John Freymann

13.4k total citations · 2 hit papers
28 papers, 5.6k citations indexed

About

John Freymann is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Cancer Research. According to data from OpenAlex, John Freymann has authored 28 papers receiving a total of 5.6k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Radiology, Nuclear Medicine and Imaging, 8 papers in Pulmonary and Respiratory Medicine and 8 papers in Cancer Research. Recurrent topics in John Freymann's work include Radiomics and Machine Learning in Medical Imaging (24 papers), Glioma Diagnosis and Treatment (7 papers) and AI in cancer detection (6 papers). John Freymann is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (24 papers), Glioma Diagnosis and Treatment (7 papers) and AI in cancer detection (6 papers). John Freymann collaborates with scholars based in United States, United Kingdom and Netherlands. John Freymann's co-authors include Justin Kirby, Fred Prior, Kirk Smith, Lawrence Tarbox, Kenneth Clark, Stephen Moore, Bruce A. Vendt, Paul Koppel, Michael Pringle and Michel Bilello and has published in prestigious journals such as Journal of Clinical Oncology, JNCI Journal of the National Cancer Institute and Cancer.

In The Last Decade

John Freymann

28 papers receiving 5.5k citations

Hit Papers

align trajectories

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.

The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository

2013 2.9k citations Kenneth Clark, Bruce A. Vendt et al. Journal of Digital Imaging profile →
Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

2017 1.5k citations Spyridon Bakas, Hamed Akbari et al. Scientific Data profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
John Freymann United States	18	3.6k	1.7k	1.5k	1.3k	1.1k	28	5.6k
Justin Kirby United States	20	3.8k 1.1×	1.8k 1.0×	1.6k 1.1×	1.3k 1.0×	1.3k 1.2×	32	6.0k
Spyridon Bakas United States	27	2.2k 0.6×	1.4k 0.8×	1.5k 1.0×	1.4k 1.0×	323 0.3×	129	4.5k
Hamed Akbari United States	26	2.2k 0.6×	1.3k 0.7×	678 0.4×	1.2k 0.9×	307 0.3×	90	4.0k
Stephen Moore United States	21	2.4k 0.7×	1.0k 0.6×	1.1k 0.7×	351 0.3×	1.1k 1.0×	81	4.5k
Bruce A. Vendt United States	8	2.1k 0.6×	739 0.4×	959 0.6×	348 0.3×	802 0.7×	9	3.3k
Lawrence Tarbox United States	9	2.2k 0.6×	787 0.5×	1.0k 0.7×	355 0.3×	834 0.8×	16	3.3k
Jinhua Yu China	31	2.5k 0.7×	635 0.4×	992 0.6×	307 0.2×	524 0.5×	195	4.0k
Michael Pringle United Kingdom	9	2.0k 0.6×	727 0.4×	919 0.6×	346 0.3×	989 0.9×	9	3.8k
Paul Koppel Netherlands	11	2.1k 0.6×	730 0.4×	932 0.6×	345 0.3×	788 0.7×	13	3.2k
Michel Bilello United States	24	1.7k 0.5×	1.2k 0.7×	668 0.4×	1.2k 0.9×	196 0.2×	60	3.5k

Countries citing papers authored by John Freymann

Since Specialization

Citations

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

Fields of papers citing papers by John Freymann

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of John Freymann

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

All Works

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20 of 20 papers shown

Kalen, Joseph D., David Clunie, Yanling Liu, et al.. (2021). Design and Implementation of the Pre-Clinical DICOM Standard in Multi-Cohort Murine Studies. Tomography. 7(1). 1–9. 2 indexed citations

Fedorov, Andriy, David Clunie, Mathias Brochhausen, et al.. (2020). DICOM re‐encoding of volumetrically annotated Lung Imaging Database Consortium (LIDC) nodules. Medical Physics. 47(11). 5953–5965. 11 indexed citations

Farahani, Keyvan, Tahsin Kurç, Spyridon Bakas, et al.. (2020). Computational Precision Medicine Radiology-Pathology challenge on Brain Tumor Classification 2020. Zenodo (CERN European Organization for Nuclear Research). 4 indexed citations

Beer, Lucian, Hilal Şahin, Nicholas W. Bateman, et al.. (2020). Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis. European Radiology. 30(8). 4306–4316. 29 indexed citations

