Cummins Donal, O'Sullivan Siobhra, Dunne Mary, McDermott Ronan, Keys Maeve, Fitzpatrick David, Faul Clare, Javadpour Mohsen, Skourou Christina
St Luke's Radiation Oncology Network, Dublin 6, Ireland.
St Luke's Institute of Cancer Research, Dublin 6, Ireland.
J Radiosurg SBRT. 2020;7(1):67-75.
A major factor in dose-fractionation selection for intracranial metastases in stereotactic radiosurgery (SRS) is the size of the target lesion and consequently the dose-volume to the surrounding normal brain tissue (NTV), as this has been correlated with brain radiation necrosis (RN). This study outlines the development and validation of a predictive model that can estimate the NTV for a range of dose-fractionation schemes based on target diameter from a patient's MRI. Data from a cohort of historical SRS clinical treatment plans were used to extract three key input parameters for the model - conformity index, gradient index, and a scaling factor which were then defined as a function of target volume. The relationship between the measured tumour diameter and the NTV was established by approximating the target to a spherical volume covered by the prescription dose. A scaling factor (λ) describes the non-linear fall-off of dose beyond the target. This was then used to provide a first-order approximation of the resulting NTV. The predictive model was retrospectively validated using linear regression against actual NTV values from 39 historical SRS plans which were independent to the derivation process. The model was validated for both three-dimensional (3D) target diameter and axial-only two-dimensional (2D) estimates of target diameter values. The prediction model directly relates lesion diameter to NTV volume (cc) and thus RN risk for a given dose-fractionation. The predicted NTV (cc) for both 3D- and 2D-based volume estimates could statistically significantly predict the actual NTV (cc): R=0.942 (p<.0005) for 3D-based estimate, and R=0.911 (p=<.0005) for axial-only 2D-based estimate. This knowledge-based method for NTV prediction in intracranial SRS provides the clinician with a decision support tool to appropriately select dose-fractionation prior to treatment planning.
立体定向放射外科(SRS)中颅内转移瘤剂量分割选择的一个主要因素是靶病变的大小,进而影响周围正常脑组织(NTV)的剂量体积,因为这与脑放射性坏死(RN)相关。本研究概述了一种预测模型的开发与验证,该模型可根据患者MRI的靶直径,针对一系列剂量分割方案估算NTV。来自一组历史SRS临床治疗计划的数据用于提取模型的三个关键输入参数——适形指数、梯度指数和一个比例因子,然后将其定义为靶体积的函数。通过将靶近似为处方剂量覆盖的球形体积,建立了测量的肿瘤直径与NTV之间的关系。比例因子(λ)描述了靶外剂量的非线性下降。然后用它来提供所得NTV的一阶近似值。使用线性回归对来自39个与推导过程无关的历史SRS计划的实际NTV值进行回顾性验证该预测模型。该模型针对三维(3D)靶直径和仅轴向二维(2D)靶直径估计值均进行了验证。该预测模型直接将病变直径与NTV体积(立方厘米)相关联,从而与给定剂量分割下的RN风险相关联。基于3D和基于2D的体积估计的预测NTV(立方厘米)在统计学上都能显著预测实际NTV(立方厘米):基于3D的估计R = 0.942(p <.0005),仅基于轴向2D的估计R = 0.911(p =<.0005)。这种基于知识的颅内SRS中NTV预测方法为临床医生提供了一种决策支持工具,以便在治疗计划前适当选择剂量分割。