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通过基于MRI的深度学习模型,预测多个线性接器在双接技术中发射的数次线性接.

Zhanwei Fu1, Shuchun Li1, Lu Zang1

  • 1Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No. 197 Ruijin Er Road, Shanghai, 200025, People's Republic of China.

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多个线性接器发射增加了腹腔镜前下切除过程中解剖器泄漏的风险. 瘤大小和CEA水平是关键的危险因素. 集成成像和临床数据的深度学习模型准确地预测高风险患者.

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科学领域:

  • 手术瘤学手术瘤学
  • 医疗成像医学成像
  • 机器学习在医学中的应用

背景情况:

  • 多次线性接器发射是使用双接技术 (DST) 进行腹腔镜低前切除 (LAR) 后解肠漏液 (AL) 的已知危险因素.
  • 预测需要三次或更多的接器发射对于优化手术策略和降低AL风险至关重要.

研究的目的:

  • 为了确定与三种或更多的线性接器发射相关的风险因素,在腹腔镜LAR与DST解剖.
  • 开发和验证预测模型,包括使用MRI的深度学习方法,用于识别需要多个接器发射的患者.

主要方法:

  • 对328名患者进行了腹腔镜LAR与DST解剖,分为训练 (n=260) 和测试 (n=68) 组的回顾性分析.
  • 开发使用后勤回归的临床预测模型和使用MRI数据上的3D卷积网络的基于图像的模型.
  • 创建一个综合模型,结合临床变量和MRI数据;所有模型都在128名患者的独立队列上得到了验证.

主要成果:

  • 17.7%的患者需要三次或更多次的接器发烧.
  • 确定的独立风险因素是瘤大小≥5厘米 (OR=2.54) 和手术前CEA水平>5 ng/mL (OR=2.20).
  • 与临床和仅图像模型相比,综合模型显示出优异的预测性能 (AUC=0.88,准确性=94.1%在训练中;AUC=0.84,准确性=93.8%在验证中).

结论:

  • 瘤大小和手术前CEA水平是需要在腹腔镜LAR期间进行多次接器发射的重要预测因素.
  • 结合盆腔MRI和临床数据的深度学习模型有效地预测了患有多重接器发烧高风险的患者.
  • 这种预测模型可以帮助在手术前为中低直肠癌患者选择最佳的静脉动技术.