Jane P. Ko

6.2k total citations · 2 hit papers
108 papers, 4.0k citations indexed

About

Jane P. Ko is a scholar working on Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging and Critical Care and Intensive Care Medicine. According to data from OpenAlex, Jane P. Ko has authored 108 papers receiving a total of 4.0k indexed citations (citations by other indexed papers that have themselves been cited), including 74 papers in Pulmonary and Respiratory Medicine, 58 papers in Radiology, Nuclear Medicine and Imaging and 12 papers in Critical Care and Intensive Care Medicine. Recurrent topics in Jane P. Ko's work include Lung Cancer Diagnosis and Treatment (58 papers), Radiomics and Machine Learning in Medical Imaging (26 papers) and Medical Imaging Techniques and Applications (25 papers). Jane P. Ko is often cited by papers focused on Lung Cancer Diagnosis and Treatment (58 papers), Radiomics and Machine Learning in Medical Imaging (26 papers) and Medical Imaging Techniques and Applications (25 papers). Jane P. Ko collaborates with scholars based in United States, Germany and South Korea. Jane P. Ko's co-authors include David P. Naidich, Margrit Betke, Sanjeev Bhalla, Fernando Uliana Kay, Jonathan H. Chung, Jeffrey P. Kanne, Michael Chung, Suhny Abbara, Harold Litt and Scott Simpson and has published in prestigious journals such as Scientific Reports, Radiology and CHEST Journal.

In The Last Decade

Jane P. Ko

103 papers receiving 3.9k citations

Hit Papers

Radiological Society of North America Expert Consensus St... 2020 2026 2022 2024 2020 2020 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jane P. Ko United States 30 2.0k 1.9k 895 497 461 108 4.0k
Seth Kligerman United States 30 1.5k 0.7× 1.4k 0.7× 817 0.9× 594 1.2× 503 1.1× 113 3.5k
Helmut Prosch Austria 38 1.5k 0.7× 1.5k 0.8× 726 0.8× 851 1.7× 1.1k 2.4× 227 4.8k
Travis S. Henry United States 26 1.4k 0.7× 1.5k 0.8× 848 0.9× 419 0.8× 412 0.9× 101 3.5k
Fernando Uliana Kay United States 20 1.2k 0.6× 980 0.5× 651 0.7× 386 0.8× 247 0.5× 61 2.4k
Jonathan H. Chung United States 31 1.2k 0.6× 2.9k 1.5× 720 0.8× 436 0.9× 459 1.0× 170 4.6k
Soon Ho Yoon South Korea 32 1.7k 0.8× 1.3k 0.7× 907 1.0× 859 1.7× 767 1.7× 179 4.1k
Wenbin Ji China 17 1.8k 0.9× 530 0.3× 1.2k 1.3× 601 1.2× 426 0.9× 84 3.3k
Peipei Pang China 26 2.9k 1.4× 798 0.4× 1.2k 1.3× 693 1.4× 234 0.5× 80 3.9k
Elaine Lee Hong Kong 23 1.9k 0.9× 613 0.3× 768 0.9× 472 0.9× 286 0.6× 110 3.6k

Countries citing papers authored by Jane P. Ko

Since Specialization
Citations

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

Fields of papers citing papers by Jane P. Ko

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jane P. Ko. 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 Jane P. Ko. The network helps show where Jane P. Ko may publish in the future.

