Edward A. Eikman

1.3k total citations
41 papers, 979 citations indexed

About

Edward A. Eikman is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Epidemiology. According to data from OpenAlex, Edward A. Eikman has authored 41 papers receiving a total of 979 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Radiology, Nuclear Medicine and Imaging, 14 papers in Pulmonary and Respiratory Medicine and 8 papers in Epidemiology. Recurrent topics in Edward A. Eikman's work include Medical Imaging Techniques and Applications (13 papers), Radiomics and Machine Learning in Medical Imaging (11 papers) and Lung Cancer Diagnosis and Treatment (6 papers). Edward A. Eikman is often cited by papers focused on Medical Imaging Techniques and Applications (13 papers), Radiomics and Machine Learning in Medical Imaging (11 papers) and Lung Cancer Diagnosis and Treatment (6 papers). Edward A. Eikman collaborates with scholars based in United States, China and Netherlands. Edward A. Eikman's co-authors include Robert J. Gillies, Robert A. Gatenby, Dung‐Tsa Chen, Mokenge P. Malafa, Matthew B. Schabath, Anders Berglund, Steven A. Eschrich, Alfredo A. Santillan, Yuhua Gu and Olya Grove and has published in prestigious journals such as Journal of Clinical Oncology, Annals of Internal Medicine and The Journal of Immunology.

In The Last Decade

Edward A. Eikman

37 papers receiving 932 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Edward A. Eikman United States 14 570 362 232 166 109 41 979
Pradnya D. Patil United States 18 577 1.0× 644 1.8× 563 2.4× 138 0.8× 102 0.9× 66 1.2k
William C. Allsbrook United States 19 194 0.3× 854 2.4× 223 1.0× 223 1.3× 218 2.0× 33 1.4k
Nicola H. Strickland United Kingdom 15 358 0.6× 349 1.0× 76 0.3× 117 0.7× 88 0.8× 43 928
Ok Hee Woo South Korea 23 801 1.4× 311 0.9× 303 1.3× 158 1.0× 254 2.3× 106 1.6k
Zafer Koçak Türkiye 19 279 0.5× 378 1.0× 278 1.2× 186 1.1× 15 0.1× 75 1.1k
Michela Gabelloni Italy 17 650 1.1× 248 0.7× 178 0.8× 192 1.2× 99 0.9× 42 939
Matthew W. Watkins United States 20 335 0.6× 273 0.8× 141 0.6× 903 5.4× 34 0.3× 47 2.1k
Munenobu Nogami Japan 28 1.3k 2.3× 1.0k 2.8× 180 0.8× 148 0.9× 32 0.3× 91 2.2k
Jia Hua China 22 917 1.6× 175 0.5× 104 0.4× 118 0.7× 78 0.7× 61 1.4k

Countries citing papers authored by Edward A. Eikman

Since Specialization
Citations

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

Fields of papers citing papers by Edward A. Eikman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Edward A. Eikman. 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 Edward A. Eikman. The network helps show where Edward A. Eikman may publish in the future.

