放射学++:对息地成像分析进行审查,以解码瘤异质性
Jiaojiao Wu1, Yuwei Xia1, Xuechun Wang1
11Department of Research and Development, United Imaging Intelligence, Shanghai, China; email: feng.shi@uii-ai.com, dinggang.shen@gmail.com.
Annual review of biomedical engineering
|January 27, 2026
概括
基于人工智能 (AI) 的息地成像分析,称为Radiomics++,为了解瘤异质性提供了一种新的方法. 这种方法比传统的放射学更有效地解码内变异,进步了精密医学.
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:
- 瘤异质性是预后和治疗反应的关键因素.
- 放射成像生成参数图,以评估瘤异质性.
- 传统的放射学从各种角度分析瘤异质性.
研究的目的:
- 审查基于人工智能 (AI) 的息地成像分析 (Radiomics++) 来解码瘤内部异质性.
- 引导研究人员了解息地成像分析的工作流程,原则,方法和临床应用.
- 探索验证进步和临床翻译挑战.
主要方法:
- 基于人工智能的息地成像分析 (Radiomics++) 方法的审查.
- 工作流程,原则和临床应用的分析.
- 检查验证策略,将放射性现象类型与生物基础相关联.
主要成果:
- 与传统的放射学相比,Radiomics++显示了解码内异质性的潜力.
- 验证的进步对于验证息地的可靠性至关重要.
- 临床翻译的关键挑战包括数据异质性,模型性能和可解释性.
结论:
- 人工智能定义的息地与多omics集成可以增强对瘤进化的理解.
- 放射学++为推进瘤学精准医学提供了一个有前途的途径.
- 需要进一步的研究来克服临床翻译障碍.
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