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首页> 外文会议>SPIE Medical Imaging Conference >Radiomic texture-curvature (RTC) features for precision medicine of patients with rheumatoid arthritis-associated interstitial lung disease
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Radiomic texture-curvature (RTC) features for precision medicine of patients with rheumatoid arthritis-associated interstitial lung disease

机译：放射线纹理曲率（RTC）功能可用于类风湿关节炎相关性间质性肺病患者的精准医学

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We investigated the effect of radiomic texture-curvature (RTC) features of lung CT images in the prediction of the overall survival of patients with rheumatoid arthritis-associated interstitial lung disease (RA-ILD). We retrospectively collected 70 RA-ILD patients who underwent thin-section lung CT and serial pulmonary function tests. After the extraction of the lung region, we computed hyper-curvature features that included the principal curvatures, curvedness, bright/dark sheets, cylinders, blobs, and curvature scales for the bronchi and the aerated lungs. We also computed gray-level co-occurrence matrix (GLCM) texture features on the regions-of-interest corresponding to the five ILD patterns (consolidated, ground-glass opacity, reticular, or honeycombing) and normal regions. An elastic-net penalty method was used to select and combine these features with a Cox proportional hazards model for predicting the survival of the patient. Evaluation was performed by use of concordance index (C-index) as a measure of prediction performance. The C-index values of the texture features, hyper-curvature features, and the combination thereof (RTC features) in predicting patient survival was estimated by use of bootstrapping with 2,000 replications, and they were compared with an established clinical prognostic biomarker known as the gender, age, and physiology (GAP) index by means of two-sided t-test. Bootstrap evaluation yielded the following C-index values for the clinical and radiomic features: (a) GAP index: 78.3%; (b) GLCM texture features: 79.6%; (c) hyper-curvature features: 80.8%; and (d) RTC features: 86.8%. The RTC features significantly outperformed any of the other predictors (P < 0.001). The Kaplan-Meier survival curves of patients stratified to low- and high-risk groups based on the RTC features showed a statistically significant (P < 0.0001) difference. Thus, the RTC features can provide an effective imaging biomarker for predicting the overall survival of patients with RA-ILD.
机译：我们调查了肺部CT图像的放射纹理曲率（RTC）功能在预测类风湿关节炎相关性间质性肺病（RA-ILD）患者的总体生存中的作用。我们回顾性收集了70例行薄层肺部CT和系列肺功能检查的RA-ILD患者。提取肺区域后，我们计算了超曲率特征，包括支气管和充气肺的主要曲率，弯曲度，明/暗片，圆柱体，斑点和曲率标度。我们还计算了与五个ILD模式（合并的，毛玻璃不透明的，网状的或蜂窝状的）和正常区域相对应的感兴趣区域上的灰度共现矩阵（GLCM）纹理特征。弹性网罚法用于选择这些特征并将其与Cox比例风险模型相结合，以预测患者的生存情况。通过使用一致性指数（C指数）作为预测绩效的衡量标准进行评估。通过使用具有2,000次重复的自举法来估计质地特征，超弯曲特征及其组合（RTC特征）在预测患者存活率方面的C-index值，并将其与已建立的临床预后生物标记物（即性别，年龄和生理（GAP）指数通过双面t检验进行。通过Bootstrap评估得出的临床和放射学特征的C指数如下：（a）GAP指数：78.3％; （b）GLCM纹理特征：79.6％; （三）超曲率特征：80.8％; （d）RTC功能：86.8％。 RTC的功能明显优于其他任何预测指标（P <0.001）。根据RTC特征将患者分为低危和高危组的Kaplan-Meier生存曲线显示出统计学上的显着差异（P <0.0001）。因此，RTC特征可以提供有效的成像生物标志物，以预测RA-ILD患者的整体生存。

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