MVR Cancer Centre and Research Institute, Calicut, India.
MVR Cancer Centre and Research Institute, Calicut, India.
Med Dosim. 2021;46(3):283-288. doi: 10.1016/j.meddos.2021.02.003. Epub 2021 Mar 18.
Parotids are considered one of the major organs at risk in Head and Neck (HN) intensity-modulated radiotherapy (IMRT). Achieving proper target coverage with reduced mean parotid dose demands an elaborate time-consuming IMRT plan optimization. A parotid mean dose prediction model based on a machine-learning linear regression was developed and validated in this study. The model was developed using independent variables, such as parotid to PTV overlapping volume, dose coverage of the overlapping PTV, the ratio of overlapping parotid volume to total parotid volume, and volume of parotid overlapping with isotopically expanded PTV contours. The Pearson correlation coefficients between these independent variables and the mean parotid dose were calculated. Multicollinearity of the independent variables was checked by calculating the Variance Inflation Factor (VIF). All variables are having VIF less than ten were taken for the model. Fifty IMRT patient plans were used to develop the model. The mean parotid dose predicted by the model was in good agreement with the obtained mean parotid dose. The model is having a Root Mean Square Error (RMSE) of 2.89 Gy and an R-square of 0.7695. The model was successfully validated using the fivefold cross-validation method, resulting R-square value of 0.6179 and an RMSE of 2.93 Gy. The normality of the model's residuals was tested using Quartile-Quartile (Q-Q) plot and Shapiro Wilk test (p = 0.996, for null hypothesis ``residuals were normally distributed''). The data points in the Q-Q plot are falling approximately along the reference line. This model can be used in clinics to help the planner in the preplanning phase for efficient plan optimization.
腮腺被认为是头颈部（HN）调强放疗（IMRT）中主要的风险器官之一。为了实现适当的靶区覆盖并降低平均腮腺剂量，需要进行精心耗时的调强放疗计划优化。本研究中开发并验证了一种基于机器学习线性回归的腮腺平均剂量预测模型。该模型使用独立变量（如腮腺与 PTV 重叠体积、重叠 PTV 的剂量覆盖、重叠腮腺体积与总腮腺体积的比值以及与同位素扩展 PTV 轮廓重叠的腮腺体积）来开发。计算了这些独立变量与腮腺平均剂量之间的 Pearson 相关系数。通过计算方差膨胀因子（VIF）检查了独立变量的多重共线性。所有 VIF 小于 10 的变量都被纳入模型。使用 50 例调强放疗患者计划来开发模型。模型预测的腮腺平均剂量与实测腮腺平均剂量吻合良好。该模型的均方根误差（RMSE）为 2.89Gy，R 平方值为 0.7695。使用五重交叉验证法成功验证了该模型，得到的 R 平方值为 0.6179，RMSE 为 2.93Gy。通过四分位距-四分位距（Q-Q）图和 Shapiro-Wilk 检验（p=0.996，用于假设“残差呈正态分布”的无效假设）测试模型残差的正态性。Q-Q 图中的数据点大致沿参考线分布。该模型可用于临床，帮助计划者在预计划阶段进行有效的计划优化。
