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临床意义上的ISUP升级在多参数MRI时代:活检瘤负担优于复杂的机器学习模型在单一中心的探索队列
Cristian Condoiu1, Adelina Baloi1,2, Dorel Sandesc2
1Doctoral School, Victor Babes University of Medicine and Pharmacy Timisoara, E. Murgu Square, No. 2, 300041 Timisoara, Romania.
Cancers
|March 14, 2026
概括
在多参数MRI (mpMRI) 导向活检后,前列腺癌 (PCa) 的临床显著升级是常见的. 活检瘤负担,特别是正核比率,是关键预测因素,超过了复杂的机器学习模型.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
背景情况:
- 多参数MRI (mpMRI) 导向活检用于前列腺癌 (PCa) 诊断.
- 在ISUP等级组 (GG) 的临床显著升级 (CSU) 从活检到急性前列腺切除术 (RP) 经常发生.
- CSU的预测因素需要识别,以改善手术前风险分层.
研究的目的:
- 在PCa患者中确定CSU的手术前预测因子 (活检GG ≤2到RP GG ≥3).
- 为了预测CSU,将一个节的物流模型与机器学习 (ML) 分类器进行比较.
- 用歧视,校准和决策曲线分析来评估预测模型的性能.
主要方法:
- 对96名PCa患者的单中心探索性分析,这些患者接受了mpMRI,活检和RP.
- 预测建模专注于活检GG 1-2 (n=64) 的患者.
- 针对参考模型的LASSO引导的特征选择和Firth惩罚的后勤回归,与ML分类器进行基准测试.
主要成果:
- 在符合条件的患者中,CSU发生在15.6% (10/64).
- 阳性核心比率是CSU的主导独立预测指标 (调整后OR为1.54每10%的增加).
- 一个两变量后勤模型 (正核心比率,PSA密度) 在区分和校准方面超过了ML分类器 (AUC ≈ 0.75-0.79).
结论:
- 活检瘤负担,特别是正核比率,是mpMRI告知PCa诊断中CSU的主要驱动因素.
- 一个简单的物流模型,结合了活检瘤负担指标,显示了手术前风险分层的前景.
- 外部验证是必要的,以确认这些发现的稳定性和通用性.
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