The textures of sarcoidosis: quantifying lung disease through variograms.

IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Physics in medicine and biology Pub Date : 2025-01-13 DOI:10.1088/1361-6560/ada19c
William L Lippitt, Lisa A Maier, Tasha E Fingerlin, David A Lynch, Ruchi Yadav, Jared Rieck, Andrew C Hill, Shu-Yi Liao, Margaret M Mroz, Briana Q Barkes, Kum Ju Chae, Hye Jeon Hwang, Nichole E Carlson
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Abstract

Objective. Sarcoidosis is a granulomatous disease affecting the lungs in over 90% of patients. Qualitative assessment of chest CT by radiologists is standard clinical practice and reliable quantification of disease from CT would support ongoing efforts to identify sarcoidosis phenotypes. Standard imaging feature engineering techniques such as radiomics suffer from extreme sensitivity to image acquisition and processing, potentially impeding generalizability of research to clinical populations. In this work, we instead investigate approaches to engineering variogram-based features with the intent to identify a robust, generalizable pipeline for image quantification in the study of sarcoidosis.Approach. For a cohort of more than 300 individuals with sarcoidosis, we investigated 24 feature engineering pipelines differing by decisions for image registration to a template lung, empirical and model variogram estimation methods, and feature harmonization for CT scanner model, and subsequently 48 sets of phenotypes produced through unsupervised clustering. We then assessed sensitivity of engineered features, phenotypes produced through unsupervised clustering, and sarcoidosis disease signal strength to pipeline.Main results. We found that variogram features had low to mild association with scanner model and associations were reduced by image registration. For each feature type, features were also typically robust to all pipeline decisions except image registration. Strength of disease signal as measured by association with pulmonary function testing and some radiologist visual assessments was strong (optimistic AUC ≈ 0.9,p≪0.0001in models for architectural distortion, conglomerate mass, fibrotic abnormality, and traction bronchiectasis) and fairly consistent across engineering approaches regardless of registration and harmonization for CT scanner.Significance. Variogram-based features appear to be a suitable approach to image quantification in support of generalizable research in pulmonary sarcoidosis.

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肉样瘤病的纹理:通过变异图量化肺部疾病。
目的:结节病是一种影响肺部的肉芽肿性疾病,发病率超过90%。放射科医生对胸部CT进行定性评估是标准的临床实践,通过CT对疾病进行可靠的量化将支持鉴别结节病表型的持续努力。标准的成像特征工程技术,如放射组学,对图像采集和处理极度敏感,潜在地阻碍了研究的推广到临床人群。在这项工作中,我们研究了基于变差特征的工程方法,目的是在结节病的研究中确定一个强大的、可推广的图像量化管道。方法:对于300多名结节病患者,我们研究了24个特征工程管道,这些管道不同于模板肺的图像配准决策、经验和模型方差估计方法、CT扫描仪模型的特征协调,以及随后通过无监督聚类产生的48组表型。然后,我们评估了工程特征的敏感性,通过无监督聚类产生的表型,以及结节病对管道的信号强度。主要结果:我们发现变异图特征与扫描仪模型有低到轻度的关联,通过图像配准可以降低这种关联。对于每种特征类型,特征对于除了图像配准之外的所有管道决策都具有鲁棒性。通过肺功能测试和一些放射科医生的视觉评估,疾病信号的强度很强(在建筑变形、团块、纤维化异常和牵引性支气管扩张模型中,乐观AUC约为0.9美元,p < ll0.0001美元),并且在所有工程方法中相当一致,无论CT扫描仪的注册和协调如何。意义:基于方差的特征似乎是一种合适的图像量化方法,支持肺结节病的可推广研究。
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来源期刊
Physics in medicine and biology
Physics in medicine and biology 医学-工程:生物医学
CiteScore
6.50
自引率
14.30%
发文量
409
审稿时长
2 months
期刊介绍: The development and application of theoretical, computational and experimental physics to medicine, physiology and biology. Topics covered are: therapy physics (including ionizing and non-ionizing radiation); biomedical imaging (e.g. x-ray, magnetic resonance, ultrasound, optical and nuclear imaging); image-guided interventions; image reconstruction and analysis (including kinetic modelling); artificial intelligence in biomedical physics and analysis; nanoparticles in imaging and therapy; radiobiology; radiation protection and patient dose monitoring; radiation dosimetry
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