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基于深度学习的自动肺分析对间质性肺疾病量化的影响:与肺功能测试结果及预后的相关性

Effects of Automatic Deep-Learning-Based Lung Analysis on Quantification of Interstitial Lung Disease: Correlation with Pulmonary Function Test Results and Prognosis.

作者信息

Aoki Ryo, Iwasawa Tae, Saka Tomoki, Yamashiro Tsuneo, Utsunomiya Daisuke, Misumi Toshihiro, Baba Tomohisa, Ogura Takashi

机构信息

Diagnostic Radiology, Yokohama City University Graduate School of Medicine, 3-9 Fukuura, Kanazawa-ku, Yokohama 236-0004, Kanagawa, Japan.

Department of Radiology, Kanagawa Cardiovascular and Respiratory Center, 6-16-1 Tomioka-higashi, Kanazawa-ku, Yokohama 236-0051, Kanagawa, Japan.

出版信息

Diagnostics (Basel). 2022 Dec 4;12(12):3038. doi: 10.3390/diagnostics12123038.

DOI:10.3390/diagnostics12123038
PMID:36553045
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9777463/
Abstract

We investigated the feasibility of a new deep-learning (DL)-based lung analysis method for the evaluation of interstitial lung disease (ILD) by comparing it with evaluation using the traditional computer-aided diagnosis (CAD) system and patients’ clinical outcomes. We prospectively included 104 patients (84 with and 20 without ILD). An expert radiologist defined regions of interest in the typical areas of normal, ground-glass opacity, consolidation, consolidation with fibrosis (traction bronchiectasis), honeycombing, reticulation, traction bronchiectasis, and emphysema, and compared them with the CAD and DL-based analysis results. Next, we measured the extent of ILD lesions with the CAD and DL-based analysis and compared them. Finally, we compared the lesion extent on computed tomography (CT) images, as measured with the DL-based analysis, with pulmonary function tests results and patients’ overall survival. Pearson’s correlation analysis revealed a significant correlation between DL-based analysis and CAD results. Forced vital capacity was significantly correlated with DL-based analysis (r = 0.789, p < 0.001 for normal lung volume and r = −0.316, p = 0.001 for consolidation with fibrosis volume). Consolidation with fibrosis measured using DL-based analysis was independently associated with poor survival. The lesion extent measured using DL-based analysis showed a negative correlation with the pulmonary function test results and prognosis.

摘要

我们通过将一种基于深度学习(DL)的新型肺部分析方法与传统计算机辅助诊断(CAD)系统的评估以及患者的临床结果进行比较,来研究其用于评估间质性肺疾病(ILD)的可行性。我们前瞻性纳入了104例患者(84例患有ILD,20例未患ILD)。一名专家放射科医生在正常、磨玻璃影、实变、伴有纤维化的实变(牵拉性支气管扩张)、蜂窝状改变、网状改变、牵拉性支气管扩张和肺气肿的典型区域定义感兴趣区,并将其与CAD和基于DL的分析结果进行比较。接下来,我们用基于CAD和DL的分析测量ILD病变范围并进行比较。最后,我们将基于DL分析测量的计算机断层扫描(CT)图像上的病变范围与肺功能测试结果和患者的总生存期进行比较。Pearson相关性分析显示基于DL的分析与CAD结果之间存在显著相关性。用力肺活量与基于DL的分析显著相关(正常肺容积时r = 0.789,p < 0.001;伴有纤维化的实变容积时r = -0.316,p = 0.001)。基于DL分析测量的伴有纤维化的实变与较差的生存率独立相关。基于DL分析测量的病变范围与肺功能测试结果和预后呈负相关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1480/9777463/eb7d406538be/diagnostics-12-03038-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1480/9777463/086778c3f23b/diagnostics-12-03038-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1480/9777463/6f2a9975a93f/diagnostics-12-03038-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1480/9777463/42f1f2f80388/diagnostics-12-03038-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1480/9777463/46fbe3f3ffbf/diagnostics-12-03038-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1480/9777463/f4be8887879e/diagnostics-12-03038-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1480/9777463/eb7d406538be/diagnostics-12-03038-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1480/9777463/086778c3f23b/diagnostics-12-03038-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1480/9777463/6f2a9975a93f/diagnostics-12-03038-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1480/9777463/42f1f2f80388/diagnostics-12-03038-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1480/9777463/46fbe3f3ffbf/diagnostics-12-03038-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1480/9777463/f4be8887879e/diagnostics-12-03038-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1480/9777463/eb7d406538be/diagnostics-12-03038-g006.jpg

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