重复性对于确保基于放射组学的预后模型的普遍性和临床实用性至关重要。本研究旨在探讨放射组学特征 (RF) 的重复性及其对预测食管鳞状细胞癌 (ESCC) 局部无复发生存 (LRFS) 和总生存 (OS) 的预后模型的跨机构普遍性的影响接受根治性（化疗）放射治疗 (dCRT)。来自两家医院的 912 名患者分别作为训练集和外部验证集。将图像扰动应用于对比增强计算机断层扫描以生成扰动图像。分别从原始图像和扰动图像中提取来自不同特征类型、箱宽度和滤波器的 6500 个 RF，以通过类内相关系数 (ICC) 评估 RF 可重复性。通过特征选择和多变量Cox比例风险回归模型分别对按中值ICC分组的高重复性和低重复性RF组进行进一步分析，以预测LRFS和OS。一阶统计特征比纹理特征具有更高的可重复性（中值ICC： 0.70 对比 0.42-0.62)。 LoG 的 RF 比小波的 RF 具有更好的重复性（中位 ICC：0.70-0.84 vs 0.14-0.64）。 bin 宽度较小的特征具有较高的重复性（中值 ICC 为 8-128：0.65-0.47）。对于 LRFS 和 OS，基于高重复性和低重复性 RF 的模型的性能在具有相似 C 指数的训练集中保持稳定（LRFS：0.65 vs 0.67，p = 0.958；OS：0.64 vs 0.65，p = 0.651），而外部验证集中基于低重复组的模型性能显着低于基于高重复组的模型（LRFS：0.61 vs 0.67，p = 0.013；OS：0.56 vs 0.63， p = 0.013)。在建模中应用高重复性RF可以保障ESCC预后模型的跨机构普适性。探索不同疾病和不同类型影像学的可重复性RF有利于促进放射组学在临床中的正确使用研究。RF 的重复性影响放射组学模型的普遍性。高重复性的 RF 保证了模型的跨机构通用性。较小的 bin 宽度有助于提高 RF 的可重复性。© 2024。作者。

Repeatability is crucial for ensuring the generalizability and clinical utility of radiomics-based prognostic models. This study aims to investigate the repeatability of radiomic feature (RF) and its impact on the cross-institutional generalizability of the prognostic model for predicting local recurrence-free survival (LRFS) and overall survival (OS) in esophageal squamous cell cancer (ESCC) receiving definitive (chemo) radiotherapy (dCRT).Nine hundred and twelve patients from two hospitals were included as training and external validation sets, respectively. Image perturbations were applied to contrast-enhanced computed tomography to generate perturbed images. Six thousand five hundred ten RFs from different feature types, bin widths, and filters were extracted from the original and perturbed images separately to evaluate RF repeatability by intraclass correlation coefficient (ICC). The high-repeatable and low-repeatable RF groups grouped by the median ICC were further analyzed separately by feature selection and multivariate Cox proportional hazards regression model for predicting LRFS and OS.First-order statistical features were more repeatable than texture features (median ICC: 0.70 vs 0.42-0.62). RFs from LoG had better repeatability than that of wavelet (median ICC: 0.70-0.84 vs 0.14-0.64). Features with smaller bin widths had higher repeatability (median ICC of 8-128: 0.65-0.47). For both LRFS and OS, the performance of the models based on high- and low-repeatable RFs remained stable in the training set with similar C-index (LRFS: 0.65 vs 0.67, p = 0.958; OS: 0.64 vs 0.65, p = 0.651), while the performance of the model based on the low-repeatable group was significantly lower than that based on the high-repeatable group in the external validation set (LRFS: 0.61 vs 0.67, p = 0.013; OS: 0.56 vs 0.63, p = 0.013).Applying high-repeatable RFs in modeling could safeguard the cross-institutional generalizability of the prognostic model in ESCC.The exploration of repeatable RFs in different diseases and different types of imaging is conducive to promoting the proper use of radiomics in clinical research.The repeatability of RFs impacts the generalizability of the radiomic model. The high-repeatable RFs safeguard the cross-institutional generalizability of the model. Smaller bin width helps improve the repeatability of RFs.© 2024. The Author(s).