用于轴向t2加权前列腺MRI质量评估的深度学习:减少不必要重新扫描的工具。

IF 3.7 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Jacob N Gloe, Eric A Borisch, Adam T Froemming, Akira Kawashima, Jordan D LeGout, Hirotsugu Nakai, Naoki Takahashi, Stephen J Riederer
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引用次数: 0

摘要

背景:t2加权图像是前列腺磁共振成像(MRI)的重要组成部分,在没有放射科医生监督的情况下,可以根据患者的具体情况自动评估图像质量(IQ)。方法:这项回顾性研究包括1412位轴位t2加权前列腺扫描。四名经验丰富的放射科医生用0到3的量表对智商进行评分(0 =无法解释;1 =边际可解释;2 =充分诊断;3 =诊断性较好),二值化为需要重新扫描的非诊断性(IQ0或IQ1)和诊断性(IQ2或IQ3),不需要重新扫描。深度学习(DL)模型进行了1006次扫描训练;203个其他扫描用于验证多个卷积神经网络;剩下的203次考试作为一个测试集。3D-DenseNet_169是基于多个评价标准从11个模型中选择的。将重新扫描的预测结果与174次考试的重新扫描次数进行比较。结果:该模型准确预测了放射科医生的智商得分(Cohen κ = 0.658),与人类评分间信度(κ = 0.688-0.791)相似。该模型还预测重新扫描的必要性,类似于放射科医生:模型κ = 0.537;Reviewer κ = 0.577-0.703。曲线下重新扫描模型预测面积为0.867。结论:DL模型具有较强的区分诊断性和非诊断性轴位t2加权前列腺图像的能力,能够准确地模拟放射科专家的智商分数。使用该模型,可将临床不必要的重扫描率从50%以上降低到30%以下。相关性声明:t2加权前列腺MRI扫描的DL评估可以准确评估IQ,确定是否需要重复不充分的扫描,以及避免对诊断质量足够的重复扫描,从而减少不必要的重新扫描。重点:人工智能评估前列腺MRI t2加权图像质量可以改善检查时间管理。该模型在评估前列腺MRI t2加权图像质量方面准确率超过75%。专家放射科医师在评估前列腺MRI t2加权图像质量方面有实质性的一致意见。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep learning for quality assessment of axial T2-weighted prostate MRI: a tool to reduce unnecessary rescanning.

Background: T2-weighted images are a critical component of prostate magnetic resonance imaging (MRI), and it would be useful to automatically assess image quality (IQ) on a patient-specific basis without radiologist oversight.

Methods: This retrospective study comprised 1,412 axial T2-weighted prostate scans. Four experienced uroradiologists graded IQ using a 0-to-3 scale (0 = uninterpretable; 1 = marginally interpretable; 2 = adequately diagnostic; 3 = more than adequately diagnostic), binarized into nondiagnostic (IQ0 or IQ1), requiring rescanning, and diagnostic (IQ2 or IQ3), not requiring rescanning. The deep learning (DL) model was trained on 1,006 scans; 203 other scans were used for validation of multiple convolutional neural networks; the remaining 203 exams were used as a test set. 3D-DenseNet_169 was chosen among 11 models based on multiple evaluation criteria. The rescan predictions were compared to the number of rescans performed on a subset of 174 exams.

Results: The model accurately predicts radiologist IQ scores (Cohen κ = 0.658), similar to the human inter-rater reliability (κ = 0.688-0.791). The model also predicts rescanning necessity similarly to radiologists: model κ = 0.537; reviewer κ = 0.577-0.703. The rescan model prediction area under the curve was 0.867.

Conclusion: The DL model showed a strong ability to differentiate diagnostic from nondiagnostic axial T2-weighted prostate images, accurately mimicking expert radiologists' IQ scores. Using the model, the clinical unnecessary rescan rate could be reduced from over 50% to less than 30%.

Relevance statement: DL assessment of T2-weighted prostate MRI scans can accurately assess IQ, determining the need to repeat inadequate scans as well as avoiding repeat scans of those with adequate diagnostic quality, resulting in reduced unnecessary rescanning.

Key points: Artificial intelligence assessment of prostate MRI T2-weighted image quality can improve exam time management. The model showed over 75% accuracy in assessing prostate MRI T2-weighted image quality. Expert radiologists have a substantial agreement in evaluating prostate MRI T2-weighted image quality.

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来源期刊
European Radiology Experimental
European Radiology Experimental Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
6.70
自引率
2.60%
发文量
56
审稿时长
18 weeks
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