用深度学习预测前列腺放疗剂量分布，提升关键器官保护。
Dose-PlanNet: Physics Based Radiotherapy Dose Prediction with Deep Learning
- 结合物理规律的3D深度学习模型，预测放疗剂量分布。
- 靶区覆盖达标，高剂量器官保护显著改善（p<0.001）。
- 自动化计划满足临床标准，适合高精度放疗部署。
自动化前列腺放疗计划在极端超分割方案下具有剂量学复杂性。本研究提出Dose-PlanNet，一种基于物理的3D深度学习架构，用于预测剂量分布。该模型在前瞻性试验的患者队列中评估，采用两种不同分割方案。结果表明，模型在靶区覆盖（D95）方面表现相当，但靶区均匀性略有下降（p<0.001）；然而，在高剂量危及器官保护方面有统计学显著改善（p<0.001）。在严格的前瞻性随机试验体积约束下，自动计划在14例中度超分割组中有11例、12例立体定向体部放疗组中有9例达到预设临床接受标准。该流程证明，物理信息引导的深度学习可加速放疗工作流，同时安全维持高精度临床应用所需的严格剂量学质量。
原文摘要 · Abstract (English)
Automating prostate radiotherapy treatment planning is dosimetrically complex, particularly for extreme hypofractionated regimens. In this study, we introduce Dose-PlanNet, a physics-guided 3D deep learning architecture designed to predict dose distributions. This model's performance was evaluated on a cohort of patients treated in a prospective trial where two different dose fractionation regimens were employed. Dose-PlanNet achieved comparable target coverage ($D_{95}$), though statistical analysis revealed a marginal reduction in target homogeneity ($p<0.001$) offset. However the model achieved statistically significant improvements in high-dose organ-at-risk sparing ($p<0.001$). When evaluated against strict Prospective Randomized protocol volumetric constraints, automated plans met prespecified clinical acceptance criteria in $11$ out of $14$ Moderate Hypofraction Arm plans and $9$ out of $12$ Stereotactic Body Radiation Therapy Arm plans. This pipeline demonstrates that physics-informed deep learning can accelerate radiotherapy workflows while safely maintaining the stringent dosimetric quality required for high-precision clinical deployment.
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