ArticleEuropean journal of nuclear medicine and molecular imaging2025
Prognostic value of body composition out of PSMA-PET/CT in prostate cancer patients undergoing PSMA-therapy.
Article in European journal of nuclear medicine and molecular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Skeletal muscle radiodensity as an automated survival predictor in men with metastatic castration-resistant prostate cancer undergoing PSMA-targeted radioligand therapy.European journal of nuclear medicine and molecular imaging · 2026Article
- Biomarkers for Precision Prognosis in Prostate Cancer: Imaging, Molecular, and Integrated Approaches.Cancers · 2026Review
- Body composition radiomics combined with machine learning for early recurrence prediction in intrahepatic cholangiocarcinoma following curative surgery: A Multi-Center study.European journal of nuclear medicine and molecular imaging · 2026Article
- Bone Mineral Density and Intermuscular Fat Derived from Computed Tomography Images Using Artificial Intelligence Are Associated with Fracture Healing.Bioengineering (Basel, Switzerland) · 2025Article
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8 authors.
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Abstract
backgroundThis retrospective study aims to develop a deep learning-based approach to whole-body CT segmentation out of standard PSMA-PET-CT to assess body composition in metastatic castration resistant prostate cancer (mCRPC) patients prior to [
methodsWe used a deep learning segmentation model to perform fully automated segmentation of different tissue compartments, including visceral- (VAT), subcutaneous- (SAT), intra/intermuscular- adipose tissue (IMAT) from [
resultsThe automated segmentation model was useful for delineating different adipose tissue compartments and skeletal muscle across different patient anatomies. Analyses revealed significant correlations between lower SAT and higher IMAT ratios and poorer therapeutic outcomes in Cox regression analysis (SAT/TAT: p = 0.038; IMAT/TAT: p < 0.001) in the 3D model. In the single slice approach only IMAT/SAT was significantly associated with survival in Cox regression analysis (p < 0.001; SAT/TAT: p > 0.05). IMAT ratio remained an independent predictor of survival in multivariate analysis when including PSMA-PET and blood-based prognostic factors.
conclusionsIn this proof-of-principle study the implementation of a deep learning-based whole-body analysis provides a robust and detailed CT-based assessment of body composition in mCRPC patients undergoing RLT. Potential prognostic parameters have to be corroborated in larger prospective datasets.
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