Yu Xiuwen, Lin Jiabin, Gong Changfei, Zhang Minhui, Luo Xianyu, Liu Qiegen, Zhang Yun
Department of Electronic Information Engineering, Nanchang University, Nanchang, China.
Department of Radiation Oncology, Jiangxi Cancer Hospital & Institute, Jiangxi Clinical Research Center for Cancer, The Second Affiliated Hospital of Nanchang Medical College, Nanchang, China.
Front Oncol. 2025 Jan 3;14:1473050. doi: 10.3389/fonc.2024.1473050. eCollection 2024.
Implementing pre-treatment patient-specific quality assurance (prePSQA) for cancer patients is a necessary but time-consuming task, imposing a significant workload on medical physicists. Currently, the prediction methods used for prePSQA fall under the category of supervised learning, limiting their generalization ability and resulting in poor performance on new data. In the context of this work, the limitation of traditional supervised models was broken by proposing a conditional generation method utilizing unsupervised diffusion model.
A conditional generation method base on the score-based diffusion model was proposed, which employed diffusion model for the first time to predict the predict patients' therapeutic doses (TherapDose). The proposed diffusion model TherapDose prediction method (DMTP) learns the data distribution of dose images. The data distribution contains the quantitative relationship between the radiotherapy dose (RTDose) derived from the VMAT plan files of the Treatment Planning System (TPS) and the measured Dose (MDose, i.e., TherapDose) obtained from the Dolphin Compass physical system. By sampling from the learnt distribution, efficient prediction of TherapDose was achieved. The training dataset comprises RTDose, and the MDose. The three-dimensional information of dose slice was utilized to predict TherapDose, aiming to enhance the accuracy and efficiency of TherapDose prediction. Root mean square error (RMSE), mean absolute error (MAE), and structural similarity (SSIM) metrics were leveraged to validate the effectiveness of the proposed method. Meanwhile, CT images were further added to test the impacts of CT images on the prediction effect of MDose.
The DMTP method has demonstrated superior performance in predicting TherapDose within key anatomical regions including the head and neck, chest, and abdomen, outperforming existing state-of-the-art methods by achieving high-quality predictions as measured across different evaluation metrics. It indicates that the proposed method is highly effective and accurate in its dose prediction capabilities.
The proposed method has proven to be highly effective, consistently outperforming state-of-the-art techniques in MDose prediction across multiple anatomical regions and evaluation metrics. This method can serve as a clinical aid to assist medical physicists in diminishing the measurement workload associated with prePSQA.
为癌症患者实施治疗前患者特异性质量保证（prePSQA）是一项必要但耗时的任务，给医学物理师带来了巨大的工作量。目前，用于prePSQA的预测方法属于监督学习类别，限制了它们的泛化能力，导致在新数据上表现不佳。在这项工作的背景下，通过提出一种利用无监督扩散模型的条件生成方法，打破了传统监督模型的局限性。
提出了一种基于分数的扩散模型的条件生成方法，该方法首次采用扩散模型来预测患者的治疗剂量（TherapDose）。所提出的扩散模型治疗剂量预测方法（DMTP）学习剂量图像的数据分布。该数据分布包含从治疗计划系统（TPS）的容积调强放疗（VMAT）计划文件中导出的放射治疗剂量（RTDose）与从海豚罗盘物理系统获得的测量剂量（MDose，即TherapDose）之间的定量关系。通过从学习到的分布中采样，实现了对TherapDose的有效预测。训练数据集包括RTDose和MDose。利用剂量切片的三维信息来预测TherapDose，旨在提高TherapDose预测的准确性和效率。利用均方根误差（RMSE）、平均绝对误差（MAE）和结构相似性（SSIM）指标来验证所提出方法的有效性。同时，进一步添加CT图像以测试CT图像对MDose预测效果的影响。
DMTP方法在预测包括头颈部、胸部和腹部在内的关键解剖区域的TherapDose方面表现出卓越的性能，通过在不同评估指标上进行测量，实现了高质量的预测，优于现有的最先进方法。这表明所提出的方法在剂量预测能力方面非常有效和准确。
所提出的方法已被证明非常有效，在多个解剖区域和评估指标的MDose预测方面始终优于最先进的技术。该方法可作为一种临床辅助工具，帮助医学物理师减少与prePSQA相关的测量工作量。
