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基于深度学习的前列腺MRI图像质量评估中的偏差

Hirotsugu Nakai¹, Adam T Froemming¹, Akira Kawashima²

¹Department of Radiology, Mayo Clinic, Rochester, USA.

Abdominal radiology (New York)

|August 25, 2025

概括

评估T2加权图像质量的深度学习 (DL) 模型可能会受到临床显著前列腺癌 (csPCa) 的偏差. 在csPCa患者中,基于DL的图像质量往往较差,影响了临床使用.

科学领域:

放射学
人工智能
医学成像

背景情况:

深度学习 (DL) 模型越来越多地用于医学成像中的自动化图像质量 (IQ) 评估.
在前列腺癌诊断中,T2权重图像 (T2WI) 是至关重要的.
基于DL的智商评估中的潜在偏差可能会影响临床解释.

研究的目的:

调查临床上显著的前列腺癌 (csPCa) 是否影响基于DL的T2WI智商评估.
在csPCa的背景下评估DL模型在区分智商退化方面的可靠性.

主要方法:

一项由放射科医生分类的2105个T2WI系列的回顾性研究.
开发一个用于智商分类的DL模型,并评估其与放射专家的协议 (kappa=0.53).
该模型应用于11723次MRI检查,使用病例对照对照比较降低智商组和最佳智商组的csPCa比例.

主要成果:

DL模型与放射科医生表示中等一致.
与最佳智商相比,轻度至中度智商降低与较高比例的csPCa相关.
观察到的具体差异:中度降解 (3.6%高于cspca),轻度降解 (2.7%高于cspca).

结论:

在csPCa患者中,基于DL的T2WI智商评估可能会产生负偏差.
这种偏差引起了关于前列腺MRI智商评估的DL模型的临床实用性和可靠性的担忧.
需要进一步的研究来解决和减轻这些潜在的偏差,以获得准确的临床应用.

关键词:

癌症查深度学习图像质量增强磁共振成像前列腺癌

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