Single patient learning for adaptive radiotherapy dose prediction.
Single patient learning for adaptive radiotherapy dose prediction.
复制标题
单个患者学习自适应放疗剂量预测。
DOI：
10.1002/mp.16799
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发表时间：
2023
期刊：
影响因子：
3.8
通讯作者：
Nguyen,Dan
中科院分区：
文献类型：
--
作者：
Maniscalco,Austen;Liang,Xiao;Lin,Mu-Han;Jiang,Steve;Nguyen,Dan
背景在患者的放射治疗过程中，由于解剖学变化，例如，患者体重减轻或肿瘤缩小，随着时间的推移保持其初始治疗计划的准确性是具有挑战性的。在线调整RT计划以适应这些变化至关重要，但手动和耗时的流程阻碍了这一点。虽然基于深度学习（DL）的解决方案在简化自适应放射治疗（ART）工作流程方面显示出了希望，但它们通常需要大量的数据集来训练基于人群的模型。PurposeThis study extends our prior research by introducing a minimalist approach to patient specific adaptive dose prediction.与我们之前的方法（涉及微调预训练的群体模型）相比，这种新方法仅使用患者的初始治疗数据从头开始训练模型。这种患者特异性剂量预测器旨在提高临床可及性，从而使医生和治疗规划者能够在ART中做出更明智的定量决策。我们假设，与基于人群的DL模型相比，患者特异性DL模型将为各自的患者提供更准确的自适应剂量预测。另外选择了10名患者，他们各自的初始RT数据作为训练患者特异性（PS）模型的单一样本。这10例患者包含额外的26个ART计划，这些计划被保留作为测试数据集，以评价AP与PS模型剂量预测性能。我们使用平均绝对百分比误差（MAPE）通过将预测剂量与最初提供的地面真实剂量进行比较来评估模型性能。我们使用Wilcoxon符号秩检验来确定测试数据集中AP和PS模型结果之间MAPE的统计学显著差异。此外，我们计算预测和地面实况平均剂量分割结构和确定的差异，为每个them.ResultsThe平均MAPE跨AP和PS模型剂量预测的统计学意义之间的差异分别为5.759%和4.069%。Wilcoxon符号秩检验得出双尾p值= 2.9802×10−8$2.9802\ \times \ {10}^{ - 8}$，表明AP和PS模型剂量预测之间的MAPE差异具有统计学显著性，95%置信区间= [−2.1610，−1.0130]，表明95%置信度下，群体的AP和PS模型之间的MAPE差异位于该范围内。在总共24个分割结构中，对12个结构的平均剂量差异的比较表明具有统计学意义，双尾p值<0.05。结论我们的研究证明了患者特定深度学习模型在ART应用中的潜力。值得注意的是，我们的方法通过最小化所需训练数据集的大小来简化训练过程，因为只需要单个患者的初始治疗数据。考虑实施这种技术的外部机构可以将这种模型打包，以便它只需要上传用于模型培训和部署的参考治疗计划。我们的单个患者学习策略由于其最小的数据集要求及其在癌症治疗个性化中的实用性而在ART中展示了前景。
BackgroundThroughout a patient's course of radiation therapy, maintaining accuracy of their initial treatment plan over time is challenging due to anatomical changes‐for example, stemming from patient weight loss or tumor shrinkage. Online adaptation of their RT plan to these changes is crucial, but hindered by manual and time‐consuming processes. While deep learning (DL) based solutions have shown promise in streamlining adaptive radiation therapy (ART) workflows, they often require large and extensive datasets to train population‐based models.PurposeThis study extends our prior research by introducing a minimalist approach to patient‐specific adaptive dose prediction. In contrast to our prior method, which involved fine‐tuning a pre‐trained population model, this new method trains a model from scratch using only a patient's initial treatment data. This patient‐specific dose predictor aims to enhance clinical accessibility, thereby empowering physicians and treatment planners to make more informed, quantitative decisions in ART. We hypothesize that patient‐specific DL models will provide more accurate adaptive dose predictions for their respective patients compared to a population‐based DL model.MethodsWe selected 33 patients to train an adaptive population‐based (AP) model. Ten additional patients were selected, and their respective initial RT data served as single samples for training patient‐specific (PS) models. These 10 patients contained an additional 26 ART plans that were withheld as the test dataset to evaluate AP versus PS model dose prediction performance. We assessed model performance using Mean Absolute Percent Error (MAPE) by comparing predicted doses to the originally delivered ground truth doses. We used the Wilcoxon signed‐rank test to determine statistically significant differences in terms of MAPE between the AP and PS model results across the test dataset. Furthermore, we calculated differences between predicted and ground truth mean doses for segmented structures and determined statistical significance in the differences for each of them.ResultsThe average MAPE across AP and PS model dose predictions was 5.759% and 4.069%, respectively. The Wilcoxon signed‐rank test yielded two‐tailedp‐value = 2.9802×10−8$2.9802\ \times \ {10}^{ - 8}$, indicating that the MAPE differences between the AP and PS model dose predictions are statistically significant, and 95% confidence interval = [−2.1610, −1.0130], indicating 95% confidence that the MAPE difference between the AP and PS models for a population lies in this range. Out of 24 total segmented structures, the comparison of mean dose differences for 12 structures indicated statistical significance with two‐tailedp‐values < 0.05.ConclusionOur study demonstrates the potential of patient‐specific deep learning models in application to ART. Notably, our method streamlines the training process by minimizing the size of the required training dataset, as only a single patient's initial treatment data is required. External institutions considering the implementation of such a technology could package such a model so that it only requires the upload of a reference treatment plan for model training and deployment. Our single patient learning strategy demonstrates promise in ART due to its minimal dataset requirement and its utility in personalization of cancer treatment.