用于前列腺癌检测的多模态MRI-超声AI优于放射科医生的MRI判读：一项多中心研究
Multimodal MRI-Ultrasound AI for Prostate Cancer Detection Outperforms Radiologist MRI Interpretation: A Multi-Center Study
AI总结：
本研究提出整合MRI和TRUS的多模态AI框架，在3110名患者数据上验证，其检测CsPCa的灵敏度、特异性和病灶Dice系数均优于单模态模型和放射科医生。
摘要：
活检前磁共振成像（MRI）越来越多地用于靶向可疑前列腺病灶。这促使人工智能（AI）应用提升了基于MRI的临床显著性前列腺癌（CsPCa）检出率。然而，MRI检出的病灶在活检期间仍需映射到经直肠超声（TRUS）图像，这会导致CsPCa的漏诊。本研究系统评估了一个整合MRI和TRUS图像序列的多模态AI框架，以增强CsPCa的识别。该研究包含来自两家机构三个队列的3110名接受前列腺活检的患者。所提出的框架基于3D UNet架构，在1700个测试病例上进行了评估，并与单独使用MRI或TRUS的单模态AI模型进行了性能比较。此外，该模型还在一个110名患者的队列中与放射科医生进行了比较。多模态AI方法取得了更优的灵敏度（80%）和病灶Dice系数（42%），优于单模态MRI（73%，30%）和TRUS模型（49%，27%）。与放射科医生相比，多模态模型显示出更高的特异性（88% vs. 78%）和病灶Dice系数（38% vs. 33%），且灵敏度相当（79%）。我们的研究结果表明，多模态AI有潜力改善活检和治疗计划中的CsPCa病灶靶向，超越了现有的单模态模型和放射科医生；最终改善前列腺癌患者的预后。
英文摘要：
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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