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深度学习的诺姆图用于预测局部晚期胃癌患者的新辅助化疗反应

Jingjing Zhang¹, Qiang Zhang², Bo Zhao³

¹Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, People's Republic of China.

Abdominal radiology (New York)

|May 26, 2024

概括

一种深度学习的诺基图准确地预测了使用CT扫描的局部晚期胃癌 (LAGC) 患者的新辅助化疗 (NAC) 反应. 这个工具有助于个性化治疗胃癌的治疗决策.

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:

局部晚期胃癌 (LAGC) 需要有效的新辅助化疗 (NAC) 来改善治疗结果.
预测NAC反应对于定制治疗策略和改善患者预后至关重要.

研究的目的:

开发和验证一个深度学习的放射学诺米克图,用于预测LAGC患者的NAC反应.
将多相对比增强型计算机断层扫描 (CECT) 成像功能与临床数据相结合,以提高预测准确度.

主要方法:

一项多中心的回顾性研究包括322名LAGC患者.
手工制作的放射学和EfficientNet V2深度学习模型应用于CET图像.
使用手工制作的功能,深度学习功能和临床数据构建了一个名ogram.

主要成果:

该名图表表现出了优异的区分能力,训练集的ROC曲线下的面积 (AUC) 为0.848.
该模型与临床模型和手工制作的放射学模型相比显示出更高的性能.
通过决策曲线分析,名图表表现出良好的校准和临床实用性.

结论:

一个经过验证的深度学习放射学名图可以准确地预测LAGC患者的NAC反应.
使用CECT图像和临床数据的诺莫图,提供了个性化的治疗见解.
这种方法支持针对LAGC患者进行手术切除的量身定制的治疗策略.

关键词:

与对比度增强的计算机断层扫描.深度学习是一种深度学习.局部发达的胃癌局部发达的胃癌新辅助化学疗法新辅助化学疗法这个名字叫做Nomogram.

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