瘤的自动纹理特征分析框架:基于多相腹部CT的手术,病理和分子评估
Huancheng Yang1,2,3,4, Hanlin Liu5, Jiashan Lin1,4,6
1Luohu Clinical Institute, Shantou University Medical College, Shantou, 515000, China.
European radiology
|August 1, 2023
概括
多相腹部CT扫描的纹理分析准确地预测脏瘤特征,包括恶性瘤和亚型. 这种自动化方法为临床诊断和治疗规划提供了可靠的工具.
科学领域:
- 放射学和医学成像学 医学成像学
- 人工智能在医学中的应用
- 在瘤学瘤学.
背景情况:
- 脏瘤需要准确的特征来进行有效的治疗.
- 多相腹部CT是一个标准的成像模式.
- 对瘤的预测模型可以改善临床决策.
研究的目的:
- 评估多相腹部CT的纹理特征分析对预测各种脏瘤特征的有效性.
- 评估模型在区分良性瘤和恶性瘤方面的表现,预测组织学亚型,病理阶段,腎切除风险,病理等级和Ki67指数.
主要方法:
- 开发使用3D-UNet进行脏和瘤细分的框架.
- 放射学特征为感兴趣的区域进行提取和缩小维度.
- 实施六个XGBoost分类器用于预测任务.
- 使用SHAP进行定量模型解释和特征贡献分析.
- 在内部队列 (850名患者) 和外部测试队列 (201名患者) 上进行验证.
主要成果:
- 该模型展示了强大的预测,在内部验证集中,接收器运行特征曲线 (AUROC) 下面的区域值很高 (范围从0.84到0.91).
- 在外部测试组中也观察到令人印象深刻的性能 (AUROC值从0.79到0.85不等).
- 有助于预测的关键放射学特征包括第一阶统计,与瘤大小相关的形态学和与形状相关的特征.
结论:
- 多相腹部CT的自动纹理特征分析为瘤提供可靠的多任务预测.
- 该框架显示出临床应用的潜力,有助于诊断和治疗规划.
- 该研究的方法,在多个中心和各种数据中验证,可转移到新的数据集和任务.
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