ArticleEJNMMI reports2025
Radiomics-based machine learning models for predicting genomic alterations in metastatic prostate cancer using PSMA PET imaging: a pilot study.
Article in EJNMMI reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Machine learning-based models for tumor mutation burden prediction in gastrointestinal cancers: a systematic review and meta-analysis.Discover oncology · 2026Review
- Integrating Multiparametric MRI and PSMA PET Imaging in Prostate Cancer: Toward a Unified Diagnostic and Risk-Stratification Paradigm.Medicina (Kaunas, Lithuania) · 2026Review
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Authors and funding
10 authors.
Funding
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Abstract
BACKGROUND AND
objectiveGenomic characterization of metastatic prostate cancer (mPCa) plays a pivotal role in guiding precision oncology. This study aimed to evaluate the feasibility of combining radiomics and clinical data within a machine learning (ML) framework to non-invasively predict key genomic mutations in patients with mPCa undergoing PSMA PET imaging.
methodsA retrospective cohort of 14 mPCa patients who underwent [ KEY
findingsFourteen patients with mPCa were included, and 46 lesions were analysed. Genomic alterations included mutations in TP53, TMPRSS2, PTEN, BRCA1/2, ATM, and others. Owing to data limitations, mutations other than TP53, TMPRSS2, and PTEN were grouped into a composite "OTHER" category. The best-performing clinical-radiomics ML models achieved AUCs of 91.11% (TP53), 84.44% (TMPRSS2), 80.00% (PTEN), and 77.78% (OTHER). Selected feature stability was consistent across repeated runs. CONCLUSIONS AND CLINICAL IMPLICATIONS: Clinical-radiomics ML models based on PSMA PET imaging show promising accuracy in predicting actionable genomic alterations in mPCa. These findings support further investigation into radiogenomics modelling as a complementary, non-invasive tool to inform molecular profiling and treatment stratification.
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