Abstract
PurposeTo find a suitable method for analyzing electronic portal imaging device (EPID) transmission fluence maps for the identification of position errors in the in vivo dose monitoring of patients with Graves’ ophthalmopathy (GO).MethodsPosition errors combining 0-, 2-, and 4-mm errors in the left-right (LR), anterior-posterior (AP), and superior-inferior (SI) directions in the delivery of 40 GO patient radiotherapy plans to a human head phantom were simulated and EPID transmission fluence maps were acquired. Dose difference (DD) and structural similarity (SSIM) maps were calculated to quantify changes in the fluence maps. Three types of machine learning (ML) models that utilize radiomics features of the DD maps (ML 1 models), features of the SSIM maps (ML 2 models), and features of both DD and SSIM maps (ML 3 models) as inputs were used to perform three types of position error classification, namely a binary classification of the isocenter error (type 1), three binary classifications of LR, SI, and AP direction errors (type 2), and an eight-element classification of the combined LR, SI, and AP direction errors (type 3). Convolutional neural network (CNN) was also used to classify position errors using the DD and SSIM maps as input.ResultsThe best-performing ML 1 model was XGBoost, which achieved accuracies of 0.889, 0.755, 0.778, 0.833, and 0.532 in the type 1, type 2-LR, type 2-AP, type 2-SI, and type 3 classification, respectively. The best ML 2 model was XGBoost, which achieved accuracies of 0.856, 0.731, 0.736, 0.949, and 0.491, respectively. The best ML 3 model was linear discriminant classifier (LDC), which achieved accuracies of 0.903, 0.792, 0.870, 0.931, and 0.671, respectively. The CNN achieved classification accuracies of 0.925, 0.833, 0.875, 0.949, and 0.689, respectively.ConclusionML models and CNN using combined DD and SSIM maps can analyze EPID transmission fluence maps to identify position errors in the treatment of GO patients. Further studies with large sample sizes are needed to improve the accuracy of CNN.
Highlights
As a treatment method for Graves’ ophthalmopathy (GO), an eye disease related to autoimmune thyroid disease [1], radiotherapy can be applied with satisfactory control while producing relatively slight post-radiotherapeutic complications [2]
Cone beam computed tomography (CBCT) is generally used clinically to correct position errors prior to treatment, the dose delivered to a patient cannot be verified using CBCT alone because position or other errors can potentially occur during treatment
For patients with GO, the rigid anatomical structure around the target volumes ensures that anatomical change has a negligible impact (Figure 1B), and the ML 1 Model - XGBoost
Summary
As a treatment method for Graves’ ophthalmopathy (GO), an eye disease related to autoimmune thyroid disease [1], radiotherapy can be applied with satisfactory control while producing relatively slight post-radiotherapeutic complications [2]. To enable the delivery of conformal and uniform doses to these target volumes while reducing the dose received by normal tissue, intensity-modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) are often used for GO patients [1,2,3] because these approaches generate a steep dose gradient between the target volume and OARs [4, 5]. This implies that errors during treatment, such as position errors, have a significant impact on the treatment results [6]. As a method for monitoring treatment, in vivo dosimetry for obtaining information on the doses delivered to patients has significant potential
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