Background/Objectives: GK plan quality is strongly affected by lesion size and shape, and the same evaluation metrics may not be directly comparable across patients with different anatomies. This study proposes a deep learning-based method to predict achievable, clinically acceptable plan quality from patient-specific geometry. Methods: A hierarchically densely connected U-Net (HD-U-Net) was trained at the lesion level to predict 3D dose distributions for the estimation of plan quality metrics, including coverage, selectivity, gradient index (GI), and conformity index at a 50% prescription dose (CI50). To improve the prediction accuracy of plan quality metrics, Dice similarity coefficient losses for the 100% and 50% isodose lines were incorporated with conventional mean squared error (MSE) loss. Results: Ten-fold cross-validation on 463 brain metastases (BMs) from 175 patients showed that our method achieved smaller mean absolute errors across all four metrics than the HD-U-Net baseline trained with MSE loss. Improvements were pronounced in all metrics for small metastases, and were observed primarily in GI and CI50 for medium and large lesions. Paired Wilcoxon signed-rank tests confirmed the statistical significance of these improvements (p< 0.05). Conclusions: The proposed method outperformed the baseline model in capturing overall trends, improving per-lesion accuracy, and enhancing robustness to dataset variability. It can serve as a pre-planning tool to guide planners in constraint setting and priority tuning, a post-planning quality control tool to identify subpar plans that could be substantially improved, and as a foundation for developing deep reinforcement learning-based automated planning of GK treatments for brain metastases.
背景/目的：伽玛刀（GK）计划质量受病灶大小和形状的显著影响，相同的评估指标可能无法直接适用于不同解剖结构的患者。本研究提出一种基于深度学习的方法，旨在根据患者特异性几何结构预测可实现的、临床可接受的计划质量。方法：在病灶层面训练一个分层密集连接U-Net（HD-U-Net），用于预测三维剂量分布，进而评估计划质量指标，包括覆盖率、选择性、梯度指数（GI）以及50%处方剂量下的适形指数（CI50）。为提高计划质量指标的预测精度，在传统均方误差（MSE）损失函数的基础上，引入了针对100%和50%等剂量线的Dice相似系数损失。结果：对来自175名患者的463例脑转移瘤（BMs）进行的十倍交叉验证显示，与仅使用MSE损失训练的HD-U-Net基线模型相比，本方法在所有四个指标上均实现了更小的平均绝对误差。对于小转移瘤，所有指标的改进均较为显著；对于中、大型病灶，改进主要体现在GI和CI50指标上。配对Wilcoxon符号秩检验证实了这些改进具有统计学显著性（p<0.05）。结论：所提出的方法在捕捉总体趋势、提高单病灶预测精度以及增强对数据集变异性的鲁棒性方面均优于基线模型。该方法可作为预计划工具，指导计划者在约束设置和优先级调整方面进行优化；作为后计划质量控制工具，识别可显著改进的次优计划；并可作为开发基于深度强化学习的脑转移瘤伽玛刀治疗自动计划系统的基础。
肿瘤(癌症)患者之家