背景:这项研究的目的是通过使用与机器参数和类平衡技术相关的输入特征来提高深度学习(DL)模型在预测和分类IMRT伽马通过率(GPR)方面的性能。
方法:回顾性收集来自204例鼻咽癌患者IMRT计划的2348个字段,形成一个数据集。输入特征映射,包括注量,叶间隙,两岸的叶片速度,和相应的错误,是从动态日志文件构造的。SHAP框架用于计算每个特征对递归特征消除的模型输出的影响。一系列基于UNet++的模型在获得的八个特征集上进行了训练,使用三种微调方法,包括标准均方误差(MSE)损失,一种重新采样技术,和拟议的加权MSE损失(WMSE)。平均绝对误差的差异,接收器工作特性曲线下面积(AUC),灵敏度,比较了不同模型之间的特异性。
结果:使用包括叶片速度和叶片间隙特征的特征集训练的模型比其他模型更准确地预测了失败领域的GPR(F(7,147)=5.378,p<0.001)。在三种微调方法中,WMSE损失在预测失败场的GPR方面具有最高的准确性(F(2,42)=14.149,p<0.001),而在预测通过场的GPR时观察到相反的趋势(F(2,730)=9.907,p<0.001)。与其他模型相比,WMSE_FS5模型实现了优异的AUC(0.92)和更平衡的灵敏度(0.77)和特异性(0.89)。
结论:机器参数可以为DL中的GPR预测提供有区别的输入特征。新颖的加权损失函数证明了在通过和失败的字段之间平衡预测和分类准确性的能力。所提出的方法能够提高DL模型在预测和分类GPR方面的性能,并且可以潜在地集成到计划优化过程中,以生成更高的可交付性计划。
背景:该临床试验于3月26日在中国临床试验注册中心注册,2020年(注册号:ChiCTR2000031276)。https://clinicaltrials.gov/ct2/show/ChiCTR2000031276.
BACKGROUND: The purpose of this study was to improve the deep learning (DL) model performance in predicting and classifying IMRT gamma passing rate (GPR) by using input features related to machine parameters and a class balancing technique.
METHODS: A total of 2348 fields from 204 IMRT plans for patients with nasopharyngeal carcinoma were retrospectively collected to form a dataset. Input feature maps, including fluence, leaf gap, leaf speed of both banks, and corresponding errors, were constructed from the dynamic log files. The SHAP framework was employed to compute the impact of each feature on the model output for recursive feature elimination. A series of UNet++ based models were trained on the obtained eight feature sets with three fine-tuning methods including the standard mean squared error (MSE) loss, a re-sampling technique, and a proposed weighted MSE loss (WMSE). Differences in mean absolute error, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were compared between the different models.
RESULTS: The models trained with feature sets including leaf speed and leaf gap features predicted GPR for failed fields more accurately than the other models (F(7, 147) = 5.378, p < 0.001). The WMSE loss had the highest accuracy in predicting GPR for failed fields among the three fine-tuning methods (F(2, 42) = 14.149, p < 0.001), while an opposite trend was observed in predicting GPR for passed fields (F(2, 730) = 9.907, p < 0.001). The WMSE_FS5 model achieved a superior AUC (0.92) and more balanced sensitivity (0.77) and specificity (0.89) compared to the other models.
CONCLUSIONS: Machine parameters can provide discriminative input features for GPR prediction in DL. The novel weighted loss function demonstrates the ability to balance the prediction and classification accuracy between the passed and failed fields. The proposed approach is able to improve the DL model performance in predicting and classifying GPR, and can potentially be integrated into the plan optimization process to generate higher deliverability plans.
BACKGROUND: This clinical trial was registered in the Chinese Clinical Trial Registry on March 26th, 2020 (registration number: ChiCTR2000031276). https://clinicaltrials.gov/ct2/show/ChiCTR2000031276.