Content-based Image Retrieval by Using Deep Learning for Interstitial Lung Disease Diagnosis with Chest CT
Content-based Image Retrieval by Using Deep Learning for Interstitial Lung Disease Diagnosis with Chest CT
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DOI：
10.1148/radiol.2021204164
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发表时间：
2022-01-01
期刊：
影响因子：
19.7
通讯作者：
Kim, Byeongsoo
中科院分区：
文献类型：
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作者：
Choe, Jooae;Hwang, Hye Jeon;Kim, Byeongsoo
背景资料：CT评估间质性肺疾病（ILD）是一项具有挑战性的任务，需要经验，并且受阅片者间差异的影响。目的：研究通过使用深度学习对相似胸部CT图像进行基于内容的图像检索（CBIR）是否有助于不同经验水平的阅片者诊断ILD。材料和方法：这项回顾性研究纳入了2000年1月至2015年12月期间经多学科讨论和可用CT图像确定的确诊ILD患者。数据库由四种疾病类别组成：普通型间质性肺炎（UIP）、非特异性间质性肺炎（NSIP）、隐源性机化性肺炎和慢性过敏性肺炎。从数据库中选择80例患者作为查询。提出的CBIR通过比较由深度学习算法量化的不同区域疾病模式的程度和分布，从数据库中检索具有诊断的前三个相似CT图像。8名经验不同的阅片者在应用CBIR之前和之后，在间隔2周的两次阅读会议上解释了查询CT图像，并提供了他们最可能的诊断。诊断准确性进行了分析，采用McNemar检验和广义估计方程，并interreader协议进行了分析，使用Fleiss k。结果：共288例患者（平均年龄，58岁，6 - 11 [标准差]; 145名妇女）。应用CBIR后，所有阅片者的总体诊断准确率均有所提高（CBIR前，46.1% [95%CI：37.1，55.3]; CBIR后，60.9% [95%CI：51.8，69.3]; P <0.001）。在疾病类别方面，在UIP（CBIR前后，分别为52.4%和72.8%; P <0.001）和NSIP病例（CBIR前后，分别为42.9%和61.6%; P <0.001）中应用CBIR后，诊断准确性有所提高。CBIR后阅片者间一致性提高（CBIR前与CBIR后Fleiss k分别为0.32与0.47; P =.005）结论：所提出的基于内容的胸部CT图像检索系统结合深度学习提高了间质性肺病的诊断准确性，并提高了不同经验水平阅片者间的一致性。(C)RSNA，2021年。
Background: Evaluation of interstitial lung disease (ILD) at CT is a challenging task that requires experience and is subject to substantial interreader variability.Purpose: To investigate whether a proposed content-based image retrieval (CBIR) of similar chest CT images by using deep learning can aid in the diagnosis of ILD by readers with different levels of experience.Materials and Methods: This retrospective study included patients with confirmed ILD after multidisciplinary discussion and available CT images identified between January 2000 and December 2015. Database was composed of four disease classes: usual interstitial pneumonia (UIP), nonspecific interstitial pneumonia (NSIP), cryptogenic organizing pneumonia, and chronic hypersensitivity pneumonitis. Eighty patients were selected as queries from the database. The proposed CBIR retrieved the top three similar CT images with diagnosis from the database by comparing the extent and distribution of different regional disease patterns quantified by a deep learning algorithm. Eight readers with varying experience interpreted the query CT images and provided their most probable diagnosis in two reading sessions 2 weeks apart, before and after applying CBIR. Diagnostic accuracy was analyzed by using McNemar test and generalized estimating equation, and interreader agreement was analyzed by using Fleiss k.Results: A total of 288 patients were included (mean age, 58 years 6 11 [standard deviation]; 145 women). After applying CBIR, the overall diagnostic accuracy improved in all readers (before CBIR, 46.1% [95% CI: 37.1, 55.3]; after CBIR, 60.9% [95% CI: 51.8, 69.3]; P < .001). In terms of disease category, the diagnostic accuracy improved after applying CBIR in UIP (before vs after CBIR, 52.4% vs 72.8%, respectively; P < .001) and NSIP cases (before vs after CBIR, 42.9% vs 61.6%, respectively; P < .001). Interreader agreement improved after CBIR (before vs after CBIR Fleiss k, 0.32 vs 0.47, respectively; P =.005).Conclusion: The proposed content-based image retrieval system for chest CT images with deep learning improved the diagnostic accuracy of interstitial lung disease and interreader agreement in readers with different levels of experience. (C) RSNA, 2021.