Research, Journal of Community Hospital Internal Medicine Perspectives and Repository of the University of Namur.
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.