基于变压器的多式融合模型预测了肝脏膜性球菌病患者的淋巴结转移:一项多中心研究
Yinshu Zhou1, Fengyuan Tian2, Mingyang Zhao3
1General surgery department, Qinghai Provincial People's Hospital, Xining, Qinghai, China.
概括
一种新的变压器模型使用CT扫描准确地预测了肝气膜状菌病 (HAE) 中的淋巴结转移. 这个工具有助于指导HAE患者的手术决策.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 肝气泡性乙烯球菌病 (HAE) 带来了诊断挑战,特别是在预测淋巴结 (LN) 转移方面.
- 准确的LN转移预测对于有效的治疗计划和HAE患者的手术干预至关重要.
研究的目的:
- 开发和验证基于CT的新型多式变压器模型,用于精确预测HAE中的LN转移.
- 将变压器模型的性能与传统的放射学和深度学习方法进行比较.
主要方法:
- 使用对比度增强的CT图像开发了一种多式变压器模型,用于3个中心的318名HAE患者的肝脏状LNs.
- 放射学,3D深度学习 (3DDL) 和2D深度学习 (2DDL) 功能被提取和利用.
- 在培训,内部和外部验证集上使用曲线下面面积 (AUC) 和决策曲线分析 (DCA) 评估模型性能.
主要成果:
- 与radiomics和2D深度学习模型相比,3D深度学习模型显示出更高的区分能力.
- 基于变压器的多式融合模型在三个验证集 (0.951,0.927,0.933) 中实现了最高的AUC值.
- 决策曲线分析证实,变压器模型提供了最大的临床净优势.
结论:
- 开发的变压器模型为预测HAE患者的LN转移提供了实用和可靠的工具.
- 这种创新的模型可以指导淋巴结剖析策略在HAE管理.
- 该模型显示了高适用性和在HAE患者护理中临床整合的潜力.
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