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A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

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现代的综合性前列腺癌诊断.

Rainer Grobholz^1,2, Felice Burn^3,4, Lukas Prause⁵

¹Medical Faculty, University of Zurich, Zurich.

Current opinion in urology

|December 11, 2025

概括

人工智能 (AI) 增强了放射学和病理学中的前列腺癌诊断,提高了准确性和效率. 多式人工智能生物标志物正在出现,用于个性化治疗选择,尽管验证和监测对于广泛采用至关重要.

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科学领域:

在瘤学瘤学.
放射学放射学是一门学科.
病理学病理学病理学
医疗成像医学成像
人工智能的人工智能

背景情况:

前列腺癌的诊断严重依赖于放射学和病理学评估.
人工智能 (AI) 正在越来越多地被探索以增强这些诊断过程.
在当前的诊断工作流中存在标准化和效率方面的挑战.

研究的目的:

通过放射学和病理学审查目前在前列腺癌诊断中的人工智能应用.
评估这些人工智能工具的性能和实施障碍.
突出用于预后和治疗选择的新型多式人工智能生物标志物.

主要方法:

对前列腺癌放射和病理诊断中人工智能的当代研究的综述.
分析AI性能指标,包括准确性,可重复性和工作流效率.
评估新兴的多式联络人工智能生物标志物开发和验证.

主要成果:

放射学 (基于MRI) 的AI在检测显著的前列腺癌方面表现出与专家放射科医生相比或超过的准确性.
病理学中的AI与癌症检测和格里森分级的病理学家具有很高的一致性,有助于标准化.
多式人工智能将基因病理与临床/分子数据整合在一起,对预后和预测生物标志物显示出有前途,目前正在进行验证.

结论:

人工智能作为一种强大的辅助技术,提高了MRI和前列腺癌病理学的诊断准确性,可重现性和效率.
综合多式联运数据正在推动验证的生物标志物用于风险分层和个性化治疗决策.
广泛采用人工智能需要严格的外部验证,质量保证和部署后监控.

关键词:

人工智能的人工智能是人工智能.人工智能生物标志物的人工智能生物标志物数字病理学数字病理学磁共振成像技术的使用前列腺癌是前列腺癌.

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