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Lars J. Grimm

ResearcherPublications, citations & collaboration network

Lars J. Grimm is a researcher indexed in ScholarIQ from OpenAlex & ORCID. ScholarIQ records 286 works, 5,089 citations, an h-index of 34 and an i10-index of 91.

286
Works
5,089
Citations
34
h-index
91
i10-index

How has Lars J. Grimm's publication output changed over time?

ScholarIQpublication output · 2014–2024

Output grew0% over the shown period — from 1 works in 2014 to 1 in 2024.

1
4
1
2
1
1
1
2014201520172018201920212024

What are the most-cited papers on Lars J. Grimm?

ScholarIQmost cited works
Benefits and Harms of Breast Cancer Screening
Evan R. Myers, Patricia G. Moorman, Jennifer M. Gierisch, Laura J. Havrilesky, Lars J. Grimm, Sujata V. Ghate, Brittany A. Davidson, Ranee Chatterjee Mongtomery, Matthew J. Crowley, Douglas C McCrory, Amy Kendrick, Gillian D Sanders
JAMA. 2015640 Citations
A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features
Ashirbani Saha, Michael R. Harowicz, Lars J. Grimm, Connie E. Kim, Sujata V. Ghate, Ruth Walsh, Maciej A. Mazurowski
British Journal of Cancer. 2018305 CitationsOPEN ACCESS
Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging
Maciej A. Mazurowski, Jing Zhang, Lars J. Grimm, Sora C. Yoon, James I. Silber
S50280174. 2014247 Citations
Abbreviated Screening Protocol for Breast MRI
Lars J. Grimm, Mary Scott Soo, Sora C. Yoon, Connie Kim, Sujata V. Ghate, Karen S. Johnson
Academic Radiology. 2015156 Citations

Related on ScholarIQ

Duke University
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Benefits and Harms of Breast Cancer Screening
Paper
A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features
Paper
Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging
Paper
Abbreviated Screening Protocol for Breast MRI
Paper
Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms
Paper
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