Multicentric development and evaluation of [
Machine learning
Non-small cell lung cancer
Radiomics
Stereotactic body radiation therapy
[18F]FDG PET/CT
Journal
European journal of nuclear medicine and molecular imaging
ISSN: 1619-7089
Titre abrégé: Eur J Nucl Med Mol Imaging
Pays: Germany
ID NLM: 101140988
Informations de publication
Date de publication:
21 Nov 2023
21 Nov 2023
Historique:
received:
29
08
2023
accepted:
03
11
2023
medline:
21
11
2023
pubmed:
21
11
2023
entrez:
21
11
2023
Statut:
aheadofprint
Résumé
To develop machine learning models to predict regional and/or distant recurrence in patients with early-stage non-small cell lung cancer (ES-NSCLC) after stereotactic body radiation therapy (SBRT) using [ We retrospectively collected 464 patients (60% for training and 40% for testing) from University Hospital of Liège and 63 patients from University Hospital of Brest (external testing set) with ES-NSCLC treated with SBRT between 2010 and 2020 and who had undergone pretreatment [ In the training (n = 273) and testing sets (n = 191 and n = 63), the clinical model achieved moderate performances to predict regional and/or distant recurrence with C-statistics from 0.53 to 0.59 (95% CI, 0.41, 0.67). The radiomic (original_firstorder_Entropy, original_gldm_LowGrayLevelEmphasis and original_glcm_DifferenceAverage) model achieved higher predictive ability in the training set and kept the same performance in the testing sets, with C-statistics from 0.70 to 0.78 (95% CI, 0.63, 0.88) while the combined model performs moderately well with C-statistics from 0.50 to 0.62 (95% CI, 0.37, 0.69). Radiomic features extracted from pre-SBRT analog and digital [
Identifiants
pubmed: 37987783
doi: 10.1007/s00259-023-06510-y
pii: 10.1007/s00259-023-06510-y
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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