Multimodal MRI-Ultrasound AI for Prostate Cancer Detection Outperforms Radiologist MRI Interpretation: A Multi-Center Study
Journal:
arXiv
Published Date:
Jan 31, 2025
Abstract
Pre-biopsy magnetic resonance imaging (MRI) is increasingly used to target
suspicious prostate lesions. This has led to artificial intelligence (AI)
applications improving MRI-based detection of clinically significant prostate
cancer (CsPCa). However, MRI-detected lesions must still be mapped to
transrectal ultrasound (TRUS) images during biopsy, which results in missing
CsPCa. This study systematically evaluates a multimodal AI framework
integrating MRI and TRUS image sequences to enhance CsPCa identification. The
study included 3110 patients from three cohorts across two institutions who
underwent prostate biopsy. The proposed framework, based on the 3D UNet
architecture, was evaluated on 1700 test cases, comparing performance to
unimodal AI models that use either MRI or TRUS alone. Additionally, the
proposed model was compared to radiologists in a cohort of 110 patients. The
multimodal AI approach achieved superior sensitivity (80%) and Lesion Dice
(42%) compared to unimodal MRI (73%, 30%) and TRUS models (49%, 27%). Compared
to radiologists, the multimodal model showed higher specificity (88% vs. 78%)
and Lesion Dice (38% vs. 33%), with equivalent sensitivity (79%). Our findings
demonstrate the potential of multimodal AI to improve CsPCa lesion targeting
during biopsy and treatment planning, surpassing current unimodal models and
radiologists; ultimately improving outcomes for prostate cancer patients.
