深度学习预测了南非种族异质人口的结直肠癌中的微卫星不稳定状态
Alessandro Pietro Aldera1,2,3, Didem Cifci4, Gregory Patrick Veldhuizen5
1Division of Anatomical Pathology, University of Cape Town, Observatory, South Africa alessandro.aldera@uct.ac.za.
Journal of clinical pathology
|May 20, 2025
概括
这项研究表明,深度学习模型可以有效地选南非患者的结直肠癌 (CRC) 中的不匹配修复缺陷 (dMMR). 特定区域的校准提高了准确性,减少了在资源有限的环境中进行广泛的下游测试的需要.
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:
- 深度学习 (DL) 模型显示出在结直肠癌 (CRC) 中检测不匹配修复缺陷 (dMMR) 的前景.
- 现有的DL模型主要训练在北半球队伍上,缺乏非洲人口的代表性.
- 本研究评估了DL模型在南非多样化CRC队列中的表现.
研究的目的:
- 评估基于变压器的深度学习模型在结直肠癌中进行dMMR查的有效性.
- 为了确定模型在种族多样化的南非患者队列中的表现.
- 评估DL模型的潜力,以减少在资源有限的环境中下游测试的需求.
主要方法:
- 用基于变压器的DL模型分析了197个CRC切除标本的队列.
- 整个幻灯片图像被拼接并通过DL模型进行处理.
- 模型性能是用接收器操作特征曲线 (AUROC) 下的面积,灵敏度和特异性来量化,最佳值是由Youden指数决定的.
主要成果:
- 该DL模型实现了0.91 (±0.05) 的AUROC.
- 最佳的0.620值产生了85.7%的灵敏度和82.4%的特异性.
- 将值调整为0.470使灵敏度最大化为95.6%,同时保持69.6%的特异性,可能将超过50%的病例排除在进一步测试之外.
结论:
- 深度学习模型可以准确地在结肠直肠癌中预先选dMMR.
- 区域特定的校准对于优化DL模型在不同人群中的性能至关重要.
- 这种方法可以在资源有限的环境中显著减少下游免疫组织化学和分子测试的工作量.
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