基于自动细分的US和MRI的多模式放射学分析,用于预测乳腺癌的无疾病生存率:一个多中心研究
Lang Xiong1, Xiaofeng Tang2, Xinhua Jiang3
1Department of Medical Imaging, First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi Province, 341000, China.
Breast cancer research : BCR
|November 13, 2024
概括
使用超声波和核磁共振的放射学,对乳腺癌的自动细分显示出预测无疾病生存率 (DFS) 的前景. 结合多模式放射学与MRI特征,改善了风险分层,并有助于临床决策.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 在瘤学瘤学.
背景情况:
- 放射学显示了乳腺癌预后预测的潜力.
- 手动瘤细分是劳动密集型的,容易变化.
- 自动细分提供了克服这些局限性的解决方案.
研究的目的:
- 评估基于自动细分的多模式放射学,用于预测侵袭性乳腺癌的无病存活率 (DFS).
- 评估结合超声波 (US) 和磁共振成像 (MRI) 功能的价值.
主要方法:
- 对643名侵袭性乳腺癌患者进行了回顾性多中心研究.
- 基于深度学习的自动瘤细分框架的开发.
- 使用最小绝对缩小和选择运算符考克斯回归构建放射学签名和分数.
- 开发和评估六个DFS预测模型.
主要成果:
- 多模式放射学签名实现了更高的C指数 (内部:0.734,外部:0.708) 对于DFS预测.
- 综合多模式临床成像模型在外部组中产生了最高的C指数 (0.795).
- 模型成功地将患者分为低风险和高风险组,并改进了校准和临床效益.
结论:
- 基于自动细分的多模式放射学是乳腺癌风险分层的一个有希望的生物标志物.
- 结合放射学,临床特征和MRI特征,可以提高个性化的DFS预测.
- 这种方法可以帮助指导乳腺癌治疗决策.
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