在前列腺癌中,当代术前检测前列腺外延伸.
Jan Stępka1,2,3, Tomasz Milecki3, Jędrzej Ksepka4
1Doctoral School, Poznań University of Medical Sciences, 60-812 Poznań, Poland.
Cancers
|February 13, 2026
概括
在前列腺癌中预测前列腺外延伸 (EPE) 是至关重要的. 使用放射学和深度学习的先进人工智能模型显示出有希望的结果,潜在地超过了改善手术规划的传统方法.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 放射学 放射学是一门学科.
- 在瘤学瘤学.
- 人工智能在医学中的应用
背景情况:
- 前列腺外延伸 (EPE) 是前列腺癌的关键预后因素,影响神经节约性激进前列腺切除等手术决策.
- 多参数MRI (mpMRI) 是局部分期的标准,但在EPE检测灵敏度和解释可变性方面存在局限性.
- 准确的手术前EPE检测对于优化治疗策略和患者结果至关重要.
研究的目的:
- 为前列腺癌中术前前前列腺外延伸检测现有策略提供全面的概述.
- 评估各种EPE预测方法的诊断性能,验证和临床适用性.
- 确定新兴技术和改善EPE分期的未来方向.
主要方法:
- 从PubMed,Embase,Web of Science和谷歌学者 (2015-2025) 来进行文学叙事审查.
- 包括评估临床参数,mpMRI特征,名图,放射学,机器学习 (ML) 和深度学习 (DL) 进行EPE预测的研究.
- 对检查方法的诊断性能,验证和临床实用性的比较分析.
主要成果:
- 临床参数和名ograms提供适度的EPE检测准确度.
- mpMRI增强了分期,瘤囊接触长度是显著的成像标记.
- 放射学,ML和DL模型的AUC达到0.75-0.85,有时超过专家放射科医生的性能,尽管DL模型往往缺乏外部验证.
- 侧面特定和分级的EPE评估在临床上比二元分类更相关.
结论:
- 放射学和人工智能驱动的模型显示了改善前手术EPE检测前列腺癌的巨大潜力.
- 对于这些先进工具的临床翻译,需要外部验证和多模式方法.
- 未来的研究应该专注于为常规临床使用开发强大,外部验证和可解释的AI框架.
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