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深度学习用于预测激进前列腺切除术中的困难:一种新的评估方案

Haonan Mei¹, Zhongyu Wang², Qingyuan Zheng¹

¹Department of Urology, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, China; Institute of Urologic Disease, Renmin Hospital of Wuhan University, Wuhan, Hubei, 430060, China.

|January 15, 2025

概括

来自MRI的新深度学习指标预测了急性前列腺切除术难度. 前列腺和骨盆之间的空间限制显著影响估计的血液损失和手术时间,改善了手术评估.

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科学领域:

医疗成像医学成像
人工智能的人工智能
手术瘤学手术瘤学

背景情况:

激进前列腺切除术难度评估对于患者的治疗结果至关重要.
目前的方法在预测手术复杂性方面缺乏精度.
术前成像为客观难度指标提供了潜力.

研究的目的:

开发和验证用于评估激进前列腺切除术难度的新型指标,使用两阶段深度学习方法.
为了利用手术前的磁共振成像 (MRI) 进行手术规划和风险分层.

主要方法:

两阶段的深度学习模型 (nnUNet_v2和PointNet) 用于从MRI获得前列腺/骨盆细分和解剖学里程碑定位.
引入了描述前列腺和骨盆之间的空间关系的新型指标.
该模型和指标在两个队列的290名患者身上得到验证 (腹腔镜和机器人辅助的激进前列腺切除术).

主要成果:

深度学习管道实现了准确的细分 (Dice 0.8641) 和毫米级的地标定位.
特定的空间指标 (例如PSD2,PSD2×ρ) 与估计的血液损失和手术时间显著相关.
对外部数据集的验证证实了研究结果的一致性和可靠性.

结论:

拟议的两阶段深度学习方法用于解剖学地标定位,可用于外科评估.
前列腺和骨盆之间的空间限制是急性前列腺切除术难度的关键指标.
这些新型指标为改善手术前手术困难评估和患者管理提供了有希望的途径.