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前列腺癌风险分层和扫描定制使用深度学习缩短前列腺MRI

Patricia M Johnson^1,2, Tarun Dutt¹, Luke A Ginocchio¹

¹Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA.

Journal of magnetic resonance imaging : JMRI

|April 22, 2025

概括

一个深度学习模型有效地使用双参数MRI (bpMRI) 识别出临床显著的前列腺癌. 这种人工智能工具有助于选择最佳的MRI协议,从而有可能改善癌症检测中的资源使用.

科学领域:

放射学放射学是一门学科.
人工智能的人工智能
在瘤学瘤学.

背景情况:

磁共振成像 (MRI) 对于前列腺癌 (PCa) 管理至关重要.
双参数MRI (bpMRI) 为多参数MRI (mpMRI) 提供了一个更快,无对比的替代方案.
根据个体风险量身定制的MRI协议可以优化资源利用.

研究的目的:

开发和评估一种深度学习 (DL) 模型,用于使用bpmri对临床显著PCa (csPCa) 的分类.
评估DL模型在优化MRI协议选择方面的潜力,只在有益时推额外的序列.

主要方法:

一个DL模型在26,129个前列腺MRI研究中进行了训练和验证.
在回顾性 (n=151) 和前性 (n=142) 队列上进行评估,并进行基准真实验证.
使用3D ResNet-50架构进行基于PI-RADS和格里森分数的分类.

主要成果:

DL模型在前性队列中 (PI-RADS ≥3) 达到0.83的AUC,在后性队列中 (Gleason ≥7) 达到0.86.
在两个队列中都表现出高灵敏度 (93%).
实时实现显示协议建议的处理延迟为14-16秒.

结论:

开发的DL模型使用bpMRI准确地识别了csPCa.
该模型可以集成到临床工作流程中,以改善PCa的检测和管理.
优化MRI协议选择和资源分配的潜力.

关键词:

双参数核磁共振扫描 (MRI) 是一种双参数的方法.深度学习是一种深度学习.前列腺癌是前列腺癌.

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