Multimodal artificial intelligence for prostate cancer imaging: workflow-relevant fusion of mpMRI, PSMA PET, ultrasound, and clinical data for diagnosis, local staging, and treatment personalization.

BACKGROUND: Prostate cancer imaging is inherently multimodal, yet many AI tools remain single-modality and therefore misaligned with real-world abdominal/genitourinary radiology decision-making. PURPOSE: We review workflow-relevant multimodal AI methods that fuse mpMRI, PSMA PET, ultrasound (including TRUS and elastography), and clinical or pathology data for diagnosis, local staging, and treatment personalization. CONTENT: MRI-plus-clinical fusion improves csPCa triage beyond imaging-only baselines and supports practical risk-model implementations. MRI-TRUS fusion models demonstrate improved lesion localization for targeted biopsy compared with unimodal AI and standard radiologist MRI interpretation in multicenter settings. For local staging, multimodal strategies for extraprostatic extension prediction are supported by meta-analytic evidence and emerging PET/MRI- or PET/CT-plus-MRI approaches that can assist radiologists and inform nerve-sparing planning. For treatment personalization, multimodal models predict biochemical recurrence after prostatectomy and extend toward systemic endpoints using imaging fused with clinical variables or pathology-derived features. CONCLUSION: The most adoption-ready directions for Abdominal Radiology readers are modular multimodal systems that improve triage, guide biopsy targeting, and quantify local extension risk with transparent validation pathways and human-centered deployment design.