Nathaniel Braman

2.5k total citations · 3 hit papers
22 papers, 1.6k citations indexed

About

Nathaniel Braman is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Oncology. According to data from OpenAlex, Nathaniel Braman has authored 22 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Radiology, Nuclear Medicine and Imaging, 8 papers in Pulmonary and Respiratory Medicine and 7 papers in Oncology. Recurrent topics in Nathaniel Braman's work include Radiomics and Machine Learning in Medical Imaging (19 papers), MRI in cancer diagnosis (5 papers) and Medical Imaging Techniques and Applications (4 papers). Nathaniel Braman is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (19 papers), MRI in cancer diagnosis (5 papers) and Medical Imaging Techniques and Applications (4 papers). Nathaniel Braman collaborates with scholars based in United States, South Korea and Belgium. Nathaniel Braman's co-authors include Anant Madabhushi, Kaustav Bera, Vamsidhar Velcheti, Amit Gupta, Prateek Prasanna, Pallavi Tiwari, Donna Plecha, Maryam Etesami, Hannah Gilmore and Christina Dubchuk and has published in prestigious journals such as Nature Communications, Journal of Clinical Oncology and Cancer Research.

In The Last Decade

Nathaniel Braman

19 papers receiving 1.6k citations

Hit Papers

Predicting cancer outcomes with radiomics and ar... 2017 2026 2020 2023 2021 2017 2019 100 200 300 400
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. Predicting cancer outcomes with radiomics and artificial intelligence in radiology
    2021 482 citations Kaustav Bera, Nathaniel Braman et al. Nature Reviews Clinical Oncology profile →
  2. Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI
    2017 479 citations Nathaniel Braman, Maryam Etesami et al. Breast Cancer Research profile →
  3. Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy forHER2 (ERBB2)–Positive Breast Cancer
    2019 235 citations Nathaniel Braman, Prateek Prasanna et al. JAMA Network Open profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nathaniel Braman United States 9 1.5k 496 349 290 282 22 1.6k
Michele Avanzo Italy 22 1.3k 0.9× 719 1.4× 253 0.7× 400 1.4× 253 0.9× 63 1.8k
Xiaokai Mo China 17 1.2k 0.8× 415 0.8× 243 0.7× 220 0.8× 254 0.9× 37 1.6k
Stefano Trebeschi Netherlands 16 1.1k 0.8× 370 0.7× 201 0.6× 318 1.1× 534 1.9× 47 1.5k
Elaine Johanna Limkin France 12 1.4k 0.9× 578 1.2× 202 0.6× 324 1.1× 616 2.2× 26 1.8k
Sylvain Reuzé France 12 1.7k 1.2× 565 1.1× 232 0.7× 436 1.5× 372 1.3× 18 2.0k
Olya Stringfield United States 12 1.2k 0.8× 852 1.7× 197 0.6× 313 1.1× 221 0.8× 24 1.4k
Yahong Luo China 20 1.1k 0.7× 474 1.0× 464 1.3× 165 0.6× 281 1.0× 62 1.4k
Eun Sook Ko South Korea 29 1.7k 1.1× 388 0.8× 544 1.6× 167 0.6× 367 1.3× 99 2.4k
David Fried United States 18 1.6k 1.0× 809 1.6× 239 0.7× 642 2.2× 452 1.6× 60 2.0k

Countries citing papers authored by Nathaniel Braman

Since Specialization
Citations

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

Fields of papers citing papers by Nathaniel Braman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nathaniel Braman

This figure shows the co-authorship network connecting the top 25 collaborators of Nathaniel Braman. A scholar is included among the top collaborators of Nathaniel Braman 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 Nathaniel Braman. Nathaniel Braman 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
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Lee, Seyoung, Jeeyeon Lee, Kai Zhang, et al.. (2024). AI-based radiomics model for predicting immune checkpoint inhibitor–related pneumonitis (CIP) in patients with advanced NSCLC: An external validation study.. Journal of Clinical Oncology. 42(16_suppl). 12136–12136. 1 indexed citations
3.
Lee, Seyoung, Jeeyeon Lee, Kai Zhang, et al.. (2024). CheckpointPx: A predictive radiology AI model of immune checkpoint inhibitor (ICI) benefit in non-small cell lung cancer (NSCLC).. Journal of Clinical Oncology. 42(16_suppl). 8632–8632.
4.
Khorrami, Mohammadhadi, Nathaniel Braman, Siddharth Kunte, et al.. (2023). Radiomic predicts early response to CDK4/6 inhibitors in hormone receptor positive metastatic breast cancer. npj Breast Cancer. 9(1). 67–67. 4 indexed citations
5.
Braman, Nathaniel, Prateek Prasanna, Kaustav Bera, et al.. (2022). Novel Radiomic Measurements of Tumor-Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers. Clinical Cancer Research. 28(20). 4410–4424. 19 indexed citations
6.
Yang, Kailin, Cheng Lu, Lin Li, et al.. (2021). Radiomic Features Associated With HPV Status on Pretreatment Computed Tomography in Oropharyngeal Squamous Cell Carcinoma Inform Clinical Prognosis. Frontiers in Oncology. 11. 744250–744250. 22 indexed citations
7.
Bera, Kaustav, Nathaniel Braman, Amit Gupta, Vamsidhar Velcheti, & Anant Madabhushi. (2021). Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nature Reviews Clinical Oncology. 19(2). 132–146. 482 indexed citations breakdown →
8.
Beig, Niha, Kaustav Bera, Prateek Prasanna, et al.. (2020). Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma. Clinical Cancer Research. 26(8). 1866–1876. 92 indexed citations
9.
Braman, Nathaniel, Mohammed El Adoui, Manasa Vulchi, et al.. (2020). Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional validation study.. Nature Communications. 2 indexed citations
10.
Prasanna, Prateek, Natália Figueiredo, Duriye Damla Sevgi, et al.. (2020). Radiomics-based assessment of ultra-widefield leakage patterns and vessel network architecture in the PERMEATE study: insights into treatment durability. British Journal of Ophthalmology. 105(8). 1155–1160. 17 indexed citations
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Kunte, Siddharth, Nathaniel Braman, Kaustav Bera, et al.. (2020). Radiomics risk score (RRS) on CT to predict survival and response to CDK 4/6 inhibitors in hormone receptor (HR) positive metastatic breast cancer (MBC).. Journal of Clinical Oncology. 38(15_suppl). e13041–e13041. 2 indexed citations
15.
Braman, Nathaniel, Prateek Prasanna, Jon Whitney, et al.. (2019). Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy forHER2 (ERBB2)–Positive Breast Cancer. JAMA Network Open. 2(4). e192561–e192561. 235 indexed citations breakdown →
16.
Vulchi, Manasa, Mohammed El Adoui, Nathaniel Braman, et al.. (2019). Development and external validation of a deep learning model for predicting response to HER2-targeted neoadjuvant therapy from pretreatment breast MRI.. Journal of Clinical Oncology. 37(15_suppl). 593–593. 6 indexed citations
17.
Beig, Niha, Mohammadhadi Khorrami, Mehdi Alilou, et al.. (2018). Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas. Radiology. 290(3). 783–792. 238 indexed citations
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Braman, Nathaniel, et al.. (2018). Predicting neo-adjuvant chemotherapy response from pre-treatment breast MRI using machine learning and HER2 status.. Journal of Clinical Oncology. 36(15_suppl). 582–582. 1 indexed citations
20.
Braman, Nathaniel, Maryam Etesami, Prateek Prasanna, et al.. (2017). Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Research. 19(1). 57–57. 479 indexed citations breakdown →

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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