Authors

• Lisa R. Rogers (ORCID: https://orcid.org/0000-0003-0942-2691)
• Mainak Bardhan (ORCID: https://orcid.org/0000-0002-4106-409X)
• Michael C. Veronesi (ORCID: https://orcid.org/0000-0001-8551-151X)
• Ameya Nayate
• Hyemin Um (ORCID: https://orcid.org/0000-0003-2083-7484)
• Marwa Ismail (ORCID: https://orcid.org/0000-0002-1396-0372)
• Pallavi Tiwari (ORCID: https://orcid.org/0000-0001-9477-4856)
• Virginia Hill (ORCID: https://orcid.org/0000-0001-9146-9370)
• Mustafa Basree (ORCID: https://orcid.org/0000-0003-2048-5116)
• Ankush Bhatia (ORCID: https://orcid.org/0000-0001-5292-2822)
• Lan Lŭ
• Manmeet S. Ahluwalia
• Andrew M. Baschnagel
• Alan B. McMillan
• Chengnan Li
• Jennifer S. Yu
• Sushant Puri
• Prateek Prasanna (ORCID: https://orcid.org/0000-0002-3068-3573)
• Anthony Higinbotham

Institutions

• Northwestern University (US)
• Baptist Hospital of Miami (US)
• Cleveland Clinic (US)
• University of Wisconsin–Madison (US)
• Henry Ford Health System (US)
• Oregon Health & Science University (US)
• University Hospitals of Cleveland (US)
• Baptist Health South Florida (US)
• University of Wisconsin Carbone Cancer Center (US)
• William S. Middleton Memorial Veterans Hospital (US)
• Johns Hopkins Hospital (US)
• University of Virginia (US)
• Stony Brook University (US)

Publication Details

Journal

Radiology Imaging Cancer

Published

2026-04-17

DOI

https://doi.org/10.1148/rycan.250435

Primary Topic

Radiomics and Machine Learning in Medical Imaging

Type

article

Field-Weighted Citation Impact

0.00

Funders

• U.S. Department of Veterans Affairs
• V Foundation for Cancer Research
• Wisconsin Alumni Research Foundation
• University of Wisconsin Carbone Cancer Center
• National Cancer Institute
• National Center for Advancing Translational Sciences