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KMeansGraphMIL模型:一个弱监督的多实例学习模型,用于预测结肠直肠癌瘤突变负担

Linghao Chen¹, Huiling Xiao¹, Jiale Jiang²

¹Radiology Department, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China.

The American journal of pathology

|January 12, 2025

概括

一个新的模型,KMeansGraphMIL,快速且负担得起地预测结直肠癌 (CRC) 患者的瘤突变负担 (TMB). 这有助于更快地做出免疫治疗的治疗决定,节省时间和成本.

科学领域:

在瘤学瘤学.
计算生物学计算生物学
生物医学成像技术生物医学成像技术

背景情况:

结肠直肠癌 (CRC) 是全球癌症相关死亡的主要原因.
免疫疗法检查点阻塞治疗对CRC有希望,但需要特定的生物标志物测量.
瘤突变负担 (TMB) 是一个关键的生物标志物,但通过下一代测序进行传统测量是昂贵且耗时的.

研究的目的:

开发一种经济和快速的方法来预测结直肠癌患者的TMB.
在TMB预测中引入KMeansGraphMIL模型用于弱监督的多实例学习.

主要方法:

提出了KMeansGraphMIL模型,利用弱监督的多实例学习.
整合了图像补丁特征向量相似性和空间关系.
评估模型性能与以前监督较弱的多实例学习方法相比.

主要成果:

在接收器运行特征曲线下的面积达到0.8334.4.
显著改善了回忆力,达到0.7556.
与现有的弱监督的多实例学习模型相比,表现出优越的性能.

结论:

KMeansGraphMIL框架为预测CRCTMB提供了一种经济和快速的方法.
这种方法有可能加速医生的治疗计划.
这项研究在为结肠直肠癌管理提供了成本效益和及时的TMB评估方面取得了重大进展.

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