Department of Nuclear Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Br J Radiol. 2023 Apr 1;96(1145):20220373. doi: 10.1259/bjr.20220373. Epub 2023 Mar 6.
A dose deposition matrix (DDM) prediction method using several voxel features and a machine learning (ML) approach is proposed for plan optimization in radiation therapy.
Head and lung cases with the inhomogeneous medium are used as training and testing data. The prediction model is a cascade forward backprop neural network where the input is the features of the voxel, including 1) voxel to body surface distance along the beamlet axis, 2) voxel to beamlet axis distance, 3) voxel density, 4) heterogeneity corrected voxel to body surface distance, 5) heterogeneity corrected voxel to beamlet axis, and (6) the dose of voxel obtained from the pencil beam (PB) algorithm. The output is the predicted voxel dose corresponding to a beamlet. The predicted DDM was used for plan optimization (ML method) and compared with the dose of MC-based plan optimization (MC method) and the dose of pencil beam-based plan optimization (PB method). The mean absolute error (MAE) value was calculated for full volume relative to the dose of the MC method to evaluate the overall dose performance of the final plan.
For patient with head tumor, the ML method achieves MAE value 0.49 × 10 and PB has MAE 1.86 × 10. For patient with lung tumor, the ML method has MAE 1.42 × 10 and PB has MAE 3.72 × 10. The maximum percentage difference in PTV dose coverage (D) between ML and MC methods is no more than 1.2% for patient with head tumor, while the difference is larger than 10% using the PB method. For patient with lung tumor, the maximum percentage difference in PTV dose coverage (D) between ML and MC methods is no more than 2.1%, while the difference is larger than 16% using the PB method.
In this work, a reliable DDM prediction method is established for plan optimization by applying several voxel features and the ML approach. The results show that the ML method based on voxel features can obtain plans comparable to the MC method and is better than the PB method in achieving accurate dose to the patient, which is helpful for rapid plan optimization and accurate dose calculation.
Establishment of a new machine learning method based on the relationship between the voxel and beamlet features for dose deposition matrix prediction in radiation therapy.
提出了一种使用多个体素特征和机器学习（ML）方法的剂量沉积矩阵（DDM）预测方法，用于放射治疗中的计划优化。
使用不均匀介质的头部和肺部病例作为训练和测试数据。预测模型是一个级联正向反向传播神经网络，其输入是体素的特征，包括 1）沿着射束轴的体素到体表面的距离，2）体素到射束轴的距离，3）体素密度，4）校正异质性的体素到体表面的距离，5）校正异质性的体素到射束轴的距离，以及 6）从铅笔束（PB）算法获得的体素剂量。输出是与射束对应的预测体素剂量。预测的 DDM 用于计划优化（ML 方法），并与基于 MC 的计划优化（MC 方法）和基于铅笔束的计划优化（PB 方法）的剂量进行比较。计算全体积相对于 MC 方法剂量的平均绝对误差（MAE）值，以评估最终计划的整体剂量性能。
对于头部肿瘤患者，ML 方法的 MAE 值为 0.49×10，而 PB 方法的 MAE 值为 1.86×10。对于肺部肿瘤患者，ML 方法的 MAE 值为 1.42×10，而 PB 方法的 MAE 值为 3.72×10。头部肿瘤患者 ML 和 MC 方法之间 PTV 剂量覆盖率（D）的最大百分比差异不超过 1.2%，而 PB 方法的差异大于 10%。对于肺部肿瘤患者，ML 和 MC 方法之间 PTV 剂量覆盖率（D）的最大百分比差异不超过 2.1%，而 PB 方法的差异大于 16%。
在这项工作中，通过应用多个体素特征和 ML 方法，为计划优化建立了一种可靠的 DDM 预测方法。结果表明，基于体素特征的 ML 方法可以获得与 MC 方法相当的计划，并且在实现患者准确剂量方面优于 PB 方法，这有助于快速计划优化和准确剂量计算。
建立了一种新的基于射束和体素特征之间关系的机器学习方法，用于放射治疗中的剂量沉积矩阵预测。
