Should end-to-end deep learning replace handcrafted radiomics?

Irène Buvat; Joyita Dutta; Abhinav K. Jha; Eliot Siegel; Fereshteh Yousefirizi; Arman Rahmim; Tyler Bradshaw

doi:10.1007/s00259-025-07314-y

Should end-to-end deep learning replace handcrafted radiomics?

Irène Buvat
, Joyita Dutta
, Abhinav K. Jha
, Eliot Siegel
, Fereshteh Yousefirizi
, Arman Rahmim
, Tyler Bradshaw

Research output: Contribution to journal › Letter › peer-review

Original language	English
Pages (from-to)	4360-4363
Number of pages	4
Journal	European Journal of Nuclear Medicine and Molecular Imaging
Volume	52
Issue number	12
DOIs	https://doi.org/10.1007/s00259-025-07314-y
State	Published - Oct 2025

Keywords

Deep learning
Machine learning
Precision medicine
Prediction
Radiomics

Access to Document

10.1007/s00259-025-07314-y

Cite this

@article{fb567656f19c4b41bbc48d2eafe29c85,

title = "Should end-to-end deep learning replace handcrafted radiomics?",

keywords = "Deep learning, Machine learning, Precision medicine, Prediction, Radiomics",

author = "Ir{\`e}ne Buvat and Joyita Dutta and Jha, \{Abhinav K.\} and Eliot Siegel and Fereshteh Yousefirizi and Arman Rahmim and Tyler Bradshaw",

year = "2025",

month = oct,

doi = "10.1007/s00259-025-07314-y",

language = "English",

volume = "52",

pages = "4360--4363",

journal = "European Journal of Nuclear Medicine and Molecular Imaging",