Site‐specific alert criteria to detect patient‐related errors with 3D EPID transit dosimetry

Abstract

Purpose: To assess the sensitivity of various EPID dosimetry alert indicators to patient-related variations and to determine alert threshold values that ensure excellent error detectability. Methods: Our virtual dose reconstruction method uses in air EPID measurements to calculate virtual 3D dose distributions within a CT data set. Patient errors are introduced by transforming the plan-CT into an error-CT data set. Virtual patient dose distributions reconstructed using the plan-CT and the error-CT data set are compared to the planned dose distributions by c(3%/3 mm) and DVH analysis using seven indicators: DD ISOC , c-mean, near c-max, c-pass rate, DPTV D2 , DPTV D50, and DPTV D98 . Translation and rotation patient setup errors and uniform contour changes are studied for 104 VMAT plans of 4 treatment sites. Lung expansions and contractions to simulate changes in lung density are considered for 26 IMRT lung plans. A ROC curve is generated for each combination of error and indicator. For each ROC curve, the AUC value and the optimal alert threshold value of the indicator are determined. Results: AUC values for c-indicators and DPTV D2 are consistently higher than for DD ISOC and DPTV D98 . For VMAT plans, error detectability to patient position shifts is worse for pelvic treatments and best for head-and-neck and brain plans. Excellent detectability is observed for 5 mm translations in head-and-neck plans (AUC = 0.94) and for 4°rotations in brain plans (AUC = 0.89). All sites but prostate show good-to-excellent detectability (AUC > 0.8) for 10 mm translations and 8°r otations and excellent detectability (AUC > 0.9) for AE6 mm patient contour changes. For head-andneck, excellent detectability is obtained with c-mean and c-pass rate threshold values of around 0.63 and 83%, respectively. For brain and rectum, these threshold values are 0.53 and 90%, respectively. In IMRT lung plans, expansions of 3 mm and contractions of 6 mm are detected (AUC > 0.8).Conclusions: By combining virtual dose reconstructions with synthetic patient data, we developed a framework to assess the sensitivity of our 3D EPID transit dosimetry method to patient-related variations. The detectability of each introduced error is specific to the treatment site and indicator used. Optimal alert criteria can be determined to ensure excellent detectability for each combination of error type and indicator. The alert threshold values and the magnitude of the error that can be detected are site-specific. In situations where the minimum error that can be detected is larger than the clinically desirable action level, EPID transit dosimetry must be used in combination with IGRT procedures to ensure correct patient positioning and early detection of anatomy variations.

In vivo dosimetry in external beam photon radiotherapy: Requirements and future directions for research, development, and clinical practice

“…In practice, and owing to the presence of false positives and false negatives, EIVD systems are typically implemented in combination with other patient-specific QA systems. False negatives are of particular importance because they hide relevant errors that are not being detected [82]. To detect these errors, EIVD must be used in combination with image-guided radiation therapy procedures such as CBCT visualization and/or CBCT-based dose calculations.…”

“…The experimental acquisition of EPID measurements to produce such samples is typically a cumbersome process, which explains why there are only a few studies on the topic in the IVD literature [78][79][80][81]. Recently, use has been made of synthetic EPID images to eliminate the need for phantom error introduction and positioning [82]. Another alternative is to model possible errors by introducing modifications in the TPS [83].…”

Using in vivo EPID images to detect and quantify patient anatomy changes with gradient dose segmented analysis

“…It has been demonstrated that this approach can detect patient setup errors as well as uniform contour changes. 16 An analysis technique recently introduced in a doctoral dissertation, originally for pretreatment IMRT QA comparisons, gradient dose segmented analysis (GDSA), has been shown to predict changes in PTV D mean from using only measurements from IMRT QA diode arrays without the need for reconstruction methods. 17 The work outlined here makes use of GDSA to analyze EPID transit images.…”

“…Since the reconstruction is performed on planning CT data, the goal of this metric is not an accurate determination of the dose delivered to the patient but a method to detect clinically relevant errors. It has been demonstrated that this approach can detect patient setup errors as well as uniform contour changes 16 …”

Transit and non‐transit 3D EPID dosimetry versus detector arrays for patient specific QA

“…38 Second, the dose delivered to the patient during treatment is affected by patient setup variations and patient anatomical changes. 39 Last, another limitation of the algorithm is that it uses the planning CT as patient anatomy model for dose reconstruction. To better cope with the challenge of patient variations, more accurate results are expected once the daily patient anatomy is used in the reconstruction.…”