一个自动化的深度学习管道用于EMVI分类和使用基线MRI预测直肠癌的反应:一个多中心研究
Lishan Cai1,2, Doenja M J Lambregts1,2, Geerard L Beets2,3
1Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
NPJ precision oncology
|January 22, 2024
概括
一个新的自动化深度学习管道准确地分类外壁血管侵袭 (EMVI),并预测使用MRI的直肠癌患者的完整反应 (CR). 这种方法表现出强烈的概括性,优于现有模型,可以更好地规划治疗.
科学领域:
- 放射学 放射学是一门学科.
- 在瘤学瘤学.
- 人工智能的人工智能
背景情况:
- 外围血管侵袭 (EMVI) 是直肠癌的关键预后标志物,预测完整反应 (CR) 有助于治疗决策.
- 目前用于EMVI分类和CR预测的放射学方法通常需要手工细分和手工制作的特征,这限制了它们的通用性.
研究的目的:
- 开发和验证一个完全自动化的深度学习管道,用于EMVI状态分类和CR预测,使用初级分期MRI.
- 与现有方法相比,提高EMVI分类和CR预测的准确性和通用性.
主要方法:
- 分析了来自9个中心的509名直肠癌患者的回顾性队列.
- 开发了使用nnUNet进行瘤细分的全自动化管道,以及采用扩散权重成像 (DWI) 和T2权重成像 (T2WI) 的多层次图像特征的新型深度学习模型 (MLNet).
- MLNet接受了EMVI分类和CR预测的培训,在内部和外部验证集上评估了性能.
主要成果:
- 在EMVI分类和CR预测的内部和外部验证数据集上,MLNet取得了强的表现.
- 在外部验证方面,MLNet在两个任务中都超过了3D ResNet10模型.
- 对于CR预测,MLNet超越了当前最先进的模型,该模型在同一外部队列中使用了成像和临床特征.
结论:
- 拟议的自动化深度学习管道,结合多层次图像表示,显著提高EMVI分类和CR预测准确性和概括性.
- 这种方法具有临床应用的潜力,特别是提高直肠癌分期的EMVI分类.
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