Department of Radiation Oncology and Image-Applied Therapy, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Department of Advanced Medical Physics, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
J Appl Clin Med Phys. 2024 Oct;25(10):e14475. doi: 10.1002/acm2.14475. Epub 2024 Aug 23.
This study aims to externally validate a predictive model for distant metastasis (DM) with computed tomography (CT)-based radiomics features in prospectively enrolled non-small-cell lung cancer patients undergoing dynamic tumor-tracking stereotactic body radiation therapy (DTT-SBRT).
The study collected retrospective data from 567 patients across 11 institutions as the training dataset and prospectively enrolled 42 patients from four institutions as the external test dataset. Four clinical features were collected, and 944 CT-based radiomic features were extracted from gross tumor volumes. After standardization and feature selection, DM predictive models were developed using fine and gray regression (FG) and random survival forest (RSF), incorporating clinical and radiomic features, and their combinations within the training dataset. Then, the model was applied to the test dataset, dividing patients into high- and low-risk groups based on medians of risk scores. Model performance was assessed using the concordance index (C-index), and the statistical significance between groups was evaluated using Gray's test.
In the training dataset, 122 of 567 patients (21.5%) developed DM, compared to 9 of 42 patients (21.4%) in the test dataset. In the test dataset, the C-indices of the clinical, radiomics, and hybrid models with FG were 0.559, 0.544, and 0.560, respectively, whereas those with RSF were 0.576, 0.604, and 0.627, respectively. The hybrid model with RSF, which exhibited the best predictive performance of all models, identified 7 of 23 patients (30.4%) as high risk and 2 of 19 patients (10.5%) as low risk for DM incidence in the test dataset (p = 0.116).
Although predictive models for DM lack significance when applied to prospectively enrolled cases undergoing DTT-lung SBRT, the model with RSF exhibits a consistent capacity to effectively classify patients at a high risk of developing DM.
本研究旨在通过前瞻性纳入接受动态肿瘤追踪立体定向体放射治疗（DTT-SBRT）的非小细胞肺癌患者的 CT 基于放射组学特征，对外科治疗远处转移（DM）的预测模型进行验证。
本研究从 11 个机构中收集了 567 例患者的回顾性数据作为训练数据集，并从 4 个机构前瞻性纳入了 42 例患者作为外部测试数据集。收集了 4 个临床特征，并从大体肿瘤体积中提取了 944 个 CT 基于放射组学特征。在标准化和特征选择后，使用精细和灰色回归（FG）和随机生存森林（RSF）在训练数据集中开发了包含临床和放射组学特征及其组合的 DM 预测模型。然后，将模型应用于测试数据集，根据风险评分中位数将患者分为高风险和低风险组。使用一致性指数（C-index）评估模型性能，并使用 Gray 检验评估组间的统计学意义。
在训练数据集中，567 例患者中有 122 例（21.5%）发生 DM，而在测试数据集中，42 例患者中有 9 例（21.4%）发生 DM。在测试数据集中，FG 的临床、放射组学和混合模型的 C-index 分别为 0.559、0.544 和 0.560，而 RSF 的 C-index 分别为 0.576、0.604 和 0.627。在所有模型中，具有最佳预测性能的 RSF 混合模型在测试数据集中确定了 23 例患者中的 7 例（30.4%）为 DM 高风险，19 例患者中的 2 例（10.5%）为 DM 低风险（p=0.116）。
尽管 DM 的预测模型在应用于接受 DTT-肺部 SBRT 的前瞻性纳入病例时缺乏意义，但具有 RSF 的模型仍能有效分类发生 DM 高风险的患者。