Co-authorship network of co-authors of Edward A. Eikman

This figure shows the co-authorship network connecting the top 25 collaborators of Edward A. Eikman. A scholar is included among the top collaborators of Edward A. Eikman 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 Edward A. Eikman. Edward A. Eikman 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.
Grove, Olya, Anders Berglund, Matthew B. Schabath, et al.. (2021). Correction: Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma. PLoS ONE. 16(3). e0248541–e0248541. 8 indexed citations
2.
Gerstner, Elizabeth R., Zheng Zhang, James R. Fink, et al.. (2016). ACRIN 6684: Assessment of Tumor Hypoxia in Newly Diagnosed Glioblastoma Using 18F-FMISO PET and MRI. Clinical Cancer Research. 22(20). 5079–5086. 85 indexed citations
3.
Pimiento, José M., Richard D. Kim, Dung‐Tsa Chen, et al.. (2015). Metabolic Activity by 18F-FDG-PET/CT Is Prognostic for Stage I and II Pancreatic Cancer. Clinical Nuclear Medicine. 41(3). 177–181. 25 indexed citations
4.
Schabath, Matthew B., Ying Liu, Anders Berglund, et al.. (2015). Semiquantitative Computed Tomography Characteristics for Lung Adenocarcinoma and Their Association With Lung Cancer Survival. Clinical Lung Cancer. 16(6). e141–e163. 44 indexed citations
5.
Kumar, Virendra, Kavindra Nath, Claudia G. Berman, et al.. (2013). Variance of SUVs for FDG-PET/CT is Greater in Clinical Practice Than Under Ideal Study Settings. Clinical Nuclear Medicine. 38(3). 175–182. 64 indexed citations
6.
Figura, Nicholas, Kujtim Latifi, Thomas J. Dilling, et al.. (2013). Dosimetric Implications of Treating 4D PET/CT-Defined Maximum Inhale Versus Exhale Target Volumes in Esophageal Cancer. Practical Radiation Oncology. 3(2). S34–S35. 1 indexed citations
7.
Kurland, Brenda F., Elizabeth R. Gerstner, James M. Mountz, et al.. (2012). Promise and pitfalls of quantitative imaging in oncology clinical trials. Magnetic Resonance Imaging. 30(9). 1301–1312. 67 indexed citations
8.
Beasley, Georgia M., Colin M. Parsons, Gloria Broadwater, et al.. (2012). A Multicenter Prospective Evaluation of the Clinical Utility of F-18 FDG-PET/CT in Patients With AJCC Stage IIIB or IIIC Extremity Melanoma. Annals of Surgery. 256(2). 350–356. 28 indexed citations
9.
10.
Tanvetyanon, Tawee, Edward A. Eikman, & Gerold Bepler. (2009). In Reply. Journal of Clinical Oncology. 27(5). 832–832. 1 indexed citations
11.
Farma, Jeffrey M., Alfredo A. Santillan, Marcovalerio Melis, et al.. (2008). PET/CT Fusion Scan Enhances CT Staging in Patients with Pancreatic Neoplasms. Annals of Surgical Oncology. 15(9). 2465–2471. 120 indexed citations
12.
Song, Dansheng, et al.. (2007). Computer-Aided Mass Detection Based on Ipsilateral Multiview Mammograms. Academic Radiology. 14(5). 530–538. 35 indexed citations
13.
Luo, Ping, et al.. (2006). Analysis of a Mammography Teaching Program Based on an Affordance Design Model. Academic Radiology. 13(12). 1542–1552. 5 indexed citations
14.
Eikman, Edward A., et al.. (2002). Acute and Chronic Aortic Dissection. PubMed. 4(4). 231–241. 4 indexed citations
15.
Bennett, Jennifer L., et al.. (2000). Comparison of Nitroglycerin magnetic resonance imaging with Dobutamine echocardiography for predicting recovery of function after revascularization. The American Journal of Cardiology. 85(10). 1250–1252. 6 indexed citations
16.
Drane, Walter E., et al.. (1996). Prospective localization of epileptogenic foci: comparison of PET and SPECT with site of surgery and clinical outcome.. Radiology. 199(2). 375–380. 11 indexed citations
17.
Alagona, Peter, et al.. (1994). Regional distribution of 2-deoxy-2[18F]-fluoro-D-glucose for metabolic imaging using positron emission tomography. International journal of cardiac imaging. 10(2). 137–143. 4 indexed citations
18.
Eikman, Edward A., et al.. (1979). Computer-assisted liver-mass estimation from gamma-camera images.. PubMed. 20(2). 144–8. 7 indexed citations
19.
Eikman, Edward A.. (1979). Radionuclide hepatobiliary procedures: when can HIDA help?. PubMed. 20(4). 358–61. 8 indexed citations
20.

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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