基于深度学习的T2加权MR图像质量评估及其对前列腺癌检测率的影响
Yue Lin1, Mason J Belue1, Enis C Yilmaz1
1Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.
Journal of magnetic resonance imaging : JMRI
|October 9, 2023
概括
高质量的前列腺MRI T2加权图像 (T2WI) 提高PI-RADS 4病变的癌症检测率. 较低质量的T2WIs可能需要结合活检方法来准确诊断前列腺癌.
科学领域:
- 放射学 放射学是一门学科.
- 在瘤学瘤学.
- 人工智能的人工智能
背景情况:
- 前列腺MRI图像质量对于准确的前列腺癌诊断至关重要.
- 将MRI应用到临床实践中,需要对图像质量进行严格的评估.
研究的目的:
- 评估前列腺MRI图像质量对前列腺癌检测的影响.
- 评估内部人工智能 (AI) 算法用于图像质量评估.
主要方法:
- 对615名接受3.0T前列腺MRI的患者进行了回顾性分析.
- 一个人工智能算法将T2加权图像 (T2WI) 分类为高质量或低质量.
- 癌症检测率 (CDR) 用系统和向活检对不同PI-RADS类别的高质量和低质量图像进行了比较.
主要成果:
- 人工智能将63%的T2WIs分类为高质量和37%的低质量.
- 高质量的T2WIs显著改善了针对性活检的PI-RADS 4病变的临床显著癌症检测率 (52%对32%).
- 在高质量和低质量的T2WI之间进行联合活检时,没有观察到PI-RADS 4患者水平CDR的显著差异.
结论:
- 更高质量的T2WI可以提高PI-RADS 4前列腺癌检测的向活检性能.
- 对于PI-RADS 4病变,当T2WI质量低于最佳时,可能需要联合活检.
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