Department of Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan.
Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Aichi, Japan.
Acta Radiol. 2022 Oct;63(10):1363-1373. doi: 10.1177/02841851211044973. Epub 2021 Oct 12.
The need for quantitative assessment of interstitial lung involvement on thin-section computed tomography (CT) has arisen in interstitial lung diseases including connective tissue disease (CTD).
To evaluate the capability of machine learning (ML)-based CT texture analysis for disease severity and treatment response assessments in comparison with qualitatively assessed thin-section CT for patients with CTD.
A total of 149 patients with CTD-related ILD (CTD-ILD) underwent initial and follow-up CT scans (total 364 paired serial CT examinations), pulmonary function tests, and serum KL-6 level tests. Based on all follow-up examination results, all paired serial CT examinations were assessed as "Stable" (n = 188), "Worse" (n = 98) and "Improved" (n = 78). Next, quantitative index changes were determined by software, and qualitative disease severity scores were assessed by consensus of two radiologists. To evaluate differences in each quantitative index as well as in disease severity score between paired serial CT examinations, Tukey's honestly significant difference (HSD) test was performed among the three statuses. Stepwise regression analyses were performed to determine changes in each pulmonary functional parameter and all quantitative indexes between paired serial CT scans.
Δ% normal lung, Δ% consolidation, Δ% ground glass opacity, Δ% reticulation, and Δdisease severity score showed significant differences among the three statuses ( < 0.05). All differences in pulmonary functional parameters were significantly affected by Δ% normal lung, Δ% reticulation, and Δ% honeycomb (0.16 ≤r ≤0.42; < 0.05).
ML-based CT texture analysis has better potential than qualitatively assessed thin-section CT for disease severity assessment and treatment response evaluation for CTD-ILD.
在包括结缔组织疾病（CTD）在内的间质性肺疾病中，对薄层层析 CT（CT）中肺间质受累的定量评估的需求已经出现。
评估基于机器学习（ML）的 CT 纹理分析在疾病严重程度和治疗反应评估方面的能力，并与 CTD 患者的定性评估薄层层析 CT 进行比较。
共 149 例 CTD 相关间质性肺病（CTD-ILD）患者接受了初始和随访 CT 扫描（共 364 对连续 CT 检查）、肺功能测试和血清 KL-6 水平测试。根据所有随访检查结果，所有配对的连续 CT 检查均被评估为“稳定”（n=188）、“恶化”（n=98）和“改善”（n=78）。接下来，通过软件确定定量指标的变化，并由两位放射科医生共识评估定性疾病严重程度评分。为了评估配对连续 CT 检查之间每个定量指标和疾病严重程度评分的差异，对三种状态之间进行了 Tukey 的诚实显著差异（HSD）检验。进行逐步回归分析，以确定配对连续 CT 扫描之间每个肺功能参数和所有定量指标的变化。
在三种状态之间，%正常肺、%实变、%磨玻璃影、%网状影和疾病严重程度评分的Δ值存在显著差异（<0.05）。所有肺功能参数的差异均受%正常肺、%网状影和%蜂窝影的显著影响（0.16≤r≤0.42；<0.05）。
基于 ML 的 CT 纹理分析在疾病严重程度评估和 CTD-ILD 治疗反应评估方面比定性评估薄层层析 CT 具有更好的潜力。
