To study radiotherapy-related adverse effects, detailed dose information (3D distribution) is needed for accurate dose-effect modeling. For childhood cancer survivors who underwent radiotherapy in the pre-CT era, only 2D radiographs were acquired, thus 3D dose distributions must be reconstructed from limited information. State-of-the-art methods achieve this by using 3D surrogate anatomies. These can however lack personalization and lead to coarse reconstructions. We present and validate a surrogate-free dose reconstruction method based on Machine Learning (ML). Abdominal planning CTs (n = 142) of recently-treated childhood cancer patients were gathered, their organs at risk were segmented, and 300 artificial Wilms’ tumor plans were sampled automatically. Each artificial plan was automatically emulated on the 142 CTs, resulting in 42,600 3D dose distributions from which dose-volume metrics were derived. Anatomical features were extracted from digitally reconstructed radiographs simulated from the CTs to resemble historical radiographs. Further, patient and radiotherapy plan features typically available from historical treatment records were collected. An evolutionary ML algorithm was then used to link features to dose-volume metrics. Besides 5-fold cross validation, a further evaluation was done on an independent dataset of five CTs each associated with two clinical plans. Cross-validation resulted in mean absolute errors ≤ 0.6 Gy for organs completely inside or outside the field. For organs positioned at the edge of the field, mean absolute errors ≤ 1.7 Gy for , ≤ 2.9 Gy for , and ≤ 13% for and , were obtained, without systematic bias. Similar results were found for the independent dataset. To conclude, we proposed a novel organ dose reconstruction method that uses ML models to predict dose-volume metric values given patient and plan features. Our approach is not only accurate, but also efficient, as the setup of a surrogate is no longer needed.

为了研究与放射疗法相关的不良反应，需要详细的剂量信息（3D分布）以进行准确的剂量效应模型。对于在CT前时代接受放射治疗的儿童癌症幸存者，仅获得了2D射线照片，因此必须从有限的信息中重建3D剂量分布。最新的方法通过使用3D替代解剖结构来实现。然而，这些可能缺乏个性化并导致粗略的重建。我们提出并验证了基于机器学习（ML）的无替代剂量重建方法。腹部的CT规划（ñ ＝ 142）最近接受治疗的儿童期癌症患者，对其危险器官进行了分割，并自动采样了300个人工Wilms的肿瘤计划。每个人工计划都自动在142个CT上进行仿真，从而得到42,600个3D剂量分布，从中可以得出剂量-体积指标。解剖特征是从CT模拟的数字重建X射线照片中提取的，类似于历史X射线照片。此外，收集了通常可从历史治疗记录中获得的患者和放射治疗计划特征。然后使用进化的ML算法将特征链接到剂量-体积指标。除了5倍交叉验证外，还对独立的5个CT数据集进行了进一步评估，每个CT与两个临床计划相关。交叉验证导致平均绝对误差≤0。6 Gy：完全位于野外或野外的器官。对于位于视野边缘的器官，平均绝对误差≤1.7 Gy获得，对于，≤2.9 Gy ，对于和，≤13％，没有系统偏差。对于独立数据集发现了相似的结果。总而言之，我们提出了一种新颖的器官剂量重建方法，该方法使用ML模型预测给定患者和计划特征的剂量-体积指标值。我们的方法不仅准确而且高效，因为不再需要设置代理。