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机器学习的 CT 肺窗肺气肿评分与气道阻塞相关。

Machine learning slice-wise whole-lung CT emphysema score correlates with airway obstruction.

机构信息

Department of Radiology and Medical Physics, Faculty of Medicine and Health, Örebro University, 701 82, Örebro, Sweden.

Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology School of Technology and Health, Stockholm, Sweden.

出版信息

Eur Radiol. 2024 Jan;34(1):39-49. doi: 10.1007/s00330-023-09985-3. Epub 2023 Aug 8.

Abstract

OBJECTIVES

Quantitative CT imaging is an important emphysema biomarker, especially in smoking cohorts, but does not always correlate to radiologists' visual CT assessments. The objectives were to develop and validate a neural network-based slice-wise whole-lung emphysema score (SWES) for chest CT, to validate SWES on unseen CT data, and to compare SWES with a conventional quantitative CT method.

MATERIALS AND METHODS

Separate cohorts were used for algorithm development and validation. For validation, thin-slice CT stacks from 474 participants in the prospective cross-sectional Swedish CArdioPulmonary bioImage Study (SCAPIS) were included, 395 randomly selected and 79 from an emphysema cohort. Spirometry (FEV1/FVC) and radiologists' visual emphysema scores (sum-visual) obtained at inclusion in SCAPIS were used as reference tests. SWES was compared with a commercially available quantitative emphysema scoring method (LAV950) using Pearson's correlation coefficients and receiver operating characteristics (ROC) analysis.

RESULTS

SWES correlated more strongly with the visual scores than LAV950 (r = 0.78 vs. r = 0.41, p < 0.001). The area under the ROC curve for the prediction of airway obstruction was larger for SWES than for LAV950 (0.76 vs. 0.61, p = 0.007). SWES correlated more strongly with FEV1/FVC than either LAV950 or sum-visual in the full cohort (r =  - 0.69 vs. r =  - 0.49/r =  - 0.64, p < 0.001/p = 0.007), in the emphysema cohort (r =  - 0.77 vs. r =  - 0.69/r =  - 0.65, p = 0.03/p = 0.002), and in the random sample (r =  - 0.39 vs. r =  - 0.26/r =  - 0.25, p = 0.001/p = 0.007).

CONCLUSION

The slice-wise whole-lung emphysema score (SWES) correlates better than LAV950 with radiologists' visual emphysema scores and correlates better with airway obstruction than do LAV950 and radiologists' visual scores.

CLINICAL RELEVANCE STATEMENT

The slice-wise whole-lung emphysema score provides quantitative emphysema information for CT imaging that avoids the disadvantages of threshold-based scores and is correlated more strongly with reference tests than LAV950 and reader visual scores.

KEY POINTS

• A slice-wise whole-lung emphysema score (SWES) was developed to quantify emphysema in chest CT images. • SWES identified visual emphysema and spirometric airflow limitation significantly better than threshold-based score (LAV950). • SWES improved emphysema quantification in CT images, which is especially useful in large-scale research.

摘要

目的

定量 CT 成像(quantitative CT imaging)是一种重要的肺气肿生物标志物,特别是在吸烟队列中,但它并不总是与放射科医生的 CT 视觉评估相关。本研究旨在开发和验证一种基于神经网络的全肺肺气肿评分(slice-wise whole-lung emphysema score,SWES),并将其应用于胸部 CT 数据,验证其在未见数据上的有效性,并与传统的定量 CT 方法进行比较。

材料和方法

本研究使用了来自前瞻性横断面瑞典心肺生物影像学研究(Swedish CArdioPulmonary bioImage Study,SCAPIS)的 474 名参与者的薄层 CT 堆栈数据,包括 395 名随机选择的参与者和 79 名肺气肿队列参与者,用于算法的开发和验证。在 SCAPIS 中,使用肺量计(FEV1/FVC)和放射科医生的视觉肺气肿评分(sum-visual)作为参考测试。通过 Pearson 相关系数和受试者工作特征(receiver operating characteristics,ROC)分析,比较 SWES 与商业上可用的定量肺气肿评分方法(LAV950)的相关性。

结果

SWES 与视觉评分的相关性强于 LAV950(r = 0.78 比 r = 0.41,p < 0.001)。SWES 预测气道阻塞的 ROC 曲线下面积(area under the ROC curve,AUC)大于 LAV950(0.76 比 0.61,p = 0.007)。在全队列中,SWES 与 FEV1/FVC 的相关性强于 LAV950(r = -0.69 比 r = -0.49/r = -0.64,p < 0.001/p = 0.007),在肺气肿队列中(r = -0.77 比 r = -0.69/r = -0.65,p = 0.03/p = 0.002),以及在随机样本中(r = -0.39 比 r = -0.26/r = -0.25,p = 0.001/p = 0.007)。

结论

SWES 与放射科医生的视觉肺气肿评分的相关性优于 LAV950,与气道阻塞的相关性优于 LAV950 和放射科医生的视觉评分。

临床意义

SWES 为 CT 成像提供了定量肺气肿信息,避免了基于阈值评分的缺点,与 LAV950 和读者的视觉评分相比,与参考测试的相关性更强。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/adf4/10791709/19f90207af8a/330_2023_9985_Fig1_HTML.jpg

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