相关实验视频

Updated: Jul 29, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

新辅助化疗疗效的AI驱动的预测模型:乳腺癌组织图像的全面分析

Fengling Li^1,2, Yani Wei^2,3, Wenchuan Zhang^1,2

¹Department of Pathology, West China Hospital, Sichuan University, Chengdu, China.

NPJ precision oncology

|July 15, 2025

概括

一个人工智能模型准确地预测了乳腺癌患者的新辅助疗法 (NAT) 反应. 该模型整合了他的病理学,临床和免疫特征,为个性化治疗策略提供了一个强大的工具.

科学领域:

在瘤学瘤学.
人工智能的人工智能
生物标志物发现发现

背景情况:

乳腺癌患者对新辅助疗法 (NAT) 的反应不同.
预测生物标志物对于优化NAT疗效至关重要.
目前的预测模型缺乏全面整合多样化的患者数据.

研究的目的:

开发和验证人工智能驱动的综合预测模型 (IPM) 来预测乳腺癌中NAT反应.
从基因病理全幻灯片图像中识别最佳的深度学习生物标志物.
评估免疫分析对提高预测准确性的附加值.

主要方法:

从1035名乳腺癌患者的整片图像中开发深度学习生物标志物 (TE-score,TS-score,TR-score).
使用曲线下的面积 (AUC) 进行生物标志物性能比较.
将最佳生物标志物 (TR-score) 与临床变量和免疫特征整合到IPM中.

主要成果:

整个瘤生物标志物 (TR-score) 显示出优异的预测性能 (AUC=0.729).
与临床病理学模型 (AUC=0.706) 相比,结合TR-score和临床数据的IPM实现了更高的预测准确性 (验证AUC=0.780).
通过纳入免疫分析 (AUC=0.831) 观察到预测的进一步提升,突出了其增量值.

结论:

TR-score有效地描述了瘤-肌瘤相互作用,验证了其生物学和临床意义.
IPM为精密瘤学提供了可通用的框架,改善了NAT响应预测.
该模型的多中心稳定性和免疫数据的实用性支持其用于指导治疗决策和临床试验分层的应用.

更多相关视频

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model

Using Computer-based Image Analysis to Improve Quantification of Lung Metastasis in the 4T1 Breast Cancer Model

Published on: October 2, 2020

相关概念视频

Cancer Survival Analysis

Cancer Survival Analysis

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...