Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Physics and Biology in Medicine Interdisciplinary Program, David Geffen School of Medicine, University of California - Los Angeles, Los Angeles, California, USA.
Med Phys. 2023 Jun;50(6):3671-3686. doi: 10.1002/mp.16372. Epub 2023 Apr 17.
While many have speculated on the reasons for gamma comparison insensitivity for patient-specific quality assurance analysis, the true reasons for insensitivity have not yet been elucidated. Failing to understand the reasons for this technique's insensitivity limits our ability to either improve the gamma metric to increase sensitivity of the comparison or the capacity to develop new comparison techniques that circumvent the limitations of the gamma comparison.
To understand the underlying cause(s) for gamma comparison insensitivity and determine if simple plan characteristics can quantitatively predict for gamma comparison sensitivity.
Known MLC and MU errors of varying magnitudes were induced on simple test fields to preliminarily investigate where gamma failures first begin to appear as error magnitude is increased. Gamma value maps between error-induced plan calculations and error-free plan calculations were created for 20 IMRT and 20 VMAT cases, each on three different detector geometries-ArcCHECK, MapCHECK, and Delta4. Gamma value maps were qualitatively compared to dose-gradient maps, and quantitative comparisons were performed between various plan descriptors and the computed gamma sensitivity for five different classes of induced errors were utilized to determine if any plan descriptor could predict the gamma sensitivity on a case-by-case basis. All comparisons were performed in a calculation-only scenario to remove uncertainties introduced by comparisons made with real patient specific QA measurements.
Gamma value maps with increasing induced error magnitude illustrated that gamma comparisons fail first in high-dose, low-gradient regions of the field. Conversely, in areas of high gradient, gamma values typically remain low, even in the presence of large errors, regardless of detector geometry and gamma normalization setting. Thus, the complex, and often overlapping, high dose gradients in plans appear to be a limiting factor in gamma comparison sensitivity as the number of points along these gradients may often outnumber the points available for failing the comparison in lower gradient regions of the field. None of the simple plan descriptors studied were able to quantitively predict gamma comparison sensitivity, suggesting that quantitatively predicting the sensitivity of gamma comparisons on a case-by-case basis may require a combination of multiple factors or metrics not studied here.
Simple plan descriptors and the number of points in high-dose, low-gradient regions of the field did not quantitively predict for gamma comparison sensitivity. However, it is clear from gradient and gamma value maps that gamma comparisons fail first in high-dose, low-gradient regions of the field in the presence of known induced errors, which we have shown to be independent of detector geometry and gamma comparison normalization setting. Gamma comparison sensitivity is thus limited by the ever-increasing complexity of plans and is particularly important to consider as treatment volumes become smaller and the complexity of overlapping plan gradients increases. This suggests that new methods for patient-specific QA comparisons are required to circumvent this limitation.
虽然许多人推测了伽马比对患者特定质量保证分析不敏感的原因，但尚未阐明不敏感的真正原因。如果不能理解该技术不敏感的原因，就会限制我们提高伽马度量以提高比较敏感性的能力，或者限制开发规避伽马比较局限性的新比较技术的能力。
了解伽马比较不敏感的根本原因，并确定简单的计划特征是否可以定量预测伽马比较的灵敏度。
在简单的测试场地上引入不同大小的已知 MLC 和 MU 误差，初步研究当误差幅度增加时，伽马失败首先开始出现的位置。为 20 个 IMRT 和 20 个 VMAT 病例中的每一个创建了 20 个 IMRT 和 20 个 VMAT 病例的误差诱导计划计算与误差自由计划计算之间的伽马值映射，每个病例在三种不同的探测器几何形状-ArcCHECK、MapCHECK 和 Delta4 上。伽马值映射与剂量梯度图进行定性比较，并对各种计划描述符进行定量比较，针对五种不同类型的诱导误差计算计算的伽马灵敏度，以确定是否可以根据具体情况预测每个病例的伽马灵敏度。所有比较都是在仅计算的情况下进行的，以消除通过与真实患者特定 QA 测量进行比较引入的不确定性。
随着诱导误差幅度的增加，伽马值映射图表明，伽马比较首先在该场的高剂量、低梯度区域失败。相反，在高梯度区域，即使存在较大误差，伽马值通常也保持较低，而不管探测器几何形状和伽马归一化设置如何。因此，由于沿着这些梯度的点数可能经常超过场中较低梯度区域中失败比较的可用点数，因此计划中复杂且经常重叠的高剂量梯度似乎成为伽马比较灵敏度的限制因素。
简单的计划描述符和高剂量、低梯度区域的点数不能定量预测伽马比较的灵敏度。然而，从梯度和伽马值映射图可以清楚地看出，在存在已知诱导误差的情况下，伽马比较首先在高剂量、低梯度区域失败，这与探测器几何形状和伽马比较归一化设置无关。因此，伽马比较灵敏度受到计划日益复杂的限制，特别是当治疗体积变得更小且重叠计划梯度的复杂性增加时，这一点非常重要。这表明需要新的患者特定 QA 比较方法来规避这一限制。