Lee, Jerry, Kathleen M. Darcy, Hai Hu, et al.. (2019). From Discovery to Practice and Survivorship: Building a National Real‐World Data Learning Healthcare Framework for Military and Veteran Cancer Patients. Clinical Pharmacology & Therapeutics. 106(1). 52–57. 17 indexed citations

Lerner, Seth P., Vinay Duddalwar, Erich P. Huang, et al.. (2019). Comprehensive radiogenomics analysis of qualitative and quantitative features of cross-sectional imaging in the TCGA project in MIBC.. Journal of Clinical Oncology. 37(7_suppl). 482–482. 2 indexed citations

Bakas, Spyridon, Hamed Akbari, Aristeidis Sotiras, et al.. (2017). Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific Data. 4(1). 170117–170117. 1476 indexed citations breakdown →

Prior, Fred, Kirk Smith, Ashish Sharma, et al.. (2017). The public cancer radiology imaging collections of The Cancer Imaging Archive. Scientific Data. 4(1). 170124–170124. 93 indexed citations

Vargas, Hebert Alberto, Erich P. Huang, Yulia Lakhman, et al.. (2017). Radiogenomics of High-Grade Serous Ovarian Cancer: Multireader Multi-Institutional Study from the Cancer Genome Atlas Ovarian Cancer Imaging Research Group. Radiology. 285(2). 482–492. 48 indexed citations

10.

Burnside, Elizabeth S., Karen Drukker, Hui Li, et al.. (2015). Using computer‐extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage. Cancer. 122(5). 748–757. 54 indexed citations

11.

Moore, Stephen, Kirk Smith, Justin Kirby, et al.. (2015). De-identification of Medical Images with Retention of Scientific Research Value. Radiographics. 35(3). 727–735. 53 indexed citations

12.

Shinagare, Atul B., Raghu Vikram, C. Carl Jaffe, et al.. (2015). Radiogenomics of clear cell renal cell carcinoma: preliminary findings of The Cancer Genome Atlas–Renal Cell Carcinoma (TCGA–RCC) Imaging Research Group. Abdominal Imaging. 40(6). 1684–1692. 90 indexed citations

13.

Hatami, Masumeh, Jixin Wang, Ginu Thomas, et al.. (2015). Multicenter imaging outcomes study of The Cancer Genome Atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survival. Neuro-Oncology. 17(11). 1525–1537. 69 indexed citations

14.

Colen, Rivka R., Márk Vangel, Jixin Wang, et al.. (2014). Imaging genomic mapping of an invasive MRI phenotype predicts patient outcome and metabolic dysfunction: a TCGA glioma phenotype research group project. BMC Medical Genomics. 7(1). 30–30. 59 indexed citations

15.

Kalpathy–Cramer, Jayashree, John Freymann, Justin Kirby, Paul E. Kinahan, & Fred Prior. (2014). Quantitative Imaging Network: Data Sharing and Competitive AlgorithmValidation Leveraging The Cancer Imaging Archive. Translational Oncology. 7(1). 147–152. 76 indexed citations

16.

Jain, Rajan, Laila Poisson, David A. Gutman, et al.. (2014). Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor. Radiology. 272(2). 484–493. 177 indexed citations

17.

Clark, Kenneth, Bruce A. Vendt, Kirk Smith, et al.. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of Digital Imaging. 26(6). 1045–1057. 2948 indexed citations breakdown →

18.

Jain, Rajan, Laila Poisson, Jayant Narang, et al.. (2012). Genomic Mapping and Survival Prediction in Glioblastoma: Molecular Subclassification Strengthened by Hemodynamic Imaging Biomarkers. Radiology. 267(1). 212–220. 114 indexed citations

19.

Levy, Mia, John Freymann, Justin Kirby, et al.. (2012). Informatics methods to enable sharing of quantitative imaging research data. Magnetic Resonance Imaging. 30(9). 1249–1256. 13 indexed citations

20.

Freymann, John, Justin Kirby, John Perry, David Clunie, & C. Carl Jaffe. (2011). Image Data Sharing for Biomedical Research—Meeting HIPAA Requirements for De-identification. Journal of Digital Imaging. 25(1). 14–24. 64 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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