459 Daily constancy checks of flatness, symmetry and output for photon beams using a commercial electronic portal image device (EPID)

Calvo, J.F.; Eraso, Aranzazu; Garrido, L.; Casals, Joan

doi:10.1016/s0167-8140(05)81435-5

Cited by 1 publication

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“…This implies that the machine output error can be eliminated via EPID calibration. On the other hand, variation of beam uniformity is complex [20] and long-term studies will be required to evaluate its impact on EPID measurements. Besides, the simulated translation positioning errors failed to cover all the possibilities of positioning errors.…”

Section: Discussionmentioning

confidence: 99%

Radiomics analysis of EPID measurements for patient positioning error detection in thyroid associated ophthalmopathy radiotherapy

Zhang¹,

Dai²,

Xiang

et al. 2021

Physica Medica

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Electronic portal imaging detector (EPID)-based patient positioning verification is an important component of safe radiotherapy treatment delivery. In computer simulation studies, learning-based approaches have proven to be superior to conventional gamma analysis in the detection of positioning errors. To approximate a clinical scenario, the detectability of positioning errors via EPID measurements was assessed using radiomics analysis for patients with thyroid-associated ophthalmopathy. Methods: Treatment plans of 40 patients with thyroid-associated ophthalmopathy were delivered to a solid anthropomorphic head phantom. To simulate positioning errors, combinations of 0-, 2-, and 4-mm translation errors in the left-right (LR), superior-inferior (SI), and anterior-posterior (AP) directions were introduced to the phantom. The positioning errors-induced dose differences between measured portal dose images were used to predict the magnitude and direction of positioning errors. The detectability of positioning errors was assessed via radiomics analysis of the dose differences. Three classification models-support vector machine (SVM), k-nearest neighbors (KNN), and XGBoost-were used for the detection of positioning errors (positioning errors larger or smaller than 3 mm in an arbitrary direction) and direction classification (positioning errors larger or smaller than 3 mm in a specific direction). The receiver operating characteristic curve and the area under the ROC curve (AUC) were used to evaluate the performance of classification models. Results: For the detection of positioning errors, the AUC values of SVM, KNN, and XGBoost models were all above 0.90. For LR, SI, and AP direction classification, the highest AUC values were 0.76, 0.91, and 0.80, respectively. Conclusions: Combined radiomics and machine learning approaches are capable of detecting the magnitude and direction of positioning errors from EPID measurements. This study is a further step toward machine learningbased positioning error detection during treatment delivery with EPID measurements.

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Section: Discussionmentioning

confidence: 99%