基于深度学习的MRI模型用于预测P53突变肝细胞癌.
Lulu Jia1, Qing Yang2,3, Hanchen Jiang4
1The First Clinical Medical College of Lanzhou University, Lanzhou City, Gansu Province, China.
BMC medical imaging
|December 22, 2025
概括
使用MRI序列的深度学习模型有效预测P53突变肝细胞癌 (HCC). 综合模型整合了动脉阶段 (AP),门静脉阶段 (VP) 和T2加权成像 (T2WI),在识别这种侵袭性癌症变体方面显示出高准确性.
科学领域:
- 放射学和医学成像学 医学成像学
- 人工智能在医学中的应用
- 在瘤学瘤学.
背景情况:
- 发生P53突变的肝细胞癌 (HCC) 具有攻击性,与血管内皮生长因子 (VEGF) 和微血管密度增加有关.
- 预测HCC中P53突变状态对于向治疗和改善患者结果至关重要.
研究的目的:
- 开发和评估一个深度学习模型,利用MRI数据进行P53突变HCC的非侵入性预测.
- 为了比较单个MRI序列与组合序列在预测P53突变状态中的性能.
主要方法:
- 对312名病理确诊的HCC患者进行了回顾性分析,这些患者接受了加多增强的MRI.
- 基于EfficientNetV2的深度学习模型的开发,使用动脉相 (AP),门静脉相 (VP),T2加权成像 (T2WI) 和肝胆相 (HBP) 序列.
- 使用AUC,精度,灵敏度,特异性,精度和F1评分评估模型性能,与AUC比较的Delong测试进行比较.
主要成果:
- 综合多相模型 (T2WI+AP+VP) 的表现明显优于单序模型,在测试数据集中达到0.914的AUC.
- 肝胆相 (HBP) 模型在单序模型中表现最好 (AUC=0.715).
- 将HBP纳入组合模型并没有显著改善预测性能 (P>0.05).
结论:
- 集成T2WI,AP和VPMRI序列的深度学习模型对于预测P53突变的HCC非常有效.
- 这种由人工智能驱动的方法提供了一个有前途的非侵入性工具,用于识别攻击性的HCC变体.
- 进一步的研究可能会探索额外的序列或特征,以潜在地提高预测准确性.
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