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基础模型基于常规磁共振成像用于脑瘤分子分析和进展预测

Junxian Li¹, Renhe Liu², Yuchen Xing³

¹Department of Blood Transfusion, Key Laboratory of Cancer Prevention and Therapy, Tianjin, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute & Hospital, Tianjin Medical University, Tianjin, China.

JCO precision oncology

|February 20, 2026

概括

一个新的自主监督磁共振成像 (MRI) 基础模型UMBIF有效支持质瘤应用. 它有助于治疗后结果评估和分子标志物预测,改善诊断工作流程.

科学领域:

医疗成像中的人工智能
神经瘤学神经瘤学
机器学习用于医疗保健

背景情况:

基础模型正在成为医学成像中的强大工具.
自主监督学习 (SSL) 减少了对大量手册注释的需求.
质瘤的诊断和治疗监测需要准确的成像分析和分子分析.

研究的目的:

开发一种自主监督的基础模型 (UMBIF),使用常规的临床MRI扫描.
评估UMBIF在治疗后成像治疗结局表征中的表现.
评估UMBIF在预测质瘤患者分子生物标志物的能力.

主要方法:

创建了统一的多模式大脑成像基金会 (UMBIF) 模型.
在51029次常规脑部MRI扫描中使用混合SSL目标的预训练UMBIF.
将预训练的UMBIF编码器调整为下游任务:结果预测和分子标记推理 (IDH,MGMT,1p/19q).

主要成果:

在下游任务中,UMBIF优于其他SSL方法和传统分类器.
实现了0.899准确度 (0.815AUC) 治疗后结果的表征.
证明了分子分析的高精度/AUC:1p/19q配分 (0.898/0.916),IDH突变 (0.829/0.896) 和MGMT甲基化 (0.905/0.859).

结论:

UMBIF显示出强大的可转移性,用于治疗后成像评估和预测质瘤中的分子状态.
自主监督的预训提高了表现,减少了对手册注释的依赖.
UMBIF框架可以简化诊断工作流程,提高效率和可靠性.

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