Lang Yankun, Rodrigues Dario B, Ren Lei
Department of Radiation Oncology Physics, University of Maryland, Baltimore, MD, USA.
Int J Hyperthermia. 2025 Dec;42(1):2554860. doi: 10.1080/02656736.2025.2554860. Epub 2025 Sep 9.
To develop a deep learning method for fast and accurate prediction of Specific Absorption Rate (SAR) distributions in the human head to support real-time hyperthermia treatment planning (HTP) of brain cancer patients.
We propose an encoder-decoder neural network with cross-attention blocks to predict SAR maps from brain electrical properties, tumor 3D isocenter coordinates and microwave antenna phase settings. A dataset of 201 simulations was generated using finite-element modeling by varying tissue properties, tumor positions, and antenna phases within a human head model equipped with a three-ring phased-array applicator. The model was trained and evaluated on this dataset using standard error metrics and structural similarity analysis.
On a held-out test set of 20 samples, the model achieved a mean root-mean-squared error (RMSE) of 3.3 W/kg and a mean absolute error (MAE) of 1.6 W/kg across the whole brain. In target regions, RMSE and MAE were 4.8 and 2.5 W/kg, respectively. The structural similarity index (SSIM) reached a mean value of 0.90, and the computation time was reduced from 10 min (simulation-based) to 4 s using our deep learning approach. The proposed method enables accurate, efficient SAR prediction for HTP in the brain, potentially supporting real-time HTP to optimize tumor temperature and improve clinical outcomes.
This work introduces a novel deep learning-based approach that significantly accelerates SAR calculation in HTP, enabling adaptive therapy strategies to improve treatment outcomes in hyperthermia.
开发一种深度学习方法，用于快速准确地预测人体头部的比吸收率（SAR）分布，以支持脑癌患者的实时热疗治疗计划（HTP）。
我们提出了一种带有交叉注意力模块的编码器-解码器神经网络，用于根据脑部电特性、肿瘤三维等中心坐标和微波天线相位设置来预测SAR图。通过在配备三环相控阵施加器的人体头部模型中改变组织特性、肿瘤位置和天线相位，使用有限元建模生成了一个包含201次模拟的数据集。使用标准误差指标和结构相似性分析在该数据集上对模型进行训练和评估。
在一个包含20个样本的预留测试集上，该模型在全脑范围内实现了平均均方根误差（RMSE）为3.3 W/kg，平均绝对误差（MAE）为1.6 W/kg。在目标区域，RMSE和MAE分别为4.8 W/kg和2.5 W/kg。结构相似性指数（SSIM）达到了0.90的平均值，并且使用我们的深度学习方法，计算时间从10分钟（基于模拟）减少到了4秒。所提出的方法能够为脑部HTP进行准确、高效的SAR预测，有可能支持实时HTP以优化肿瘤温度并改善临床结果。
这项工作引入了一种基于深度学习的新方法，该方法显著加速了HTP中的SAR计算，使自适应治疗策略能够改善热疗的治疗效果。
