PURE-MRI:一项国际研究评估医生在前列腺MRI上划分前列腺和尿道的准确性
Lily Nguyen1, Yuze Song2, Anna Dornisch3
1Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA; University of California San Diego School of Medicine, La Jolla, CA, USA.
概括
医生在MRI上准确地对前列腺进行细分,但在尿道划分方面遇到了困难. 人工智能 (AI) 工具与医生的前列腺精度相匹配,并在尿道细分方面表现出色,改善前列腺癌护理.
科学领域:
- 生殖尿道瘤学
- 医学成像医学成像
- 医疗保健中的人工智能
背景情况:
- 在MRI上精确细分生殖尿路结构对于有效的前列腺癌 (PCa) 治疗至关重要.
- 为了尽量减少毒性和损伤,在PCa护理过程中需要精确地划分前列腺和尿道.
- 在MRI前列腺和尿道 (PURE-MRI) 研究解决了标准化的准确性评估的需要.
研究的目的:
- 评估医生在MRI上对前列腺和尿道进行细分的准确性.
- 将医生细分精度与参考共识细分和人工智能工具进行比较.
- 为了确定影响PCa管理中的轮性能的因素.
主要方法:
- 国际前性研究涉及来自11个国家的62名专家.
- 医生在T2加权的MRI病例中对前列腺和尿道进行了轮.
- 使用空间和体积分析对专家共识细分和验证的自动细分人工智能工具进行比较.
主要成果:
- 医生在前列腺细分方面获得了0.92的Dice中位数,与AI (0.95) 相比.
- 与人工智能 (人工智能分数0.41,覆盖率81%) 相比,医生对尿道的细分显示出更低的准确性 (迪斯得分0.33) 和覆盖率 (36%).
- 医生学员在轮准确度方面表现类似于经验丰富的临床医生.
结论:
- 医生在MRI上显示前列腺细分的高精度,但误差可能超过5-10毫米.
- 医生在尿道细分方面的表现低于最佳,覆盖范围明显低于AI工具.
- 人工智能工具为医生提供可比的前列腺细分精度和优越的尿道细分,突出了它们在PCa护理中的潜力.
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