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@ARTICLE{Buchner:282682,
author = {J. A. Buchner and J. C. Peeken$^*$ and L. Etzel$^*$ and I.
Ezhov and M. Mayinger and S. M. Christ and T. B. Brunner and
A. Wittig and B. Menze and C. Zimmer and B. Meyer and M.
Guckenberger and N. Andratschke and R. A. El Shafie and J.
Debus and S. Rogers and O. Riesterer and K. Schulze and H.
J. Feldmann and O. Blanck and C. Zamboglou$^*$ and K.
Ferentinos and A. Bilger$^*$ and A. L. Grosu$^*$ and R.
Wolff and J. S. Kirschke and K. A. Eitz$^*$ and S. E.
Combs$^*$ and D. Bernhardt$^*$ and D. Rueckert and M. Piraud
and B. Wiestler and F. Kofler},
title = {{I}dentifying core {MRI} sequences for reliable automatic
brain metastasis segmentation.},
journal = {Radiotherapy and oncology},
volume = {188},
issn = {0167-8140},
address = {Amsterdam [u.a.]},
publisher = {Elsevier Science},
reportid = {DKFZ-2023-01828},
pages = {109901},
year = {2023},
note = {Volume 188, November 2023, 109901},
abstract = {Many automatic approaches to brain tumor segmentation
employ multiple magnetic resonance imaging (MRI) sequences.
The goal of this project was to compare different
combinations of input sequences to determine which MRI
sequences are needed for effective automated brain
metastasis (BM) segmentation.We analyzed preoperative
imaging (T1-weighted sequence ± contrast-enhancement
(T1/T1-CE), T2-weighted sequence (T2), and T2
fluid-attenuated inversion recovery (T2-FLAIR) sequence)
from 339 patients with BMs from seven centers. A baseline 3D
U-Net with all four sequences and six U-Nets with plausible
sequence combinations (T1-CE, T1, T2-FLAIR, T1-CE+T2-FLAIR,
T1-CE+T1+T2-FLAIR, T1-CE+T1) were trained on 239 patients
from two centers and subsequently tested on an external
cohort of 100 patients from five centers.The model based on
T1-CE alone achieved the best segmentation performance for
BM segmentation with a median Dice similarity coefficient
(DSC) of 0.96. Models trained without T1-CE performed worse
(T1-only: DSC = 0.70 and T2-FLAIR-only: DSC = 0.73). For
edema segmentation, models that included both T1-CE and
T2-FLAIR performed best (DSC = 0.93), while the remaining
four models without simultaneous inclusion of these both
sequences reached a median DSC of 0.81-0.89.A T1-CE-only
protocol suffices for the segmentation of BMs. The
combination of T1-CE and T2-FLAIR is important for edema
segmentation. Missing either T1-CE or T2-FLAIR decreases
performance. These findings may improve imaging routines by
omitting unnecessary sequences, thus allowing for faster
procedures in daily clinical practice while enabling optimal
neural network-based target definitions.},
keywords = {CNN (Other) / FLAIR (Other) / MRI sequences (Other) / T1
(Other) / U-net (Other) / brain metastases (Other) / deep
learning (Other) / segmentation (Other)},
cin = {MU01 / FR01},
ddc = {610},
cid = {I:(DE-He78)MU01-20160331 / I:(DE-He78)FR01-20160331},
pnm = {899 - ohne Topic (POF4-899)},
pid = {G:(DE-HGF)POF4-899},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:37678623},
doi = {10.1016/j.radonc.2023.109901},
url = {https://inrepo02.dkfz.de/record/282682},
}