一个基于机器学习的框架,用于预后预测和瘤微环境特征局部先进的宫癌与并发的化学放射治疗
Yue Feng1,2, Zijian Sun1,2,3, Yuqiang Li2,4
1Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
NPJ precision oncology
|December 12, 2025
概括
通过新的深度学习模型 (DeepMR-LACC) 和放射性蛋白质学,改善了局部晚期宫癌 (LACC) 的准确预后. 这种方法有助于个性化治疗,并揭示瘤微环境的洞察力.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 人工智能的人工智能
背景情况:
- 对于局部晚期宫癌 (LACC) 后并发化疗放射治疗 (CCRT) 的准确预后预测对于个性化治疗至关重要.
- 目前对LACC的预后因素需要加强,以改善患者分层.
研究的目的:
- 开发一个多任务预后模型 (DeepMR-LACC),使用深度学习对LACC的治疗前MRI扫描进行深度学习.
- 调查放射性-表型关联和放射性蛋白质学,以改善LACC患者的风险分层.
主要方法:
- 开发了一个深度学习模型 (DeepMR-LACC),使用T2加权的MRI来预测无进展生存 (PFS) 和整体生存 (OS).
- 在宫活检中进行了蛋白质组学分析,以表征瘤微环境,并使基于放射蛋白质组学的风险分层成为可能.
- 该模型的性能在培训,内部测试和外部测试队列中进行了评估,并与临床风险因素进行了比较.
主要成果:
- DeepMR-LACC模型表现出强大的预后性能,PFS和OS的C指数在整个队列中从0.65到0.87不等.
- 该模型有效地将LACC患者分为高风险和低风险组,优于现有的临床因素.
- 蛋白质组分析显示,高风险组中的瘤微环境具有免疫抑制作用,基于放射性蛋白质组的分层显示出更高的预后准确性 (PFS和OS的C指数为0.85).
结论:
- 深度MR-LACC模型为用CCRT治疗的LACC患者提供了准确的预后预测.
- 基于放射性蛋白质组学的风险分层提供了优越的预后性能,与单独的深度学习模型相比.
- 这种综合方法增强了瘤微环境的特征,支持LACC的个性化长期管理策略.
更多相关视频
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
8.6K
07:15Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
7.3K
相关概念视频
02:17The Tumor Microenvironment
7.6K
Every normal cell or tissue is embedded in a complex local environment called stroma, consisting of different cell types, a basal membrane, and blood vessels. As normal cells mutate and develop into cancer cells, their local environment also changes to allow cancer progression. The tumor microenvironment (TME) consists of a complex cellular matrix of stromal cells and the developing tumor. The cross-talk between cancer cells and surrounding stromal cells is critical to disrupt normal tissue...
7.6K
01:21Cancer Survival Analysis
629
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
629