Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.
Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany.
Eur Radiol Exp. 2024 Oct 14;8(1):114. doi: 10.1186/s41747-024-00519-0.
Non-specific interstitial pneumonia (NSIP) is an interstitial lung disease that can result in end-stage fibrosis. We investigated the influence of body composition and pulmonary fat attenuation volume (CTpfav) on overall survival (OS) in NSIP patients.
In this retrospective single-center study, 71 NSIP patients with a median age of 65 years (interquartile range 21.5), 39 females (55%), who had a computed tomography from August 2009 to February 2018, were included, of whom 38 (54%) died during follow-up. Body composition analysis was performed using an open-source nnU-Net-based framework. Features were combined into: Sarcopenia (muscle/bone); Fat (total adipose tissue/bone); Myosteatosis (inter-/intra-muscular adipose tissue/total adipose tissue); Mediastinal (mediastinal adipose tissue/bone); and Pulmonary fat index (CTpfav/lung volume). Kaplan-Meier analysis with a log-rank test and multivariate Cox regression were used for survival analyses.
Patients with a higher (> median) Sarcopenia and lower (< median) Mediastinal Fat index had a significantly better survival probability (2-year survival rate: 83% versus 71% for high versus low Sarcopenia index, p = 0.023; 83% versus 72% for low versus high Mediastinal fat index, p = 0.006). In univariate analysis, individuals with a higher Pulmonary fat index exhibited significantly worse survival probability (2-year survival rate: 61% versus 94% for high versus low, p = 0.003). Additionally, it was an independent risk predictor for death (hazard ratio 2.37, 95% confidence interval 1.03-5.48, p = 0.043).
Fully automated body composition analysis offers interesting perspectives in patients with NSIP. Pulmonary fat index was an independent predictor of OS.
The Pulmonary fat index is an independent predictor of OS in patients with NSIP and demonstrates the potential of fully automated, deep-learning-driven body composition analysis as a biomarker for prognosis estimation.
This is the first study assessing the potential of CT-based body composition analysis in patients with non-specific interstitial pneumonia (NSIP). A single-center analysis of 71 patients with board-certified diagnosis of NSIP is presented Indices related to muscle, mediastinal fat, and pulmonary fat attenuation volume were significantly associated with survival at univariate analysis. CT pulmonary fat attenuation volume, normalized by lung volume, resulted as an independent predictor for death.
非特异性间质性肺炎（NSIP）是一种间质性肺病，可导致终末期纤维化。我们研究了人体成分和肺脂肪衰减体积（CTpfav）对 NSIP 患者总生存期（OS）的影响。
本回顾性单中心研究纳入了 71 名 NSIP 患者，中位年龄 65 岁（四分位距 21.5），女性 39 名（55%），2009 年 8 月至 2018 年 2 月进行了计算机断层扫描，随访期间 38 名（54%）患者死亡。采用开源 nnU-Net 为基础的框架进行人体成分分析。特征合并为：肌肉减少症（肌肉/骨）；脂肪（总脂肪组织/骨）；肌内脂肪增多症（肌内/肌间脂肪/总脂肪组织）；纵隔（纵隔脂肪组织/骨）；和肺脂肪指数（CTpfav/肺容积）。采用 Kaplan-Meier 分析和对数秩检验及多变量 Cox 回归进行生存分析。
肌肉减少症较高（>中位数）和纵隔脂肪指数较低（<中位数）的患者的生存概率明显较高（2 年生存率：高肌肉减少症指数组为 83%，低肌肉减少症指数组为 71%，p=0.023；低纵隔脂肪指数组为 83%，高纵隔脂肪指数组为 72%，p=0.006）。在单变量分析中，肺脂肪指数较高的患者生存概率明显较低（2 年生存率：高肺脂肪指数组为 61%，低肺脂肪指数组为 94%，p=0.003）。此外，肺脂肪指数是死亡的独立风险预测因子（危险比 2.37，95%置信区间 1.03-5.48，p=0.043）。
完全自动化的人体成分分析为 NSIP 患者提供了有趣的视角。肺脂肪指数是 OS 的独立预测因子。
肺脂肪指数是 NSIP 患者 OS 的独立预测因子，并证明了完全自动化、深度学习驱动的人体成分分析作为预后估计生物标志物的潜力。
这是第一项评估 CT 人体成分分析在非特异性间质性肺炎（NSIP）患者中的应用潜力的研究。本研究为一项中心性分析，纳入了 71 名经认证的 NSIP 患者，对 2009 年 8 月至 2018 年 2 月的 NSIP 患者进行了分析。与生存相关的肌肉、纵隔脂肪和肺脂肪衰减体积指数在单变量分析中具有显著相关性。肺脂肪衰减体积，按肺容积归一化，是死亡的独立预测因子。
