Skeletal muscle radiodensity as an automated survival predictor in men with metastatic castration-resistant prostate cancer undergoing PSMA-targeted radioligand therapy.
Muscle quality visible on routine imaging predicts survival in prostate cancer patients receiving radiotherapy, enabling better risk planning.
In the largest PSMA-RLT cohort to date (n=410 mCRPC patients), automated AI-based skeletal muscle radiodensity quantification from the L3-level CT component of routine pre-therapeutic PET/CT scans independently predicted overall survival with a hazard ratio of 1.87 (P<0.001), stratifying patients into two groups with 7.6 vs 12.2 months median OS. SMD is a host-derived imaging biomarker extractable without additional tests, supporting its integration into pre-treatment risk stratification and prehabilitation planning for PSMA-RLT candidates.
What the study was
- Study design
- retrospective cohort
- Population
- men with metastatic castration-resistant prostate cancer undergoing PSMA-targeted radioligand therapy
- Sample size
- 410
- Category
- Diagnostics
- Maturity
- Validated
- Journal
- European Journal of Nuclear Medicine and Molecular Imaging
Why it surfaced
Largest PSMA-RLT cohort demonstrates automated AI-derived body composition biomarker (SMD) from routine PET/CT predicts OS with clinically meaningful separation; immediately actionable for treatment decision support and prehabilitation in a growing mCRPC population.
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