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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：含有类风湿性关节炎相关间质肺病患者精密药物的辐射纹理 - 曲率（RTC）特征

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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CT图像的辐射纹理 - 曲率（RTC）特征的效果在预测类风湿性关节炎相关的间质肺病（RA-ILD）的整体存活中。我们回顾性地收集了70名接受薄膜肺CT和连续肺功能测试的患者。在肺区提取后，我们计算了支气管和充气肺部的主曲率，曲线，明亮/暗片，圆柱，斑点和曲率鳞片的超曲率特征。我们还在对应于五个ILD模式（综合，浇水玻璃不透明度，网状或蜂窝）和正常区域的物息区域上计算灰度的共同发生矩阵（GLCM）纹理特征。使用弹性净惩罚方法选择并将这些特征与COX比例危害模型进行选择并结合，用于预测患者的存活率。通过使用一致性指数（C-INDEX）作为预测性能的量度来进行评估。通过使用2,000次重复使用自血动映射估算了预测患者存活中的纹理特征，超曲率特征和其组合（RTC特征）的C折射率值，并将其与已知为的临床预后生物标志物进行比较通过双面T检验，性别，年龄和生理学（间隙）指数。引导评估产生以下C级指数值，适用于临床和射出物特征：（a）间隙指数：78.3％; （b）GLCM质地特点：79.6％; （c）超曲率特征：80.8％; （d）RTC特征：86.8％。 RTC特征显着优于任何其他预测因子（P <0.001）。基于RTC特征的低和高风险群体分层的Kaplan-Meier存活曲线显示出统计学意义（P <0.0001）差异。因此，RTC特征可以提供有效的成像生物标志物，用于预测RA-ILD患者的整体存活。

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