基于放射学的混合模型用于预测辐射肺炎:系统性审查和元分析
Heesoon Sheen1, Wonyoung Cho2, Changhwan Kim3
1Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, South Korea.
基于放射学的混合模型显著改善了对辐射肺炎 (RP) 的预测. 将放射学与临床和剂量计因素相结合,提供了一种可行的方法来识别高风险患者,提高治疗和生活质量.
科学领域:
- 医学成像分析 医学成像分析
- 放射治疗瘤学 放射治疗瘤学
- 人工智能在医学中的应用
背景情况:
- 放射性肺炎 (RP) 是胸部放射治疗的常见并发症.
- 预测RP对于优化放射治疗计划和患者的治疗结果至关重要.
- 这项研究已在PROSPERO注册 (CRD42023426565).
研究的目的:
- 审查和元分析基于放射学混合模型的证据,以预测辐射肺炎 (RP).
- 为了比较RP的不同模型和放射学特征的预测性能.
- 识别提高RP预测准确性的关键放射学特征.
主要方法:
- 在Google Scholar,Embase,PubMed和MEDLINE进行系统的文献搜索,截至2024年4月19日.
- 包括16项符合预定义标准的研究.
- 对RP的放射学特征和预测模型进行比较.
主要成果:
- 仅仅放射学就实现了0.73.7的AUROC.
- 结合放射学与临床和剂量计因素的混合模型显示出更高的性能,AUROC高达0.81.
- 关键的放射学特征,如GLCM和GLSZM被确定为提高预测准确性的关键.
结论:
- 基于放射学的混合模型在预测RP方面非常有效.
- 这些模型提供了一种临床上可行的方法来识别患有高RP风险的患者.
- 改进RP风险识别可以提高临床结果和患者的生活质量.
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