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基于损伤区域和明显扩散系数量化值的临床显著前列腺癌的预后预测模型的开发
Rani Ashouri1, Penny S Reynolds2, Shay Rajavel3
1University of Florida, UF Health, Department of Urology. Gainesville, FL.
Urology
|January 30, 2026
概括
整合前列腺癌成像特征的新模型改善了临床显著前列腺癌 (csPCa) 的预测. 这种工具有助于对PI-RADS 3-5分数的男性进行活检决策,但并不排除活检.
科学领域:
- 放射学和成像科学 放射学和成像科学
- 在瘤学瘤学.
- 生物统计学 生物统计学
背景情况:
- 对临床显著的前列腺癌 (csPCa) 的准确预测对于活检决定至关重要.
- 使用PI-RADS评分的多参数磁共振成像 (mpMRI) 有助于检测前列腺癌.
- 通过结合定量成像特征和病变特征,可以提高PI-RADS v2.1的预测价值.
研究的目的:
- 在PI-RADS 3-5分数的男性中开发cspca的预测模型.
- 评估前列腺损伤区域对预测准确性的影响.
- 确定csPCa的表面扩散系数 (ADC) 值,并创建一个包含区域和ADC的后勤回归模型.
主要方法:
- 从429名接受前列腺mpMRI的男性中对546个病变进行了回顾性分析.
- 经过奖学金培训的生殖泌尿器放射科医生和病理学家对损伤水平进行分析.
- 使用临床数据,病变区域和量化ADC值开发后勤回归模型.
主要成果:
- 前列腺区域 (PZ与TZ) 是 csPCa (OR 2.58,p<0.0005) 的显著预测因素.
- 包括区域和前列腺特异性抗原密度在内的组合模型在ADC截止值为947μm2/s时达到0.78的AUC.
- 模拟场景显示概率在16%至82%之间,通常高于活检遗漏值.
结论:
- 前列腺区域和量化的ADC值独立地提高了csPCa的可预测性.
- 开发的模型显示出有希望的结果,但在模拟的MRI可见病变的情况下没有产生活检-遗漏值.
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