基于U-Net的骨盆骨髓自动细分的可行性研究,用于宫癌的术后放射治疗
Hao Qiu1, Qianjin Shi1, Tianhong Tang1
1Department of Radiation Oncology, Siyang Hospital, Suqian, China.
Frontiers in oncology
|February 12, 2026
概括
在宫癌放射治疗中,RT-Mind软件准确地对临床目标体积和盆骨髓进行细分,大大减少了轮时间,并改善了处于危险的器官节省.
科学领域:
- 辐射瘤学 辐射瘤学
- 医疗成像医学成像
- 人工智能在医学中的应用
背景情况:
- 宫癌的术后放射治疗需要精确划分临床目标体积 (CTV) 和有风险的器官 (OAR).
- 手动轮是耗时的,容易引起观察者之间的变化.
- 精确的骨盆骨髓细分对于评估辐射诱导毒性至关重要.
研究的目的:
- 评估基于卷积神经网络 (CNN) 的自动细分软件RT-Mind的可行性和临床实用性.
- 评估RT-Mind在术后宫癌放射治疗中将CTV和PBM划分为OAR的准确性.
- 为了比较RT-Mind自动细分与手动轮的效率和剂量测量结果.
主要方法:
- 对55名接受术后放射治疗的宫癌患者的回顾性分析.
- 对RT-Mind自分区与放射瘤学家对CTV和OAR (直肠,膀,肠囊,大腿骨头,骨髓) 的手动划分进行比较.
- 使用子相似系数 (DSC),豪斯多夫距离 (HD),雅卡德指数 (JAC),灵敏度指数 (SI) 和时间效率进行评估.
主要成果:
- RT-Mind对骨髓细分 (DSC:0.89 ± 0.05) 和其他OAR如膀和股骨头 (DSC>0.91) 显示出高准确度.
- 自动细分显著减少了轮时间,例如,CTV从~4151s降至~46s (p < 0.001).
- 剂量测量分析显示,可比的CTV覆盖率和骨髓,小肠和直肠的OAR节省显著改善 (p <0.05).
结论:
- 基于U-Net的CNN软件RT-Mind显示了CTV和OAR细分在术后宫癌放射治疗中的高精度和效率.
- 该软件擅长划分盆骨髓,减少轮时间和观察者之间的变化.
- 在改善放射治疗的规划和执行方面,RT-Mind具有有前途的临床应用性.
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