通过群体惩罚模型改善癌症预后:在肺腺癌和脑膜瘤中进行放射性特征选择的比较研究
Yonghan Kwon1, Young Joo Suh2, Yae Won Park1
1Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
Quantitative imaging in medicine and surgery
|February 11, 2026
概括
与传统的拉索相比,群体惩罚模型改善了癌症预后的放射性特征选择稳定性. 这些模型通过选择特征组,而不仅仅是单个特征,提供了更好的解释性.
科学领域:
- 医学成像分析分析 医学成像分析
- 无线电学 (Radiomics) 是一种辐射学.
- 癌症预后 癌症预后
背景情况:
- 放射学从医学图像中提取了用于癌症预后的定量特征.
- 高维放射性数据在特征选择和模型稳定性方面存在挑战.
- 传统的拉索回归可以导致相关特征之间的不稳定特征选择.
研究的目的:
- 为了比较群体惩罚模型与Lasso对癌症预后放射性特征选择的性能.
- 评估不同集体惩罚方法的稳定性和预测准确性.
主要方法:
- 分析了590例肺腺癌和194例脑膜瘤病例.
- 与Lasso.一起应用各种团体惩罚模型 (Lasso组,MCP组,SCAD组,graper组)
- 根据细分区域和数学属性分组的特征.
- 使用AUC和雅卡德指数进行10倍交叉验证和独立测试集验证,以评估性能和稳定性.
主要成果:
- 与Lasso.com相比,SCAD组的肺腺癌交叉验证AUC (0.804) 和雅卡德指数 (0.613) 较高.
- 与拉索相比,拉索组在脑膜瘤预测方面获得了更高的交叉验证AUC (0.816) 和雅卡德指数 (0.7).
- 测试集验证显示,对于两个数据集,组模型和拉索模型之间的预测性能是可比的.
结论:
- 与拉索相比,群体惩罚模型在放射学中提供了优越的特征选择稳定性.
- 这些模型通过选择生物学上有意义的特征组来提高可解释性.
- 群体惩罚模型为基于放射学的稳定和可解释的癌症预后提供了强大的框架.
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