在良性和恶性肺结节的诊断中,PET/CT放射学和深度学习:进展和挑战
Yan Sun1,2,3, Xinyu Ge1,2,3, Rong Niu1,2,3
1Department of Nuclear Medicine, the Third Affiliated Hospital of Soochow University, Changzhou, China.
Frontiers in oncology
|November 25, 2024
概括
早期诊断肺癌至关重要. 将18F-FDG PET/CT成像与放射学和深度学习相结合,提高了识别肺结节 (PNs) 的准确性,提高了患者的生存率.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 肺癌仍然是全球癌症死亡的主要原因.
- 通过查进行早期检测对于减少肺癌死亡率至关重要.
- 肺结节 (PNs) 往往是肺癌的早期症状,但区分良性和恶性PNs是具有挑战性的.
研究的目的:
- 审查使用18F-FDG PET/CT用于肺结节诊断的放射学和深度学习的最新进展.
- 讨论这些综合方法在提高PNS诊断准确度方面的潜力.
- 为了确定PNS的非侵入性诊断当前的挑战和未来的方向.
主要方法:
- 从18F-FDG PET/CT图像中提取高通量定量放射学特征.
- 应用深度学习算法来分析PN分类的放射学特征.
- 综合放射学和深度学习方法的诊断性能评估研究的审查.
主要成果:
- 对18F-FDG PET/CT的放射学分析提供了超越视觉评估的定量数据.
- 集成放射学特征的深度学习模型显示出非侵入性PN诊断的巨大潜力.
- 这些先进的方法旨在提高PN特征的特异性和准确性.
结论:
- 18F-FDG PET/CT,放射学和深度学习的结合为诊断肺结节提供了一个有希望的非侵入性策略.
- 需要进一步的研究来克服挑战,并充分认识到这些技术的临床实用性.
- 改善PN的诊断准确度可以导致更好的患者管理和生存结果.
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