Novel 4D radiomics applied to dynamic FES PET images to improve prediction of breast cancer response to ER-targeted therapy

Authors

Jennifer M. Specht (ORCID: https://orcid.org/0000-0003-1484-2113)
Lanell M. Peterson (ORCID: https://orcid.org/0000-0002-1613-5909)
Mark Muzi (ORCID: https://orcid.org/0000-0002-8767-191X)
Despina Kontos (ORCID: https://orcid.org/0000-0001-9031-5126)
David A. Mankoff (ORCID: https://orcid.org/0000-0002-6971-5413)
Carla R. Zeballos Torrez
Eric A. Cohen (ORCID: https://orcid.org/0000-0002-5067-8835)
Andrew William Chen (ORCID: https://orcid.org/0000-0001-7781-1109)
Hannah Linden

Institutions

University of Washington (US)
Columbia University Irving Medical Center (US)
University of Pennsylvania (US)

Publication Details

Journal: European Journal of Nuclear Medicine and Molecular Imaging
Published: 2025-11-23
DOI: https://doi.org/10.1007/s00259-025-07570-y
Primary Topic: Radiomics and Machine Learning in Medical Imaging
Type: article
Field-Weighted Citation Impact: 0.00

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article

Novel 4D radiomics applied to dynamic FES PET images to improve prediction of breast cancer response to ER-targeted therapy

Jennifer M. Specht, Lanell M. Peterson, Mark Muzi, Despina Kontos et al.

Nov 23, 2025

European Journal of Nuclear Medicine and Molecular Imaging

Radiomics and Machine Learning in Medical Imaging

article

Novel 4D radiomics applied to dynamic FES PET images to improve prediction of breast cancer response to ER-targeted therapy

Jennifer M. Specht, Lanell M. Peterson, Mark Muzi, Despina Kontos, David A. Mankoff, Carla R. Zeballos Torrez, Eric A. Cohen, Andrew William Chen, Hannah Linden

November 23, 2025article en

Abstract

No abstract available for this paper.

European Journal of Nuclear Medicine and Molecular ImagingVol. 53(5)

University of Washington (US), Columbia University Irving Medical Center (US), University of Pennsylvania (US)

Openalex Percentile: Top 49%

Radiomics and Machine Learning in Medical Imaging

DOI Open Access PDF OpenAlex

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