使用临床和病理学支持的诊断标签,分析机器学习分类器在 CT 上无明确或可能的通常间质性肺炎模式的间质性肺病病例中的验证性能

IF 2.9 2区 工程技术 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Journal of Digital Imaging Pub Date : 2024-01-11 DOI:10.1007/s10278-023-00914-w
Marcello Chang, Joshua J. Reicher, Angad Kalra, Michael Muelly, Yousef Ahmad
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引用次数: 0

摘要

我们以前验证过一种机器学习分类系统 Fibresolve,它可以无创预测特发性肺纤维化 (IPF) 的诊断。该系统采用自动深度学习算法,通过分析胸部计算机断层扫描(CT)成像来评估与特发性肺纤维化相关的特征。在此,我们评估了对通常间质性肺炎(UIP)模式特征之外的模式进行评估的性能。机器学习分类器之前已开发完成,并使用标准训练集、验证集和测试集进行了验证,其中包括临床和病理确定的基本事实。在本次调查中,多站点 295 例患者验证数据集被用于重点亚组分析,以评估分类器在有和无放射学 UIP 及可能 UIP 指征病例中的性能范围。对 UIP 特定特征的放射学评估,包括网状结构、磨玻璃、支气管扩张和蜂窝状结构的存在和分布,被用于放射学模式的分配。分类器的输出结果在不同的 UIP 分组中进行评估。机器学习分类器能将不符合 UIP 或可能 UIP 标准的病例分类为 IPF,灵敏度估计为 56-65%,特异度估计为 92-94%。示例病例显示了非基底动脉占主导地位以及磨玻璃模式,这些病例通过主观成像标准无法确定是否为 UIP,但分类系统却能将其正确识别为 IPF,并通过多学科讨论(一般包括组织病理学)予以确认。机器学习分类器Fibresolve可能有助于诊断无放射学UIP和可能有UIP模式的IPF病例。
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Analysis of Validation Performance of a Machine Learning Classifier in Interstitial Lung Disease Cases Without Definite or Probable Usual Interstitial Pneumonia Pattern on CT Using Clinical and Pathology-Supported Diagnostic Labels

We previously validated Fibresolve, a machine learning classifier system that non-invasively predicts idiopathic pulmonary fibrosis (IPF) diagnosis. The system incorporates an automated deep learning algorithm that analyzes chest computed tomography (CT) imaging to assess for features associated with idiopathic pulmonary fibrosis. Here, we assess performance in assessment of patterns beyond those that are characteristic features of usual interstitial pneumonia (UIP) pattern. The machine learning classifier was previously developed and validated using standard training, validation, and test sets, with clinical plus pathologically determined ground truth. The multi-site 295-patient validation dataset was used for focused subgroup analysis in this investigation to evaluate the classifier’s performance range in cases with and without radiologic UIP and probable UIP designations. Radiologic assessment of specific features for UIP including the presence and distribution of reticulation, ground glass, bronchiectasis, and honeycombing was used for assignment of radiologic pattern. Output from the classifier was assessed within various UIP subgroups. The machine learning classifier was able to classify cases not meeting the criteria for UIP or probable UIP as IPF with estimated sensitivity of 56–65% and estimated specificity of 92–94%. Example cases demonstrated non-basilar-predominant as well as ground glass patterns that were indeterminate for UIP by subjective imaging criteria but for which the classifier system was able to correctly identify the case as IPF as confirmed by multidisciplinary discussion generally inclusive of histopathology. The machine learning classifier Fibresolve may be helpful in the diagnosis of IPF in cases without radiological UIP and probable UIP patterns.

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来源期刊
Journal of Digital Imaging
Journal of Digital Imaging 医学-核医学
CiteScore
7.50
自引率
6.80%
发文量
192
审稿时长
6-12 weeks
期刊介绍: The Journal of Digital Imaging (JDI) is the official peer-reviewed journal of the Society for Imaging Informatics in Medicine (SIIM). JDI’s goal is to enhance the exchange of knowledge encompassed by the general topic of Imaging Informatics in Medicine such as research and practice in clinical, engineering, and information technologies and techniques in all medical imaging environments. JDI topics are of interest to researchers, developers, educators, physicians, and imaging informatics professionals. Suggested Topics PACS and component systems; imaging informatics for the enterprise; image-enabled electronic medical records; RIS and HIS; digital image acquisition; image processing; image data compression; 3D, visualization, and multimedia; speech recognition; computer-aided diagnosis; facilities design; imaging vocabularies and ontologies; Transforming the Radiological Interpretation Process (TRIP™); DICOM and other standards; workflow and process modeling and simulation; quality assurance; archive integrity and security; teleradiology; digital mammography; and radiological informatics education.
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