在固体癌症中,从奥米克到临床漫画的旅程:成功故事和挑战
Sanjana Mehrotra1, Sankalp Sharma1, Rajeev Kumar Pandey2
1Guru Nanak Dev University, Amritsar, Punjab, India.
Advances in protein chemistry and structural biology
|March 6, 2024
概括
癌症研究整合了多学科数据和人工智能 (AI) 以实现个性化医疗. 临床漫画和放射漫画弥合了发现和临床应用之间的差距,改善了对包括乳腺癌在内的固体瘤的患者护理.
科学领域:
- 在瘤学瘤学.
- 翻译生物信息学
- 医疗成像医学成像
背景情况:
- 癌症是全球主要的死亡原因,需要进行先进的研究以获得有效的治疗方法.
- 高通量奥米克技术和成像已经改善了瘤分子理解,但临床翻译仍然是一个挑战.
- 弥合基础研究和临床应用之间的差距需要分析复杂的,高维数据集.
研究的目的:
- 要总结在固体癌症中的多omics研究,重点关注乳腺癌.
- 审查用于癌症诊断,亚型,预后和治疗反应预测的机器学习和AI算法.
- 突出临床和放射学在推进个性化癌症护理方面的作用.
主要方法:
- 整合多omics数据与临床信息 (临床漫画).
- 使用深度学习和AI (放射学) 来提取定量成像特征.
- 对癌症数据集应用的机器学习和人工智能算法的审查.
主要成果:
- 多组学,临床学和放射学为个性化癌症治疗提供了一条途径.
- 人工智能和机器学习在改善癌症诊断,亚型和预后方面表现有前途.
- 这些综合方法可以潜在地预测治疗耐药性和复发.
结论:
- 多组学,翻译生物信息学,放射学和临床学的融合对于基于证据的个性化癌症治疗至关重要.
- 在瘤学中,人工智能驱动的多模式数据分析是加速从临床到病床过渡的关键.
- 进一步的研究和这些综合方法的应用将提高患者在固体癌症的治疗结果.
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