Co-authorship network of co-authors of Jane P. Ko

This figure shows the co-authorship network connecting the top 25 collaborators of Jane P. Ko. A scholar is included among the top collaborators of Jane P. Ko 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 Jane P. Ko. Jane P. Ko 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.
Raad, Roy A., et al.. (2025). Imaging and Management of Subsolid Lung Nodules. Radiologic Clinics of North America. 63(4). 517–535. 1 indexed citations
2.
Fletcher, Joel G., Akitoshi Inoue, Kelly K. Horst, et al.. (2024). Photon-counting CT in Thoracic Imaging: Early Clinical Evidence and Incorporation Into Clinical Practice. Radiology. 310(3). e231986–e231986. 19 indexed citations
3.
Azour, Lea, Leopoldo N. Segal, Rany Condos, et al.. (2024). Low-field MRI lung opacity severity associated with decreased DLCO in post-acute Covid-19 patients. Clinical Imaging. 115. 110307–110307.
4.
Lee, Jong Eun, Kum Ju Chae, Kwang Nam Jin, et al.. (2024). Diagnostic performance of radiologists in distinguishing post-COVID-19 residual abnormalities from interstitial lung abnormalities. European Radiology. 35(4). 2265–2274.
5.
O’Donnell, Thomas, et al.. (2024). Differentiation of intrathoracic lymph node histopathology by volumetric dual energy CT radiomic analysis. Clinical Imaging. 114. 110252–110252.
6.
Henry, Travis S., Mark F. Berry, Geoffrey B. Johnson, et al.. (2023). ACR Appropriateness Criteria® Incidentally Detected Indeterminate Pulmonary Nodule. Journal of the American College of Radiology. 20(11). S455–S470. 5 indexed citations
7.
Azour, Lea, et al.. (2022). Current imaging of PE and emerging techniques: is there a role for artificial intelligence?. Clinical Imaging. 88. 24–32. 3 indexed citations
8.
Patel, Smita, et al.. (2022). Pitfalls and Pearls of Imaging Non-traumatic Thoracic Aortic Disease. Seminars in Ultrasound CT and MRI. 43(3). 204–220. 1 indexed citations
9.
Ko, Jane P., Jonathan Goldstein, Larry Latson, et al.. (2021). Chest CT Angiography for Acute Aortic Pathologic Conditions: Pearls and Pitfalls. Radiographics. 41(2). 399–424. 22 indexed citations
10.
Mossa‐Basha, Mahmud, Mitchell D. Schnall, Carolyn C. Meltzer, et al.. (2021). Special Report of the RSNA COVID-19 Task Force: Crisis Leadership of Major Health System Radiology Departments during COVID-19. Radiology. 299(1). E187–E192. 6 indexed citations
11.
Strange, Chad D., Ioannis Vlahos, Mylene T. Truong, et al.. (2021). Pearls and Pitfalls in Postsurgical Imaging of the Chest. Seminars in Ultrasound CT and MRI. 42(6). 563–573. 1 indexed citations
12.
Moore, William H., James S. Babb, David Kaminetzky, et al.. (2020). Pulmonary Embolism at CT Pulmonary Angiography in Patients with COVID-19. Radiology Cardiothoracic Imaging. 2(4). e200308–e200308. 48 indexed citations
13.
Segal, Leopoldo N., José C. Clemente, Benjamin G. Wu, et al.. (2016). Randomised, double-blind, placebo-controlled trial with azithromycin selects for anti-inflammatory microbial metabolites in the emphysematous lung. Thorax. 72(1). 13–22. 124 indexed citations
14.
Ko, Jane P., James Suh, Joanna G. Escalon, et al.. (2016). Lung Adenocarcinoma: Correlation of Quantitative CT Findings with Pathologic Findings. Radiology. 280(3). 931–939. 68 indexed citations
15.
Ko, Jane P., et al.. (2015). Imaging the Solitary Pulmonary Nodule. Clinics in Chest Medicine. 36(2). 161–178. 11 indexed citations
16.
Fantauzzi, John, et al.. (2012). Clinical Significance of Lung Nodules Reported on Abdominal CT. American Journal of Roentgenology. 198(4). 793–799. 21 indexed citations
17.
Ko, Jane P., et al.. (2011). Pulmonary Nodule Detection, Characterization, and Management With Multidetector Computed Tomography. Journal of Thoracic Imaging. 26(2). 90–105. 74 indexed citations
18.
Godoy, Myrna C. B., et al.. (2010). Ground-Glass Centrilobular Nodules on Multidetector CT Scan. CHEST Journal. 138(2). 427–433. 1 indexed citations
19.
Ko, Jane P., Henry Rusinek, David P. Naidich, et al.. (2003). Wavelet Compression of Low-Dose Chest CT Data: Effect on Lung Nodule Detection. Radiology. 228(1). 70–75. 49 indexed citations
20.
Betke, Margrit, et al.. (2003). Landmark detection in the chest and registration of lung surfaces with an application to nodule registration. Medical Image Analysis. 7(3). 265–281. 93 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.

Explore authors with similar magnitude of impact

Rankless by CCL
2026