Clinical Decision Support for Axillary Lymph Node Staging in Newly Diagnosed Breast Cancer Patients Based on
PET/MRI
breast cancer
lymph node metastases
machine learning
Journal
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
ISSN: 1535-5667
Titre abrégé: J Nucl Med
Pays: United States
ID NLM: 0217410
Informations de publication
Date de publication:
02 2023
02 2023
Historique:
received:
16
03
2022
revised:
19
08
2022
pubmed:
23
9
2022
medline:
4
2
2023
entrez:
22
9
2022
Statut:
ppublish
Résumé
In addition to its high prognostic value, the involvement of axillary lymph nodes in breast cancer patients also plays an important role in therapy planning. Therefore, an imaging modality that can determine nodal status with high accuracy in patients with primary breast cancer is desirable. Our purpose was to investigate whether, in newly diagnosed breast cancer patients, machine-learning prediction models based on simple assessable imaging features on MRI or PET/MRI are able to determine nodal status with performance comparable to that of experienced radiologists; whether such models can be adjusted to achieve low rates of false-negatives such that invasive procedures might potentially be omitted; and whether a clinical framework for decision support based on simple imaging features can be derived from these models.
Identifiants
pubmed: 36137756
pii: jnumed.122.264138
doi: 10.2967/jnumed.122.264138
pmc: PMC9902847
doi:
Substances chimiques
Fluorodeoxyglucose F18
0Z5B2CJX4D
Radiopharmaceuticals
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
304-311Informations de copyright
© 2023 by the Society of Nuclear Medicine and Molecular Imaging.
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