Jiao Zhicheng, Li Hongming, Xiao Ying, Aggarwal Charu, Galperin-Aizenberg Maya, Pryma Daniel, Simone Charles B, Feigenberg Steven J, Kao Gary D, Fan Yong
Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
Int J Radiat Oncol Biol Phys. 2021 Apr 1;109(5):1647-1656. doi: 10.1016/j.ijrobp.2020.12.014. Epub 2021 Jan 19.
To predict overall survival of patients receiving stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer (ES-NSCLC), we developed a radiomic model that integrates risk of death estimates and changes based on pre- and posttreatment computed tomography (CT) scans. We hypothesize this innovation will improve our ability to stratify patients into various oncologic outcomes with greater accuracy.
Two cohorts of patients with ES-NSCLC uniformly treated with SBRT (a median dose of 50 Gy in 4-5 fractions) were studied. Prediction models were built on a discovery cohort of 100 patients with treatment planning CT scans, and then were applied to a separate validation cohort of 60 patients with pre- and posttreatment CT scans for evaluating their performance.
Prediction models achieved a c-index up to 0.734 in predicting survival outcomes of the validation cohort. The integration of the pretreatment risk of survival measures (risk-high vs risk-low) and changes (risk-increase vs risk-decrease) in risk of survival measures between the pretreatment and posttreatment scans further stratified the patients into 4 subgroups (risk: high, increase; risk: high, decrease; risk: low, increase; risk: low, decrease) with significant difference (χ = 18.549, P = .0003, log-rank test). There was also a significant difference between the risk-increase and risk-decrease groups (χ = 6.80, P = .0091, log-rank test). In addition, a significant difference (χ = 7.493, P = .0062, log-rank test) was observed between the risk-high and risk-low groups obtained based on the pretreatment risk of survival measures.
The integration of risk of survival measures estimated from pre- and posttreatment CT scans can help differentiate patients with good expected survival from those who will do more poorly following SBRT. The analysis of these radiomics-based longitudinal risk measures may help identify patients with early-stage NSCLC who will benefit from adjuvant treatment after lung SBRT, such as immunotherapy.
为预测接受立体定向体部放射治疗（SBRT）的早期非小细胞肺癌（ES-NSCLC）患者的总生存期，我们开发了一种整合死亡风险估计值及基于治疗前和治疗后计算机断层扫描（CT）图像变化的放射组学模型。我们假设这一创新将提高我们更准确地将患者分层为不同肿瘤学结局的能力。
研究了两组均接受SBRT（中位剂量50 Gy，分4-5次）治疗的ES-NSCLC患者。预测模型基于100例有治疗计划CT图像的患者组成的发现队列构建，然后应用于另一组由60例有治疗前和治疗后CT图像的患者组成的验证队列，以评估其性能。
预测模型在预测验证队列的生存结局时，c指数高达0.734。整合治疗前生存测量风险（高风险与低风险）以及治疗前和治疗后扫描之间生存测量风险的变化（风险增加与风险降低），进一步将患者分为4个亚组（风险：高，增加；风险：高，降低；风险：低，增加；风险：低，降低），差异有统计学意义（χ = 18.549，P = .0003，对数秩检验）。风险增加组和风险降低组之间也有显著差异（χ = 6.80，P = .0091，对数秩检验）。此外，基于治疗前生存测量风险得出的高风险组和低风险组之间观察到显著差异（χ = 7.493，P = .0062，对数秩检验）。
整合治疗前和治疗后CT扫描估计的生存测量风险，有助于区分预期生存良好的患者与接受SBRT后预后较差的患者。对这些基于放射组学的纵向风险测量进行分析，可能有助于识别早期NSCLC患者中那些在肺部SBRT后将从辅助治疗（如免疫治疗）中获益的患者。
