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文献速递神经MR

基于CE-T1w与黑血序列的AI靶区定义在脑转移瘤立体定向放疗中的应用

AI-driven target definition using CE-T1w and black blood sequence imaging in stereotactic radiosurgery for brain metastases.
PubMed 文献 · Eur J Radiol · 2026-04-26

本研究为单中心回顾性研究，纳入206例接受立体定向放疗的脑转移瘤患者，基于CE-T1w和黑血CE-T1w MRI开发nnUNet深度学习模型，用于脑转移瘤的自动检测和靶区勾画。AI模型总体F1为0.93（专家0.96），敏感度0.89（专家0.97），阳性预测值0.98（专家0.95）；对≥0.1cc病灶检测性能与专家相近，但小病灶（<0.1cc）敏感度低于专家（0.83 vs 0.95）。AI模型识别出3.0%被专家遗漏的病灶，勾画Dice系数平均0.83，平均Hausdorff距离1.4mm。

要点

  • AI对≥0.1cc脑转移瘤检测性能与专家相近，小病灶敏感度仍待提高
  • AI可检出3.0%专家遗漏病灶，可作为辅助读片工具
  • 勾画Dice系数随病灶增大而提高（小0.76、中0.90、大0.94）

局限性

  • 单中心回顾性研究
  • 小转移瘤（<0.1cc）检测敏感度低于专家

以上解读依据论文摘要（全文未开放获取或许可不允许翻译转载）。

英文原文（摘要）· 点击展开

OBJECTIVES: Development and evaluation of a deep learning-based method for automatic detection and target delineation of brain metastases on contrast-enhanced T1-weighted (CE-T1w) and black blood (BB) MR scans for optimization of stereotactic radiosurgery planning.

MATERIALS &amp; METHODS: In this retrospective, single-center study, CE-T1w and BB CE-T1w scans with manual delineations from 206 consecutive stereotactic radiosurgery (SRS) patients were used to develop an nnUNet-based model. 162 patients were randomly allocated to the training set and 44 to the testing set. Detection was assessed using F1 score, sensitivity, and positive predictive value (PPV) across small (S; <0.1 cc), medium (M; ≥0.1 and < 1cc), and large (L; ≥1cc) lesions. Delineation was evaluated using Dice coefficient and Hausdorff distance. Multi-expert review by radiation oncologists and neuroradiologists served as reference.

RESULTS: The AI model achieved an F1 score of 0.93 vs 0.96 by the experts. Sensitivity and PPV were 0.89 (AI) vs 0.97 (experts) and 0.98 (AI) vs 0.95 (experts). AI and clinicians showed similar sensitivities for M and L lesions (0.97-0.98 vs 1.0), while for small lesions, experts outperformed AI (0.95 vs 0.83). Overall, the experts missed 3.0% of the lesions. Dice coefficients were 0.76 (S), 0.90 (M), and 0.94 (L), averaging 0.83. The average Hausdorff distance was 1.4 mm.

CONCLUSION: Our deep learning-based method shows promise for automated detection and delineation of brain metastases ≥ 0.1 cc on MRI in patients undergoing SRS. The AI model identified 3.0% of lesions missed by experts. Improving model performance, especially for small metastases, is key for clinical adoption.

来源：Eur J Radiol，查看原文。版权归原作者所有。

引用

  1. Snoeijink JAN, Slotman DJ, Kamerbeek AM, et al. AI-driven target definition using CE-T1w and black blood sequence imaging in stereotactic radiosurgery for brain metastases. Eur J Radiol. 2026. PMID: 42066626. doi:10.1016/j.ejrad.2026.112891
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