PSMA-positive prostatic volume prediction with deep learning based on T2-weighted MRI.
Artificial intelligence
Neural network
PET/MRI
Prediction
Prostate cancer
Journal
La Radiologia medica
ISSN: 1826-6983
Titre abrégé: Radiol Med
Pays: Italy
ID NLM: 0177625
Informations de publication
Date de publication:
03 May 2024
03 May 2024
Historique:
received:
31
07
2023
accepted:
16
04
2024
medline:
3
5
2024
pubmed:
3
5
2024
entrez:
3
5
2024
Statut:
aheadofprint
Résumé
High PSMA expression might be correlated with structural characteristics such as growth patterns on histopathology, not recognized by the human eye on MRI images. Deep structural image analysis might be able to detect such differences and therefore predict if a lesion would be PSMA positive. Therefore, we aimed to train a neural network based on PSMA PET/MRI scans to predict increased prostatic PSMA uptake based on the axial T2-weighted sequence alone. All patients undergoing simultaneous PSMA PET/MRI for PCa staging or biopsy guidance between April 2016 and December 2020 at our institution were selected. To increase the specificity of our model, the prostatic beds on PSMA PET scans were dichotomized in positive and negative regions using an SUV threshold greater than 4 to generate a PSMA PET map. Then, a C-ENet was trained on the T2 images of the training cohort to generate a predictive prostatic PSMA PET map. One hundred and fifty-four PSMA PET/MRI scans were available (133 [ Increased prostatic PSMA uptake on PET might be estimated based on T2 MRI alone. Further investigation with larger cohorts and external validation is needed to assess whether PSMA uptake can be predicted accurately enough to help in the interpretation of mpMRI.
Identifiants
pubmed: 38700556
doi: 10.1007/s11547-024-01820-z
pii: 10.1007/s11547-024-01820-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s).
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