标题： 多模态MRI-超声AI在前列腺癌检测中优于放射科医生MRI解读：一项多中心研究 显示英文标题

标题： Multimodal MRI-Ultrasound AI for Prostate Cancer Detection Outperforms Radiologist MRI Interpretation: A Multi-Center Study

摘要： 经前列腺活检前磁共振成像（MRI）正越来越多地用于靶向可疑的前列腺病变。 这导致了人工智能（AI）应用于基于MRI检测临床显著前列腺癌（CsPCa）的改进。 然而，在活检过程中，MRI检测到的病灶仍必须映射到经直肠超声（TRUS）图像上，这会导致CsPCa漏诊。 本研究系统评估了一种多模态AI框架，该框架整合了MRI和TRUS图像序列，以增强CsPCa的识别。 研究包括来自两家机构三个队列的3110名患者，这些患者接受了前列腺活检。 所提出的框架基于3D UNet架构，在1700个测试病例中进行了评估，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，所提出的模型在110名患者的队列中与放射科医生进行了比较。 与单模态MRI（73%，30%）和TRUS模型（49%，27%）相比，多模态AI方法在敏感性（80%）和病灶Dice系数（42%）方面表现更优。 与放射科医生相比，多模态模型显示出更高的特异性（88%比78%）和病灶Dice系数（38%比33%），而敏感性相当（79%）。 我们的研究结果表明，多模态AI在提高活检和治疗计划期间CsPCa病灶靶向方面的潜力，超过了当前的单模态模型和放射科医生；最终改善了前列腺癌患者的预后。 显示英文摘要

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.