发表机构
Corewell Health William Beaumont University Hospital（Corewell Health William Beaumont大学医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对肺调强质子治疗剂量预测问题，提出DoseBridge去噪扩散桥模型，融合CT与射野几何信息，在52例患者数据上的多项指标优于对比模型，为放疗剂量预测提供新方法。
AI 中文摘要
大多数放射治疗剂量预测模型仅使用CT图像和解剖结构，然而调强质子治疗（IMPT）的剂量还强烈依赖于射野几何结构，且可用临床数据集通常较小。本文提出DoseBridge，一种以患者CT作为结构化桥端点的去噪扩散桥模型，通过空间对齐的射野掩码编码计划特异性射野几何结构。多尺度融合模块将CT、靶区、危及器官和射野掩码的表示进行融合，仅增加1.95%的参数。对DoseBridge进行回顾性评估，数据集为52例接受30次分割共60 Gy治疗的晚期肺癌患者的单中心CT图像和治疗计划，其中42例用于训练，10例用于测试。采用图像相似度、剂量体积以及Lyman-Kutcher-Burman正常组织并发症概率（NTCP）指标评估性能，并与两种深度学习模型对比。在测试队列中，DoseBridge的平均绝对误差为4.170 Gy，峰值信噪比为23.06 dB，结构相似性指数为0.798，在上述指标上均优于两种对比模型。临床靶区D95与参考剂量的差异为0.62±1.6 Gy；危及器官的平均剂量差异范围为-0.32至0.24 Gy，急性食管炎和放射性肺炎的NTCP差异分别为-0.40±2.2和0.52±3.4个百分点。仅改变射野掩码即可重定向预测的低剂量入射区域，同时保留高剂量靶区。据作者所知，DoseBridge是首个用于放射治疗剂量预测的去噪扩散桥模型，研究结果支持其作为肺IMPT的感知射野计划先验的可行性，有待更大外部队列的评估。
英文摘要
Most radiotherapy dose-prediction models use only CT images and anatomical structures, although intensity-modulated proton therapy (IMPT) dose also depends strongly on beam geometry and available clinical datasets are often small. We present DoseBridge, a denoising diffusion bridge model that uses the patient CT as a structured bridge endpoint and encodes plan-specific beam geometry in a spatially aligned beam mask. Multiscale fusion combines CT, target, organ-at-risk, and beam-mask representations with 1.95% additional parameters. DoseBridge was retrospectively evaluated on single-institution CT images and treatment plans from 52 patients with advanced-stage lung cancer treated with 60 Gy in 30 fractions; 42 cases were used for training and 10 for testing. Performance was assessed using image-similarity, dose-volume, and Lyman-Kutcher-Burman normal-tissue complication probability (NTCP) metrics and compared with two deep-learning models. On the test cohort, DoseBridge achieved a mean absolute error of 4.170 Gy, peak signal-to-noise ratio of 23.06 dB, and structural similarity index of 0.798, outperforming both comparison models on these metrics. Clinical target volume D95 differed from the reference dose by 0.62 +/- 1.6 Gy; signed organ-at-risk mean-dose differences ranged from -0.32 to 0.24 Gy, and NTCP differences were -0.40 +/- 2.2 and 0.52 +/- 3.4 percentage points for acute esophagitis and radiation pneumonitis, respectively. Changing only the beam mask redirected predicted low-dose entrance regions while preserving the high-dose target region. To our knowledge, DoseBridge is the first denoising diffusion bridge model for radiotherapy dose prediction. These results support its feasibility as a beam-aware planning prior for lung IMPT, pending evaluation in larger external cohorts.
