基于MRI/RNA-Seq的放射基因组学和人工智能,以更准确地分期肌肉侵入性膀癌
Touseef Ahmad Qureshi1,2, Xingyu Chen2,3, Yibin Xie1
1Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
International journal of molecular sciences
|January 11, 2024
概括
使用人工智能与MRI和RNA测序相结合,提高了精确的膀癌分期. 这种放射遗传学方法提高了诊断准确性,帮助治疗决策,减少错误分期的错误.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 生物信息学是一种生物信息学.
背景情况:
- 精确的膀癌分期对于选择适当的治疗方法至关重要,但目前的方法导致显著的过度和不足的分期.
- 现有的分期方法缺乏在膀癌管理中最佳临床决策所需的精度.
研究的目的:
- 调查集成磁共振成像 (MRI) 放射学,RNA测序 (RNA-seq) 和人工智能 (AI) 的有效性,以更准确的膀癌分期.
- 开发和验证一个放射基因组学模型,以更好地区分膀内和膀外瘤.
主要方法:
- 采用了40个匹配的MRI和甲固定嵌 (FFPE) 组织样本用于培训 (n=28) 和验证 (n=12).
- 在FFPE样本上进行大量RNA-seq,然后进行生物信息学分析.
- 从膀瘤的MRI扫描中提取并分析了数百个放射性特征.
主要成果:
- 开发的放射基因组学模型在区分膀内癌与膀外癌时,平均灵敏度为94%,特异性为88%,准确度为92%.
- 综合模型显示,与仅基于基因和放射性基因模型相比,该模型有显著的改进,提高了关键性能矩阵的16%至33%.
结论:
- 放射基因组学,结合MRI,RNA-seq和AI,为更准确的膀癌分期提供了一种强大的方法.
- 这种新的模式提供了对歧视性特征的洞察,可以改善临床决策和患者的结果.
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