深度学习算法用于预测接受新辅助化疗或放射治疗的直肠癌患者的MRI病理完整反应:系统性审查
Bor-Kang Jong1,2, Zhen-Hao Yu1,2, Yu-Jen Hsu1,2
1Colorectal Section, Department of Surgery, Chang Gung Memorial Hospital, Taoyuan, Taiwan.
International journal of colorectal disease
|January 20, 2025
概括
深度学习人工智能模型在使用MRI的新辅助化疗放射治疗 (nCRT) 后预测直肠癌患者的病理完整反应 (pCR) 方面表现有前途. 结合T2W和DWIMRI序列可以提高准确性,更大的数据集可以产生更好的结果.
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:
- 结直肠癌的治疗通常涉及新辅助化疗或放射治疗 (nCRT).
- 预测病理完整反应 (pCR) 对于治疗规划至关重要.
- 当前的预测方法有其局限性.
研究的目的:
- 系统地审查深度学习算法的实用性,用于预测直肠癌患者的pCR.
- 评估基于MRI的人工智能 (AI) 模型的性能.
- 确定影响这些AI模型诊断准确性的因素.
主要方法:
- 按照PRISMA指南进行系统审查.
- 在PubMed,Embase和Cochrane图书馆搜索预测PCR的AI和MRI研究.
- 包括对MRI应用的深度学习模型;排除非MRI或非AI研究.
- 关于研究特征,MRI序列,AI模型和性能指标的数据提取.
- 使用PROBAST工具进行质量评估.
主要成果:
- 26项研究符合512个初始记录中的纳入标准.
- 人工智能模型显示出有希望的诊断性能,在外部验证中,AUC往往超过0.8.
- T2W + DWI MRI 序列比单独的 T2W 提高了准确性.
- 较大的数据集通常与更好的模型性能相关.
- 模型的异质性,MRI协议和有限的临床数据整合是挑战.
结论:
- 人工智能增强的MRI显示了在直肠癌中预测pCR的巨大潜力.
- T2W + DWI 序列和更大的数据集是改善预测的关键.
- 标准化方法和扩展数据集对于临床实用性至关重要.
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