Development of PSMA-PET-guided CT-based radiomic signature to predict biochemical recurrence after salvage radiotherapy.
Outcome prediction
PSMA-PET/CT
Personalization
Prostate cancer
Radiomics
Salvage radiotherapy
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:
Jul 2023
Jul 2023
Historique:
received:
01
12
2022
accepted:
07
03
2023
medline:
12
6
2023
pubmed:
18
3
2023
entrez:
17
3
2023
Statut:
ppublish
Résumé
To develop a CT-based radiomic signature to predict biochemical recurrence (BCR) in prostate cancer patients after sRT guided by positron-emission tomography targeting prostate-specific membrane antigen (PSMA-PET). Consecutive patients, who underwent Among 99 patients, median interval until BCR was the radiomic models outperformed clinical models and combined clinical-radiomic models for prediction of BCR with a C-index of 0.71 compared to 0.53 and 0.63 in the test sets, respectively. In contrast to the other models, the radiomic model achieved significantly improved patient stratification in Kaplan-Meier analysis. The radiomic and clinical-radiomic model achieved a significantly better time-dependent net reclassification improvement index (0.392 and 0.762, respectively) compared to the clinical model. Decision curve analysis demonstrated a clinical net benefit for both models. Mean intensity was the most predictive radiomic feature. This is the first study to develop a PSMA-PET-guided CT-based radiomic model to predict BCR after sRT. The radiomic models outperformed clinical models and might contribute to guide personalized treatment decisions.
Identifiants
pubmed: 36929180
doi: 10.1007/s00259-023-06195-3
pii: 10.1007/s00259-023-06195-3
pmc: PMC10250433
doi:
Substances chimiques
Gallium Radioisotopes
0
Gallium Isotopes
0
Types de publication
Multicenter Study
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2537-2547Subventions
Organisme : Deutschen Konsortium für Translationale Krebsforschung
ID : IMPRO-REC
Informations de copyright
© 2023. The Author(s).
Références
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