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自动导管数字化在乳腺支臂疗法中

Sébastien Quetin^1,2,3, Hossein Jafarzadeh^1,2,3, Jonathan Kalinowski^1,3

¹Medical Physics Unit, Department of Oncology, McGill University, Montreal, Canada.

Medical physics

|September 12, 2025

概括

此摘要是机器生成的。

这项研究引入了一条自动化管道,用于数字化乳腺高剂量率 (HDR) 胸腔治疗中的导管,显著减少手工劳动. 这种由人工智能驱动的系统准确地数字化了导管,并计算了剂量,提高了癌症治疗的效率和精度.

关键词:

自动导管数字化自动导管数字化乳腺支臂疗法是什么深度学习是一种深度学习.

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科学领域:

医学物理医学物理
放射治疗瘤学放射治疗瘤学
人工智能在医学中的应用

背景情况:

在高剂量速率 (HDR) 胸膜治疗中手动导管数字化是耗时且复杂的,特别是在乳腺癌中,因为它的解剖结构不规则.
临床经验严重影响手动导管数字化的准确性,导致潜在的变化.

研究的目的:

开发和验证第一个自动化管道,用于导管数字化,专门用于乳腺HDR胸膜治疗.
为乳腺癌患者引入一条全面的管道,自动化导管数字化,居住位置生成和剂量计算.

主要方法:

利用了117名接受HDR支臂治疗的乳腺癌患者的治疗数据.
训练了一条nnU-Net管道,从CT图像中对导管的伪轮进行细分,然后进行自动数字化和脊柱配套.
开发了处理碰撞的导管和识别导管尖端的算法,然后使用人工智能模型生成停留位置和预测吸收剂量.

主要成果:

自动管道数字化了408个导管,其平均轴距离为0.70 ± 3.91毫米,尖端距离为1.37 ± 5.25毫米.
手动和自动化计划之间的剂量测量误差在关键规划目标量和风险指数的器官中低于3%.
管道执行时间平均为118秒,并成功标记了所有需要手动审查的案例.

结论:

这条管道代表了乳腺支架疗法导管数字化的第一个自动化,包括停留位置生成和基于AI的剂量预测.
自动化数字化显示出与手工方法的良好一致性,并提供了更准确的导管解剖学表示.
该系统提供了一种更快,更一致,并可能更准确的方法来规划乳腺HDR支臂疗法.