Vipul Baxi

1.3k total citations · 1 hit paper
16 papers, 446 citations indexed

About

Vipul Baxi is a scholar working on Oncology, Radiology, Nuclear Medicine and Imaging and Molecular Biology. According to data from OpenAlex, Vipul Baxi has authored 16 papers receiving a total of 446 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Oncology, 7 papers in Radiology, Nuclear Medicine and Imaging and 4 papers in Molecular Biology. Recurrent topics in Vipul Baxi's work include Cancer Immunotherapy and Biomarkers (10 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Immunotherapy and Immune Responses (3 papers). Vipul Baxi is often cited by papers focused on Cancer Immunotherapy and Biomarkers (10 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Immunotherapy and Immune Responses (3 papers). Vipul Baxi collaborates with scholars based in United States, Switzerland and Sweden. Vipul Baxi's co-authors include Michael Montalto, Robin Edwards, Saurabh Saha, Dimple Pandya, George Lee, Cristian Barrera, Benjamin Glass, Cyrus V. Hedvat, Daniel N. Cohen and Ilan Wapinski and has published in prestigious journals such as Journal of Clinical Oncology, Cancer Research and Journal of Hepatology.

In The Last Decade

Vipul Baxi

16 papers receiving 441 citations

Hit Papers

Digital pathology and artificial intelligence in translat... 2021 2026 2022 2024 2021 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Digital pathology and artificial intelligence in translational medicine and clinical practice
    2021 314 citations Vipul Baxi, Robin Edwards et al. Modern Pathology profile →

Peers

Vipul Baxi
Emmanuel Agosto‐Arroyo United States
Giovanni Lujan United States
John Maddison United Kingdom
Max Schmitt Germany
Luca L. Weishaupt United States
Peter Truszkowski United States
Jerome Cheng United States
Marko van Treeck Germany
Charles Maussion France
Andrew Zhang United States
Emmanuel Agosto‐Arroyo United States
Vipul Baxi
Citations per year, relative to Vipul Baxi Vipul Baxi (= 1×) peers Emmanuel Agosto‐Arroyo

Countries citing papers authored by Vipul Baxi

Since Specialization
Citations

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

Fields of papers citing papers by Vipul Baxi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vipul Baxi

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

All Works

16 of 16 papers shown
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Carbone, David P., Tudor–Eliade Ciuleanu, Michael Schenker, et al.. (2023). First-line (1L) nivolumab (N) + ipilimumab (I) + chemotherapy (C) vs C alone in patients (pts) with metastatic NSCLC (mNSCLC) from CheckMate 9LA: 4-y clinical update and outcomes by tumor histologic subtype (THS).. Journal of Clinical Oncology. 41(17_suppl). LBA9023–LBA9023. 6 indexed citations
3.
Prasanna, Prateek, Germán Corredor, Cristian Barrera, et al.. (2022). Image analysis reveals molecularly distinct patterns of TILs in NSCLC associated with treatment outcome. npj Precision Oncology. 6(1). 33–33. 32 indexed citations
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Sargent, Rachel L., George Lee, Jacqueline A. Brosnan‐Cashman, et al.. (2022). 1291 A multi-tumor machine learning model to identify tertiary lymphoid structures in histopathological H&E images as a potential clinical biomarker. Regular and Young Investigator Award Abstracts. A1341–A1341. 1 indexed citations
7.
Corredor, Germán, Prateek Prasanna, Cristian Barrera, et al.. (2022). Density patterns of tumor-infiltrating lymphocytes and association with objective response to nivolumab in patients with lung adenocarcinoma from CheckMate 057.. Journal of Clinical Oncology. 40(16_suppl). 2662–2662. 1 indexed citations
8.
Baxi, Vipul, Robin Edwards, Michael Montalto, & Saurabh Saha. (2021). Digital pathology and artificial intelligence in translational medicine and clinical practice. Modern Pathology. 35(1). 23–32. 314 indexed citations breakdown →
9.
Lee, George, Keyur Desai, Hao Tang, et al.. (2021). 387 The utility of AI-powered spatial classification of intratumoral CD8+ immune-cell distribution in predicting overall survival in patients with melanoma as part of the checkMate 067 clinical trial. Journal for ImmunoTherapy of Cancer. 9(Suppl 2). A420–A420. 2 indexed citations
10.
McNamara, George, Justin Lucas, John F. Beeler, et al.. (2020). New Technologies to Image Tumors. Cancer treatment and research. 180. 51–94. 4 indexed citations
11.
Montalto, Michael, George Lee, Dimple Pandya, et al.. (2020). Abstract 2017: Association of digital and manual quantification of tumor PD-L1 expression with outcomes in nivolumab-treated patients. Cancer Research. 80(16_Supplement). 2017–2017. 3 indexed citations
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Szabó, Péter M., George Lee, Scott Ely, et al.. (2019). CD8+ T cells in tumor parenchyma and stroma by image analysis (IA) and gene expression profiling (GEP): Potential biomarkers for immuno-oncology (I-O) therapy.. Journal of Clinical Oncology. 37(15_suppl). 2594–2594. 4 indexed citations
14.
Luke, Jason J., Robin Edwards, Cyrus V. Hedvat, et al.. (2018). Characterization of the immune tumor microenvironment (TME) to inform personalized medicine with immuno-oncology (IO) combinations. Annals of Oncology. 29. viii403–viii403. 2 indexed citations
15.
Baxi, Vipul, et al.. (2011). The accuracy of dynamic predictive autofocusing for whole slide imaging. Journal of Pathology Informatics. 2(1). 38–38. 19 indexed citations
16.
Srikhirin, Toemsak, et al.. (2009). Investigation of enzyme reaction by surface plasmon resonance (SPR) technique. Sensors and Actuators B Chemical. 139(2). 274–279. 11 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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