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视下成像信号作为即将发生的肺转移的生物标志物:一个多中心泛癌研究

Ranze Zhang¹, Haojiang Li², Lei Ding³

¹Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China; Guangdong Provincial Clinical Research Center for Breast Diseases, Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.

European journal of cancer (Oxford, England : 1990)

|October 23, 2025

概括

胸部CT扫描中的潜视信号可以在可见之前多年预测肺转移. 乳腺癌,结肠直肠癌和食道癌的早期检测允许及时干预.

科学领域:

在瘤学瘤学.
放射学放射学是一门学科.
人工智能的人工智能

背景情况:

早期检测远程转移对于癌症患者的治疗结果至关重要.
目前的成像技术只有在放射能可见时才能检测到转移,从而限制了早期干预.
胸部CT扫描中的潜视信号有可能成为肺转移的早期生物标志物.

研究的目的:

调查视下胸部CT信号是否可以作为即将发生的肺转移的早期生物标志物.
开发和评估用于检测这些潜视信号的机器学习模型.
为了评估信号检测和放射性转移之间的领先时间.

主要方法:

一项涉及乳腺,结肠直肠和食道癌症患者的多中心研究,多次随访胸部CT扫描.
从3D感兴趣的肺部区域 (ROI) 提取信号特征.
应用多时间点建模方法与机器学习来分析时间特征和预测转移风险.

主要成果:

对2,148名患者的10,280张CT扫描分析显示,信号阳性患者的转移风险显著增加.
转移检测的领先时间从0.83到1.41年不等.
模型性能受到CT扫描数量和间隔的影响,并建议最佳应用.

结论:

视下胸部CT信号是预测各种癌症类型即将发生的肺转移的有效生物标志物.
这种非侵入性方法可以动态识别高风险患者.
早期发现有助于潜在的干预,改善患者管理.

关键词:

早期检测早期检测肺部转移肺部转移肺部转移机器学习机器学习在视觉下,胸部CT信号.

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