Wang Hui-Ju, Maniscalco Austen, Sher David, Lin Mu-Han, Jiang Steve, Nguyen Dan
Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, United States of America.
Mach Learn Health. 2025 Dec 1;1(1):015008. doi: 10.1088/3049-477X/adfade. Epub 2025 Aug 26.
Online adaptive radiation therapy (ART) personalizes treatment plans by accounting for daily anatomical changes, requiring workflows distinct from conventional radiotherapy. Deep learning-based dose prediction models can enhance treatment planning efficiency by rapidly generating accuracy dose distributions, reducing manual trial-and-error and accelerating the overall workflow; however, most existing approaches overlook critical pre-treatment plan information-specifically, physician-defined clinical objectives tailored to individual patients. To address this limitation, we introduce the multi-headed U-Net (MHU-Net), a novel architecture that explicitly incorporates physician intent from pre-treatment plans to improve dose prediction accuracy in adaptive head and neck cancer treatments. Our dataset comprised 43 patients, each with pre-treatment plans, adaptive treatment plans, structure sets, and CT images. MHU-Net builds upon the widely adopted Stander U-Net architecture, extending it with a dual-head design: the primary head processes adaptive session contours and their corresponding signed distance maps, while the secondary head integrates pre-treatment contours, signed distance maps, and dose distributions. The features are merged within a primary U-Net framework to enhance dose prediction accuracy for adaptive treatment sessions. To evaluate the effectiveness of MHU-Net, we conducted a comparative analysis against U-Net. On average, MHU-Net reduced organ-at-risk dose prediction errors, achieving 1.78% lower maximum dose error and 1.22% lower mean dose error compared to U-Net. For the planning target volume, MHU-Net demonstrated significantly improved accuracy, with maximum and mean dose errors of 3.54 ± 2.75% and 1.07 ± 0.88%, respectively, outperforming U-Net's corresponding errors of 5.36 ± 4.19% and 2.76 ± 2.22% ( < 0.05). Taken together, these findings demonstrate that the proposed MHU-Met advances DL-based dose prediction for ART by effectively integrating both pre-treatment and adaptive session data. This approach facilitates the generation of dose distributions that more closely resemble the clinical ground truth, supporting personalization in ART planning and improving alignment with physician intent and treatment objectives.
在线自适应放射治疗（ART）通过考虑每日的解剖结构变化来实现治疗计划的个性化，这需要与传统放射治疗不同的工作流程。基于深度学习的剂量预测模型可以通过快速生成准确的剂量分布来提高治疗计划效率，减少人工试错并加速整个工作流程；然而，大多数现有方法忽略了关键的治疗前计划信息，特别是针对个体患者的医生定义的临床目标。为了解决这一局限性，我们引入了多头U-Net（MHU-Net），这是一种新颖的架构，它明确纳入了治疗前计划中的医生意图，以提高头颈癌自适应治疗中的剂量预测准确性。我们的数据集包括43名患者，每名患者都有治疗前计划、自适应治疗计划、结构集和CT图像。MHU-Net基于广泛采用的标准U-Net架构构建，通过双头设计对其进行扩展：主头处理自适应疗程轮廓及其相应的有符号距离图（signed distance maps），而次头整合治疗前轮廓、有符号距离图和剂量分布。这些特征在主U-Net框架内合并，以提高自适应治疗疗程的剂量预测准确性。为了评估MHU-Net的有效性，我们与U-Net进行了对比分析。平均而言，MHU-Net减少了危及器官的剂量预测误差，与U-Net相比，最大剂量误差降低了1.78%，平均剂量误差降低了1.22%。对于计划靶体积，MHU-Net显示出显著提高的准确性，最大剂量误差和平均剂量误差分别为3.54±2.75%和1.07±0.88%，优于U-Net相应的5.36±4.19%和2.76±2.22%的误差（<0.05）。综上所述，这些发现表明，所提出的MHU-Net通过有效整合治疗前和自适应疗程数据，推进了基于深度学习的ART剂量预测。这种方法有助于生成更接近临床真实情况的剂量分布，支持ART计划中的个性化，并改善与医生意图和治疗目标的一致性。
