Artificial Intelligence-based Quantification of Pleural Plaque Volume and Association With Lung Function in Asbestos-exposed Patients.

IF 2 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Journal of Thoracic Imaging Pub Date : 2024-05-01 Epub Date: 2023-11-01 DOI:10.1097/RTI.0000000000000759
Kevin B W Groot Lipman, Thierry N Boellaard, Cornedine J de Gooijer, Nino Bogveradze, Eun Kyoung Hong, Federica Landolfi, Francesca Castagnoli, Nargiza Vakhidova, Illaa Smesseim, Ferdi van der Heijden, Regina G H Beets-Tan, Rianne Wittenberg, Zuhir Bodalal, Jacobus A Burgers, Stefano Trebeschi
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引用次数: 0

Abstract

Purpose: Pleural plaques (PPs) are morphologic manifestations of long-term asbestos exposure. The relationship between PP and lung function is not well understood, whereas the time-consuming nature of PP delineation to obtain volume impedes research. To automate the laborious task of delineation, we aimed to develop automatic artificial intelligence (AI)-driven segmentation of PP. Moreover, we aimed to explore the relationship between pleural plaque volume (PPV) and pulmonary function tests.

Materials and methods: Radiologists manually delineated PPs retrospectively in computed tomography (CT) images of patients with occupational exposure to asbestos (May 2014 to November 2019). We trained an AI model with a no-new-UNet architecture. The Dice Similarity Coefficient quantified the overlap between AI and radiologists. The Spearman correlation coefficient ( r ) was used for the correlation between PPV and pulmonary function test metrics. When recorded, these were vital capacity (VC), forced vital capacity (FVC), and diffusing capacity for carbon monoxide (DLCO).

Results: We trained the AI system on 422 CT scans in 5 folds, each time with a different fold (n = 84 to 85) as a test set. On these independent test sets combined, the correlation between the predicted volumes and the ground truth was r = 0.90, and the median overlap was 0.71 Dice Similarity Coefficient. We found weak to moderate correlations with PPV for VC (n = 80, r = -0.40) and FVC (n = 82, r = -0.38), but no correlation for DLCO (n = 84, r = -0.09). When the cohort was split on the median PPV, we observed statistically significantly lower VC ( P = 0.001) and FVC ( P = 0.04) values for the higher PPV patients, but not for DLCO ( P = 0.19).

Conclusion: We successfully developed an AI algorithm to automatically segment PP in CT images to enable fast volume extraction. Moreover, we have observed that PPV is associated with loss in VC and FVC.

石棉暴露患者胸膜菌斑体积的人工智能量化及其与肺功能的关系。
目的:胸膜斑块是长期接触石棉的形态学表现。PP和肺功能之间的关系尚不清楚,而PP描绘以获得体积的耗时性阻碍了研究。为了自动化费力的描绘任务,我们旨在开发人工智能(AI)驱动的PP自动分割。此外,我们还旨在探索胸膜斑块体积(PPV)与肺功能测试之间的关系。材料和方法:放射科医生在职业性接触石棉患者的计算机断层扫描(CT)图像中回顾性地手动描绘PP(2014年5月至2019年11月)。我们训练了一个没有新的UNet架构的人工智能模型。骰子相似系数量化了人工智能和放射科医生之间的重叠。Spearman相关系数(r)用于PPV和肺功能测试指标之间的相关性。记录时,这些是肺活量(VC)、强迫肺活量和一氧化碳扩散能力(DLCO)。结果:我们对AI系统进行了5次422次CT扫描的训练,每次扫描都有不同的倍数(n=84至85)作为测试集。在这些独立测试集的组合中,预测体积与地面实况之间的相关性为r=0.90,中值重叠为0.71骰子相似系数。我们发现VC(n=80,r=-0.40)和FVC(n=82,r=-0.38)与PPV呈弱至中度相关性,但DLCO(n=84,r=-0.09)无相关性。当按PPV中位数划分队列时,我们观察到PPV较高患者的VC(P=0.001)和FVC(P=0.04)值在统计学上显著较低,结论:我们成功地开发了一种AI算法来自动分割CT图像中的PP,实现了快速的体积提取。此外,我们观察到PPV与VC和FVC的损失有关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Thoracic Imaging
Journal of Thoracic Imaging 医学-核医学
CiteScore
7.10
自引率
9.10%
发文量
87
审稿时长
6-12 weeks
期刊介绍: Journal of Thoracic Imaging (JTI) provides authoritative information on all aspects of the use of imaging techniques in the diagnosis of cardiac and pulmonary diseases. Original articles and analytical reviews published in this timely journal provide the very latest thinking of leading experts concerning the use of chest radiography, computed tomography, magnetic resonance imaging, positron emission tomography, ultrasound, and all other promising imaging techniques in cardiopulmonary radiology. Official Journal of the Society of Thoracic Radiology: Japanese Society of Thoracic Radiology Korean Society of Thoracic Radiology European Society of Thoracic Imaging.
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