多发性骨髓瘤生存预测的放射学特征分析:自动化PET/CT方法
Javier Guinea-Pérez1, Alessandro Ceresi1, Anaida Fernández García1
1Universidad Politécnica de Madrid, Avenida Complutense, 30, Madrid, 28040, Spain.
Computer methods and programs in biomedicine
|August 27, 2025
概括
来自FDG PET/CT扫描的放射特征可以预测多发性骨髓瘤 (MM) 的进展. 脊椎区域的纹理特征对风险分层提供了最多信息,改善了患者的治疗结果.
科学领域:
- 放射学
- 医学成像
- 癌症学
背景情况:
- FDG PET/CT成像是评估多发性骨髓瘤 (MM) 的新兴工具.
- 精确的风险分层对于优化MM患者管理至关重要.
研究的目的:
- 开发和评估用于MM风险分层的自动化放射学管道.
- 确定最有信息的成像区域和特征,以预测MM患者的无进展生存率.
主要方法:
- 使用了227名MM患者的全身FDG PET/CT图像.
- 骨架结构 (脊柱) 的自动细分和放射性特征提取.
- 使用放射性特征训练和交叉验证各种生存模型 (DeepSurv,随机生存森林).
主要成果:
- 最好的模型 (DeepSurv) 使用来自脊椎和周围区域的PET图像纹理特征.
- 平均C指数为0.657,表明预后良好.
- 图像异质性,特别是纹理特征,与疾病进展风险的增加密切相关.
结论:
- 自动化放射学分析,特别是使用脊柱PET纹理特征,有效地分层了MM风险.
- DeepSurv和随机生存森林模型的表现优于传统的基于Cox的模型.
- 这项研究提供了MM中可复制放射学分析的开源管道.
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