发表机构
Indian Institute of Technology Kharagpur; Tata Medical Center Kolkata（印度理工学院克勒格布尔分校; 塔塔医疗中心 Kolkata）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出物理学引导的3D深度学习模型Dose-PlanNet，用于前列腺放疗剂量预测，在保证靶区覆盖度的同时，显著提升高危器官高剂量区保护效果，可加速放疗工作流程并满足临床剂量学要求。
AI 中文摘要
自动化前列腺放射治疗计划的剂量学操作十分复杂，尤其是针对极端大分割治疗方案。本研究提出了Dose-PlanNet，一种用于预测剂量分布的物理学引导型3D深度学习架构。该模型在一项前瞻性试验的患者队列上进行评估，该队列采用了两种不同的剂量分割方案。Dose-PlanNet实现了相当的靶区覆盖度（D₉₅），统计分析显示其靶区均匀性略有降低（p<0.001），但该模型在高危器官的高剂量区保护方面取得了统计学显著的改善（p<0.001）。在针对严格的前瞻性随机方案体积约束进行评估时，自动生成的计划在14个中度大分割组计划中有11个符合预设的临床接受标准，在12个体部立体定向放射治疗组计划中有9个符合。该流程表明，物理学驱动的深度学习可加速放射治疗工作流程，同时安全维持高精度临床部署所需的严格剂量学质量。
英文摘要
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.
CommentsThe paper consists of 22 pages, 4 figures, 6 tables. The end-to-end pipeline of Dose-PlanNet will soon be made available on the GitHub repository of CHAVI-India (https://github.com/CHAVI-India)
