多模态AI融合MRI与超声图像，提升前列腺癌检出率。
Multimodal MRI-Ultrasound AI for Prostate Cancer Detection Outperforms Radiologist MRI Interpretation: A Multi-Center Study
- 用3D UNet融合MRI与超声序列，实现多模态联合分析。
- 敏感性达80%，病灶分割Dice为42%，优于单一模态模型。
- 性能超越放射科医生，适合临床辅助诊断与治疗规划。
术前磁共振成像（MRI）在靶向可疑前列腺病灶中日益重要，推动了基于AI的显著性前列腺癌（CsPCa）检测发展。然而，MRI病灶仍需映射到经直肠超声（TRUS）图像进行活检，易导致漏诊。本研究系统评估了一种融合MRI与TRUS图像序列的多模态AI框架，以提升CsPCa识别能力。研究纳入来自两家机构三个队列的3110名患者，测试集包含1700例病例，对比了基于3D UNet架构的多模态模型与仅使用MRI或仅使用TRUS的单模态模型。此外，在110名患者的队列中，多模态模型与放射科医生进行比较。结果显示，多模态模型敏感性达80%，病灶分割Dice为42%，显著优于单模态MRI（73%，30%）和单模态TRUS模型（49%，27%）。相比放射科医生，多模态模型具有更高特异性（88% vs. 78%）和病灶分割Dice（38% vs. 33%），且敏感性相当（79%）。结果表明，多模态AI在活检靶向与治疗规划中具备超越现有单模态模型和放射科医生的潜力，有助于改善前列腺癌患者预后。
原文摘要 · Abstract (English)
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.
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