Objectives Interstitial lung disease (ILD) is the most common cause of death in patients with systemic sclerosis (SSc), although disease behavior is highly heterogeneous. While a usual interstitial pneumonia (UIP) pattern is associated with worse survival in other ILDs, its significance in SSc-ILD is unclear. We sought to assess the prognostic utility of a deep-learning HRCT algorithm of UIP probability in SSc-ILD. Methods Patients with SSc-ILD were included if HRCT images, concomitant lung function tests, and follow-up data were available. We used the Systematic Objective Fibrotic Imaging analysis Algorithm (SOFIA), a convolution neural network algorithm which provides probabilities of a UIP pattern on HRCT images. These were converted into the Prospective Investigation of Pulmonary Embolism Diagnosis (PIOPED)-based UIP probability categories. Decline in lung function was assessed by mixed-effect model analysis and relationship with survival by Cox proportional hazards analysis. Results 522 patients were included in the study. 19.5% were classified as UIP not in the differential, 53.5% as low probability of UIP, 25.7% as intermediate probability of UIP, and 1.3% as high probability of UIP. A higher likelihood of UIP probability expressed as PIOPED categories was associated with worse baseline FVC, as well as with decline in FVC (p= 0.008), and worse 15-year survival (p= 0.001), both independently of age, gender, ethnicity, smoking history, and baseline FVC or Goh et al. staging system. Conclusion A higher probability of a SOFIA-determined UIP pattern is associated with more advanced ILD, disease progression, and worse survival, suggesting that it may be a useful prognostic marker in SSc-ILD.

中文翻译：

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深度学习 CT 成像算法检测系统性硬化症相关间质性肺病患者的常见间质性肺炎模式：与疾病进展和生存率的相关性


目的 间质性肺病 （ILD） 是系统性硬化症 （SSc） 患者最常见的死亡原因，尽管疾病行为具有高度异质性。虽然常见的间质性肺炎 （UIP） 模式与其他 ILD 的生存率较差相关，但其在 SSc-ILD 中的意义尚不清楚。我们试图评估 UIP 概率的深度学习 HRCT 算法在 SSc-ILD 中的预后效用。方法 如果 HRCT 图像、伴随肺功能检查和随访数据可用，则纳入 SSc-ILD 患者。我们使用了系统客观纤维化成像分析算法 （SOFIA），这是一种卷积神经网络算法，可在 HRCT 图像上提供 UIP 模式的概率。这些被转换为基于肺栓塞诊断前瞻性调查 （PIOPED） 的 UIP 概率类别。通过混合效应模型分析评估肺功能下降，通过 Cox 比例风险分析评估与生存的关系。结果 纳入 522 例患者。19.5% 被归类为不在鉴别中的 UIP，53.5% 为 UIP 的低概率，25.7% 为 UIP 的中等概率，1.3% 为 UIP 的高风险。以 PIOPED 类别表示的 UIP 概率较高的可能性与较差的基线 FVC 以及 FVC 的下降 （p= 0.008） 和较差的 15 年生存率 （p= 0.001） 相关，这与年龄、性别、种族、吸烟史和基线 FVC 或 Goh 等人无关分期系统。结论 SOFIA 确定的 UIP 模式的概率较高与更晚期的 ILD 、疾病进展和较差的生存率相关，表明它可能是 SSc-ILD 的有用预后标志物。