Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Int J Radiat Oncol Biol Phys. 2018 Nov 15;102(4):1090-1097. doi: 10.1016/j.ijrobp.2017.10.046. Epub 2017 Nov 15.
To develop and validate a radiomics signature that can predict the clinical outcomes for patients with stage I non-small cell lung cancer (NSCLC).
We retrospectively analyzed contrast-enhanced computed tomography images of patients from a training cohort (n = 147) treated with surgery and an independent validation cohort (n = 295) treated with stereotactic ablative radiation therapy. Twelve radiomics features with established strategies for filtering and preprocessing were extracted. The random survival forests (RSF) method was used to build models from subsets of the 12 candidate features based on their survival relevance and generate a mortality risk index for each observation in the training set. An optimal model was selected, and its ability to predict clinical outcomes was evaluated in the validation set using predicted mortality risk indexes.
The optimal RSF model, consisting of 2 predictive features, kurtosis and the gray level co-occurrence matrix feature homogeneity2, allowed for significant risk stratification (log-rank P < .0001) and remained an independent predictor of overall survival after adjusting for age, tumor volume and histologic type, and Karnofsky performance status (hazard ratio [HR] 1.27; P < 2e-16) in the training set. The resultant mortality risk indexes were significantly associated with overall survival in the validation set (log-rank P = .0173; HR 1.02, P = .0438). They were also significant for distant metastasis (log-rank P < .05; HR 1.04, P = .0407) and were borderline significant for regional recurrence on univariate analysis (log-rank P < .05; HR 1.04, P = .0617).
Our radiomics model accurately predicted several clinical outcomes and allowed pretreatment risk stratification in stage I NSCLC, allowing the choice of treatment to be tailored to each patient's individual risk profile.
开发和验证一种可预测 I 期非小细胞肺癌（NSCLC）患者临床结局的放射组学特征。
我们回顾性分析了来自手术治疗的训练队列（n=147）和立体定向消融放疗治疗的独立验证队列（n=295）的增强 CT 图像。提取了 12 个具有过滤和预处理策略的放射组学特征。基于生存相关性，使用随机生存森林（RSF）方法从 12 个候选特征的子集中构建模型，并为训练集中的每个观察生成死亡率风险指数。选择最佳模型，并使用预测死亡率风险指数在验证集中评估其预测临床结局的能力。
由 2 个预测特征组成的最佳 RSF 模型，即峰度和灰度共生矩阵特征同质性 2，允许进行显著的风险分层（对数秩 P＜0.0001），并在调整年龄、肿瘤体积、组织学类型和卡氏功能状态后，仍然是训练集中总生存的独立预测因素（危险比[HR] 1.27；P＜2e-16）。在验证集中，得到的死亡率风险指数与总生存显著相关（对数秩 P=0.0173；HR 1.02，P=0.0438）。它们对于远处转移也是显著的（对数秩 P＜0.05；HR 1.04，P=0.0407），对于局部复发在单因素分析中也是边缘显著的（对数秩 P＜0.05；HR 1.04，P=0.0617）。
我们的放射组学模型准确预测了几种临床结局，并允许对 I 期 NSCLC 进行治疗前风险分层，从而可以根据每个患者的个体风险概况来选择治疗方案。
