Sun Lingyue, Burke Ben, Quon Harvey, Swallow Alec, Kirkby Charles, Smith Wendy
Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, Canada.
Division of Medical Physics, Tom Baker Cancer Centre, Calgary, Alberta, Canada.
Adv Radiat Oncol. 2023 Mar 27;8(5):101227. doi: 10.1016/j.adro.2023.101227. eCollection 2023 Sep-Oct.
The objective of this work was to investigate whether including additional dosiomic features can improve biochemical failure-free survival prediction compared with models with clinical features only or with clinical features as well as equivalent uniform dose and tumor control probability.
This retrospective study included 1852 patients who received diagnoses of localized prostate cancer between 2010 and 2016 and were treated with curative external beam radiation therapy in Albert, Canada. A total of 1562 patients from 2 centers were used for developing 3 random survival forest models: Model A included only 5 clinical features; Model B included 5 clinical features, equivalent uniform dose, and tumor control probability; and Model C considered 5 clinical features and 2074 dosiomic features derived from the planned dose distribution of the clinical target volume and planning target volume with further feature selection to determine prognostic features. No feature selection was performed for models A and B. Two hundred ninety patients from another 2 centers were used for independent validation. Individual model-based risk stratification was examined, and the log-rank tests were performed to test statistically significant differences between the risk groups. The 3 models' performances were evaluated using Harrell's concordance index (C-index) and compared using one-way repeated-measures analysis of variance with post hoc paired test.
Model C selected 6 dosiomic features and 4 clinical features to be prognostic. There were statistically significant differences between the 4 risk groups for both training and validation data sets. The C-index for the out-of-bag samples of the training data set was 0.650, 0.648, and 0.669 for models A, B, and C, respectively. The C-index for the validation data set for models A, B, and C was 0.653, 0.648, and 0.662, respectively. Although gains were modest, Model C was statistically significantly better than models A and B.
Dosiomics contain information beyond common dose-volume histogram metrics from planned dose distributions. Incorporation of prognostic dosiomic features in biochemical failure-free survival outcome models can lead to statistically significant although modest improvement in performance.
本研究的目的是探讨与仅包含临床特征的模型或包含临床特征以及等效均匀剂量和肿瘤控制概率的模型相比，纳入额外的剂量学特征是否能改善无生化复发生存预测。
这项回顾性研究纳入了1852例在2010年至2016年间被诊断为局限性前列腺癌并在加拿大艾伯塔省接受根治性外照射放疗的患者。来自2个中心的1562例患者用于开发3个随机生存森林模型：模型A仅包含5个临床特征；模型B包含5个临床特征、等效均匀剂量和肿瘤控制概率；模型C考虑5个临床特征和从临床靶区和计划靶区的计划剂量分布中提取的2074个剂量学特征，并通过进一步的特征选择来确定预后特征。模型A和B未进行特征选择。来自另外2个中心的290例患者用于独立验证。检查了基于个体模型的风险分层，并进行对数秩检验以检验风险组之间的统计学显著差异。使用Harrell一致性指数（C指数）评估这3个模型的性能，并使用单向重复测量方差分析和事后配对检验进行比较。
模型C选择了6个剂量学特征和4个临床特征作为预后特征。训练数据集和验证数据集的4个风险组之间均存在统计学显著差异。训练数据集的袋外样本的C指数，模型A、B和C分别为0.650、0.648和0.669。模型A、B和C的验证数据集的C指数分别为0.653、0.648和0.662。尽管改善幅度不大，但模型C在统计学上显著优于模型A和B。
剂量学包含来自计划剂量分布的常见剂量体积直方图指标之外的信息。在无生化复发生存结局模型中纳入预后剂量学特征可导致性能有统计学显著但适度的改善。
