定量成像 (放射学) 和人工智能在精密瘤学中的新兴作用
Ashish Kumar Jha1,2,3, Sneha Mithun1,2,3, Umeshkumar B Sherkhane1,2
1Department of Radiation Oncology (Maastro), GROW School for Oncology, Maastricht University Medical Centre+, 6200 Maastricht, The Netherlands.
Exploration of targeted anti-tumor therapy
|September 18, 2023
概括
放射性特征对于精确瘤学至关重要,但存在不稳定性,阻碍了模型的概括. 确定这些成像生物标志物的稳定性对于可靠的癌症管理和人工智能应用至关重要.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 癌症是全球主要的死亡原因,需要先进的管理策略.
- 精密瘤学使用生物标志物来分组患者,超越传统的,适合所有人的治疗方法.
- 医学成像是整个癌症护理连续性的组成部分,从诊断到随访.
研究的目的:
- 突显成像生物标志物和人工智能 (AI) 在瘤学中的日益重要.
- 解决放射性特征不稳定性及其对预测模型的影响这一关键问题.
- 强调需要强大的放射性特征来可靠地概括人工智能驱动的瘤学工具.
主要方法:
- 关于成像生物标志物,放射学和瘤学人工智能的现有文献的审查.
- 讨论多机构环境中放射性特征不稳定所带来的挑战.
- 确定旨在标准化和稳定放射性特征的倡议.
主要成果:
- 放射学使各种成像生物标志物能够用于精确瘤学.
- 放射性特征的不稳定性是全球应用预测模型的重要障碍.
- 现有的QIN,QIBA和IBSI等倡议正在努力实现特征稳定.
结论:
- 基于放射性成像生物标志物的进步对于精确瘤学至关重要.
- 确保放射性特征的强度和稳定性是癌症治疗中可靠的人工智能实施的优先事项.
- 标准化工作对于放射性模型在不同机构的成功通用化至关重要.
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