Objectives Physician’s evaluation of interstitial lung disease (ILD) extension with high-resolution CT (HRCT) has limitations such as lack of objectivity and reproducibility. This study aimed to investigate the utility of computer-based deep-learning analysis using QZIP-ILD® software (DL-QZIP) compared with conventional approaches in CTD-related ILD. Methods Patients with CTD–ILD visiting our Rheumatology Centre between December 2020 and April 2024 were recruited. Quantitative scores, including the percentage of lung involvement in ground-glass opacity (QGG), total fibrotic lesion (QFIB), and overall ILD extension encompassing both QGG and QFIB (QILD), calculated by DL-QZIP, were compared with semiquantitative visual method, employing intraclass correlation coefficients (ICC). We compared the capability of QILD scores to distinguish patients with forced vital capacity (FVC) % <70 in both methods determined by the area under the curve (AUC) by the receiver-operating characteristic curve analysis and DeLong’s test. Results Eighty patients (median age, 66 years; 14 men) were included. Median QGG, QFIB and QILD scores were 3.45%, 2.19% and 5.35% using DL-QZIP, and 3.25%, 4.06% and 8.48% using visual method, respectively. Correlations between DL-QZIP and visual method were 0.75 for QGG, 0.61 for QFIB and 0.75 for QILD. The AUC of QILD scores for FVC% <70 was significantly higher with DL-QZIP (0.833) compared with visual method (0.660) (P < 0.01). Conclusion QZIP-ILD® demonstrates superior capability in distinguishing patients with a radiological scenario correlated to severe physiological impairment, while showing relatively good correlations in quantifying the extent on HRCT compared with conventional method in CTD–ILD.

中文翻译：

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CT 上结缔组织病相关间质性肺病范围评估的新型深度学习分析：初步横断面研究


目的 医生用高分辨率 CT （HRCT） 评估间质性肺病 （ILD） 扩展存在缺乏客观性和可重复性等局限性。本研究旨在探讨使用 QZIP-ILD® 软件 （DL-QZIP） 进行基于计算机的深度学习分析与传统方法在 CTD 相关 ILD 中的效用。方法 招募了 2020 年 12 月至 2024 年 4 月期间访问我们风湿病中心的 CTD-ILD 患者。通过 DL-QZIP 计算的定量评分，包括毛玻璃样混浊 （QGG） 、总纤维化病变 （QFIB） 和包括 QGG 和 QFIB （QILD） 的总体 ILD 扩展的肺部受累百分比，与采用类内相关系数 （ICC） 的半定量视觉方法进行比较。我们比较了 QILD 评分区分两种方法中用力肺活量 （FVC） % <70 患者的能力，该方法由受试者工作特征曲线分析和 DeLong 检验的曲线下面积 （AUC） 确定。结果 共纳入 80 例患者 （中位年龄 66 岁;14 例男性）。DL-QZIP 法的 QGG 、 QFIB 和 QILD 评分中位数分别为 3.45% 、 2.19% 和 5.35%，视觉法 的中位数 QGG 、 QFIB 和 8.48% 评分分别为 3.25% 、 4.06% 和 8.48%。DL-QZIP 与视觉方法的相关性为 QGG 为 0.75，QFIB 为 0.61，QILD 为 0.75。DL-QZIP 组 （0.833） 的 FVC% <70 QILD 评分的 AUC 显著高于视觉法 （0.660） （P < 0.01）。结论 QZIP-ILD® 在区分与严重生理损害相关的放射学情况患者方面表现出卓越的能力，同时与 CTD-ILD 中的常规方法相比，在量化 HRCT 范围方面显示出相对较好的相关性。