深度学习序列CT 预测免疫疗法治疗的非小细胞肺癌的生存情况
Chiharu Sako1, Brenda F Kurland2, Taly G Schmidt1
1Onc.AI, San Carlos, California.
JAMA network open
|January 28, 2026
概括
一个新的深度学习生物标志物,串行CT响应得分 (串行CTRS),准确地预测了接受免疫检查点抑制剂 (ICI) 治疗的高级非小细胞肺癌患者的整体存活率. 序列CTRS的性能优于RECIST和瘤体积变化,以改善临床决策.
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:
- 对于接受免疫检查点抑制剂 (ICI) 的高级非小细胞肺癌 (NSCLC) 患者,需要可靠的整体生存 (OS) 早期生物标志物.
- 目前像RECIST和瘤体积变化 (TVC) 这样的成像指标对长期结果的预测能力有限.
- 先进的成像生物标志物可以改善NSCLC治疗中的临床决策.
研究的目的:
- 开发和验证一个完全自动化的深度学习成像生物标志物,使用治疗前和12周后续计算机断层扫描 (CT) 扫描.
- 评估生物标志物对接受ICI治疗的晚期NSCLC患者整体存活 (OS) 的预测性表现.
主要方法:
- 一项预后研究利用了回顾性常规临床实践 (RCP) 和临床试验数据 (2013-2023年).
- 一个深度学习模型,串行CT响应得分 (串行CTRS),被开发和验证在多个数据集,包括一个跨国临床试验.
- 考克斯比例危险回归和ROC-AUC分析模拟了串行CTRS和OS之间的关联.
主要成果:
- 这项研究包括了1830名接受ICI治疗的晚期NSCLC患者.
- 串行CTRS在多变量分析中显示出与OS的显著关联,在风险歧视方面表现优于RECIST和TVC.
- 生物标志物的预测值在各个子组中一致,包括PD-L1表达和RECIST标准.
结论:
- 完全自动化的Serial CTRS生物标志物有效预测ICI治疗的晚期NSCLC患者的OS.
- 与RECIST和TVC相比,串行CTRS提供了更高的预后准确性,使用相同的CT扫描.
- 这种先进的成像生物标志物可以提高临床试验设计和高级NSCLC患者管理.
相关概念视频
Cancer Survival Analysis
670
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...
670
Tumor Immunotherapy
1.9K
Immunotherapy is a treatment that boosts or manipulates the immune system to fight diseases, including cancer. For instance, by stimulating an immune response through vaccinations against viruses that cause cancers, like hepatitis B virus and human papillomavirus, these diseases can be prevented. Nonetheless, some cancer cells can avoid the immune system due to their rapid mutation and division. The immune response to many cancers involves three phases: elimination, equilibrium, and escape.
1.9K
Serial Position Effect
539
The serial position effect is a cognitive phenomenon where individuals are more likely to recall the first and last items in a list compared to those in the middle. This effect is divided into the primacy effect and the recency effect. The primacy effect is observed when the initial items in a list are remembered better. This occurs because these items are rehearsed more frequently or receive more elaborative processing, allowing them to be encoded into long-term memory more effectively. For...
539
Predicting Molecular Geometry
45.7K
VSEPR Theory for Determination of Electron Pair Geometries
45.7K
Survival Curves
698
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
698
Survival Tree
418
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
418


