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使用深度神经网络自动预测肺瘤的侵袭性

Xiuyuan Xu¹, Nan Chen², Zongxuan Jin¹

¹Department of Computer Science, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu, 610065, Sichuan, China.

Medical engineering & physics

|August 20, 2025

概括

此摘要是机器生成的。

精确的肺瘤侵袭性预测对于早期肺癌治疗至关重要. 一个新的人工智能系统LTI-Net使用CT扫描有效地分类肺瘤, 克服数据挑战并提高诊断准确性.

关键词:

智能系统肺部瘤的侵入性肺腺癌

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科学领域:

医学成像
人工智能
癌症学

背景情况:

早期发现肺癌的侵袭性对于患者的结果至关重要.
目前用于肺瘤侵入性检测的临床方法具有挑战性和侵入性.
有限的公共数据集和阶级不平衡阻碍了自动化LTI预测算法的开发.

研究的目的:

使用计算机断层扫描 (CT) 数据开发一种新的人工智能系统,用于非侵入性肺瘤侵入性预测.
在自动化LTI检测中解决有限的数据可用性和类不平衡的挑战.
提高肺瘤侵袭性分类的诊断性能.

主要方法:

收集并整理了804名患者的大型高质量的计算机断层扫描数据集,
开发了肺瘤侵入性预测神经网络 (LTI-Net),利用3D残余神经网络骨干从CT值分析瘤内异质性.
引入了一种新型的替代函数来近似曲线下的面积 (AUC) 度量,通过配对样本训练来增强特征歧视和稳定优化.

主要成果:

在收集的数据集上,LTI-Net系统显示了对肺瘤侵袭性进行分类的显著潜力.
与现有最先进的方法相比,LTI-Net在真正阳性和真负率 (HMoPN) 的和平均值中取得了显著的改善.
在各种不平衡的数据设置中,拟议的方法显示HMoPN得分增加了2.92%,这突显了它的稳定性.

结论:

开发的LTI-Net提供了使用CT成像进行肺瘤侵袭性预测的有效非侵入性方法.
该系统成功地解决了在该领域普遍存在的数据限制和阶级不平衡问题.
LTI-Net为改善早期肺癌诊断和治疗规划提供了有希望的进展.