Multi-institutional MRI-based radiomic pilot study to measure the variations between scanner vendors and imaging sessions

Some radiomic features showed significant intra-scanner variations. This study demonstrated that a standardized radiomic phantom is required to characterize individual scanners and MR sequences for establishing the baseline of radiomic features, which could be important for multi-institutional radiomic studies using MRI.

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Publication Details

Journal
Frontiers in Oncology
Published
2026-03-03
DOI
https://doi.org/10.3389/fonc.2026.1686601
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00

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article

Multi-institutional MRI-based radiomic pilot study to measure the variations between scanner vendors and imaging sessions

Jennie Crosby, C. Glide-Hurst, Yunfeng Cui, J. Richard Monroe et al.
Frontiers in Oncology
Radiomics and Machine Learning in Medical Imaging
article

Multi-institutional MRI-based radiomic pilot study to measure the variations between scanner vendors and imaging sessions

Jennie Crosby, C. Glide-Hurst, Yunfeng Cui, J. Richard Monroe, Hui Li, D. Lee, Jason W Sohn, B. R. Paliwal, Suong Thi-Thanh Duong, Khadija Rafique Sheikh, Michael Boss, Taeho Kim, Jung Hun Oh, Mi Huang, Ying Xiao
article en

Abstract

Some radiomic features showed significant intra-scanner variations. This study demonstrated that a standardized radiomic phantom is required to characterize individual scanners and MR sequences for establishing the baseline of radiomic features, which could be important for multi-institutional radiomic studies using MRI.

Frontiers in OncologyVol. 16
American College of Radiology (US), Allegheny Health Network (US), Memorial Sloan Kettering Cancer Center (US), Pennsylvania Department of Environmental Protection (US), University of Wisconsin–Madison (US), Johns Hopkins University (US), Medical College of Wisconsin (US), Washington University in St. Louis (US), Johns Hopkins Medicine (US), Johns Hopkins University Applied Physics Laboratory (US), St. Louis VA Medical Center (US), Duke University Hospital (US), Geisinger Commonwealth School of Medicine (US), University of Pennsylvania (US)
Sun Nuclear Corporation, National Institutes of Health
Openalex Percentile: Top 37%
Radiomics and Machine Learning in Medical Imaging
DOIOpen Access PDFOpenAlex
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Multi-institutional MRI-based radiomic pilot study to measure the variations between scanner vendors and imaging sessions — Jennie Crosby, C. Glide-Hurst, et al. · Frontiers in Oncology (2026) | TGRS Research Map | TGRS