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研究领域生物医学和临床科学瘤学和致癌症预测和预后标志物
使用CT纹理分析与机器学习相结合,在食管胃结腺癌中进行淋巴结转移的术前预测

使用CT纹理分析与机器学习相结合,在食管胃结腺癌中进行淋巴结转移的术前预测

Dawei Wang ¹, Min Wang ², Rong Chen ², Jianxia Song ², Yaying Su ³, Yuchen Wang ¹, Feng Liu ⁴, Xiaolong Zhu ⁵, Fei Yang ⁵

Dawei Wang ¹, Min Wang ², Rong Chen ²

¹Department of Thoracic Surgery, The First Affiliated Hospital of Hebei North University, Zhangjiakou, China.
²Department of Medicine, Hebei North University, Zhangjiakou, China.
³Department of Nuclear Medicine, The First Affiliated Hospital of Hebei North University, Zhangjiakou, China.
⁴Department of Radiology, Tianjin Dongli Hospital, Tianjin, China.
⁵Department of Medical Imaging, The First Affiliated Hospital of Hebei North University, Zhangjiakou, China.

⁰Department of Thoracic Surgery, The First Affiliated Hospital of Hebei North University, Zhangjiakou, China.

Medicine +

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2025年九月3日

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在PubMed上查看摘要

概括

此摘要是机器生成的。

这项研究开发了一种非侵入性计算机断层扫描 (CT) 纹理分析和机器学习模型,用于预测食管胃结腺癌 (AEG) 的淋巴结转移. 该模型在AEG患者的手术前预测中显示出强大的疗效.

科学领域

医学成像
癌症学
人工智能

背景情况

在治疗计划中,精确的食管胃结腺癌预手术阶段确定至关重要.
淋巴结转移是AEG的关键预后因素,但其非侵入性预测仍然具有挑战性.

研究的目的

开发和验证AEG中淋巴结转移的非侵入性术前预测模型.
使用计算机断层扫描 (CT) 纹理特征和机器学习算法进行预测.

主要方法

对57名病理确诊的AEG患者的手术前CT增强扫描进行分析.
使用机器学习算法从动脉和静脉相 CT 图像中提取和选纹理特征 (随机森林,后勤回归,决策树,支向量机器).
使用接收器操作特征曲线和各种预测指标进行模型性能评估.

主要成果

机器学习模型在动脉和静脉阶段有效预测淋巴结转移.
具体模型在不同的指标上表现出色,例如,在动脉阶段的逻辑回归显示出高特异性 (1.0),而决策树在各阶段的表现一致.
在模型之间没有发现统计学上显著的AUC差异,表明可比的预测性能.

结论

基于CT纹理的机器学习模型为预测AEG的淋巴结转移提供了强大的非侵入性方法.
这种方法可以帮助进行术前分期和优化AEG患者的治疗策略.

关键词:

胃食道结合腺癌淋巴结转移机器学习质地分析

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