ArticleLa Radiologia medica2024
PSMA-positive prostatic volume prediction with deep learning based on T2-weighted MRI.
Article in La Radiologia medica, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Emerging frontiers and challenges of artificial intelligence in PSMA-PET imaging: pioneering a new chapter in prostate cancer care.Japanese journal of radiology · 2026Review
- Global research landscape of PSMA-targeted radiopharmaceuticals: a two-decade multidimensional bibliometric analysis.Japanese journal of radiology · 2026Article
- Radiomics-based machine learning models for predicting genomic alterations in metastatic prostate cancer using PSMA PET imaging: a pilot study.EJNMMI reports · 2025Article
- AI Revolution in Radiology, Radiation Oncology and Nuclear Medicine: Transforming and Innovating the Radiological Sciences.Journal of medical imaging and radiation oncology · 2025Review
- [European journal of nuclear medicine and molecular imaging · 2025Article
- AI may help to predict thyroid nodule malignancy based on radiomics features from [EJNMMI research · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeHigh 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
methodsAll 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.
resultsOne hundred and fifty-four PSMA PET/MRI scans were available (133 [
conclusionIncreased 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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