Cheeloo College of Medicine, Shandong University, Jinan, China; Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Int J Radiat Oncol Biol Phys. 2021 Oct 1;111(2):443-455. doi: 10.1016/j.ijrobp.2021.04.047. Epub 2021 May 8.
Our purpose was to construct a computed tomography (CT)-based delta-radiomics nomogram and corresponding risk classification system for individualized and accurate estimation of severe acute radiation pneumonitis (SARP) in patients with esophageal cancer (EC) after radiation therapy.
Four hundred patients with EC were enrolled from 2 independent institutions and were divided into the training (n = 200) and validation (n = 200) cohorts. Eight hundred fifty radiomics features of lung were extracted from treatment planning images, including the positioning CT before radiation therapy (CT) and the resetting CT after receiving 40 to 45 Gy (CT). The longitudinal net changes in radiomics features from CT to CT were calculated and defined as delta-radiomics features. Least absolute shrinkage and selection operator algorithm was performed to features selection and delta-radiomics signature building. Integrating the signature with multidimensional clinicopathologic, dosimetric, and hematological predictors of SARP, a novel CT-based delta-radiomics nomogram was established according to multivariate analysis. The clinical application values of nomogram were both evaluated in the training and validation cohorts by concordance index, calibration curves, and decision curve analysis. Recursive partitioning analysis was used to generate a risk classification system.
The delta-radiomics signature consisting of 24 features was significantly associated with SARP status (P < .001). Incorporating it with other high-risk factors, Subjective Global Assessment score, pulmonary fibrosis score, mean lung dose, and systemic immune inflammation index, the developed delta-radiomics nomogram showed increased improvement in SARP discrimination accuracy with concordance index of 0.975 and 0.921 in the training and validation cohorts, respectively. Calibration curves and decision curve analysis confirmed the satisfactory clinical feasibility and utility of nomogram. The risk classification system displayed excellent performance on identifying SARP occurrence (P < .001).
The delta-radiomics nomogram and risk classification system as low-cost and noninvasive means exhibited superior predictive accuracy and provided individualized probability of SARP stratification for patients with EC.
我们旨在构建一种基于计算机断层扫描（CT）的 delta 放射组学列线图和相应的风险分类系统，用于个体化、准确地预测接受放射治疗后的食管癌（EC）患者发生严重急性放射性肺炎（SARP）的风险。
从 2 个独立机构共招募了 400 名 EC 患者，将其分为训练队列（n=200）和验证队列（n=200）。从治疗计划图像中提取了 855 个肺放射组学特征，包括放射治疗前的定位 CT（CT）和接受 40 至 45 Gy 后重置的 CT（CT）。计算从 CT 到 CT 的放射组学特征的纵向净变化，并将其定义为 delta 放射组学特征。使用最小绝对收缩和选择算子算法进行特征选择和 delta 放射组学特征构建。将特征与 SARP 的多维临床病理、剂量学和血液学预测因子相结合，根据多变量分析建立基于 CT 的 delta 放射组学列线图。通过一致性指数、校准曲线和决策曲线分析在训练和验证队列中评估列线图的临床应用价值。使用递归分区分析生成风险分类系统。
由 24 个特征组成的 delta 放射组学特征与 SARP 状态显著相关（P＜0.001）。将其与其他高危因素（主观整体评估评分、肺纤维化评分、平均肺剂量和全身免疫炎症指数）相结合，所开发的 delta 放射组学列线图在训练和验证队列中的 SARP 鉴别准确性均有所提高，一致性指数分别为 0.975 和 0.921。校准曲线和决策曲线分析证实了列线图具有良好的临床可行性和实用性。风险分类系统在识别 SARP 发生方面表现出优异的性能（P＜0.001）。
作为一种低成本、非侵入性的方法，delta 放射组学列线图和风险分类系统具有较高的预测准确性，为 EC 患者提供了个体化的 SARP 分层概率。
