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Open Access 03-05-2024 | Prostate Cancer | Computer Application

PSMA-positive prostatic volume prediction with deep learning based on T2-weighted MRI

Authors: Riccardo Laudicella, Albert Comelli, Moritz Schwyzer, Alessandro Stefano, Ender Konukoglu, Michael Messerli, Sergio Baldari, Daniel Eberli, Irene A. Burger

Published in: La radiologia medica

Abstract

Purpose

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.

Material and methods

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.

Results

One hundred and fifty-four PSMA PET/MRI scans were available (133 [⁶⁸Ga]Ga-PSMA-11 and 21 [¹⁸F]PSMA-1007). Significant cancer was present in 127 of them. The whole dataset was divided into a training cohort (n = 124) and a test cohort (n = 30). The C-ENet was able to predict the PSMA PET map with a dice similarity coefficient of 69.5 ± 15.6%.

Conclusion

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.

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Title: PSMA-positive prostatic volume prediction with deep learning based on T2-weighted MRI
Authors: Riccardo Laudicella
Albert Comelli
Moritz Schwyzer
Alessandro Stefano
Ender Konukoglu
Michael Messerli
Sergio Baldari
Daniel Eberli
Irene A. Burger
Publication date: 03-05-2024
Publisher: Springer Milan
Keywords: Prostate Cancer
Prostate Cancer
Published in: La radiologia medica
Print ISSN: 0033-8362
Electronic ISSN: 1826-6983
DOI: https://doi.org/10.1007/s11547-024-01820-z

Keynote webinar | Spotlight on medication adherence

Springer Medicine

PSMA-positive prostatic volume prediction with deep learning based on T2-weighted MRI

Abstract

Purpose

Material and methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Purpose

Material and methods

Results

Conclusion

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