Severity-stratification of interstitial lung disease by deep learning enabled assessment and quantification of lesion indicators from HRCT images.

IF 1.7 3区 医学 Q3 INSTRUMENTS & INSTRUMENTATION
Yexin Lai, Xueyu Liu, Fan Hou, Zhiyong Han, Linning E, Ningling Su, Dianrong Du, Zhichong Wang, Wen Zheng, Yongfei Wu
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引用次数: 0

Abstract

Background: Interstitial lung disease (ILD) represents a group of chronic heterogeneous diseases, and current clinical practice in assessment of ILD severity and progression mainly rely on the radiologist-based visual screening, which greatly restricts the accuracy of disease assessment due to the high inter- and intra-subjective observer variability.

Objective: To solve these problems, in this work, we propose a deep learning driven framework that can assess and quantify lesion indicators and outcome the prediction of severity of ILD.

Methods: In detail, we first present a convolutional neural network that can segment and quantify five types of lesions including HC, RO, GGO, CONS, and EMPH from HRCT of ILD patients, and then we conduct quantitative analysis to select the features related to ILD based on the segmented lesions and clinical data. Finally, a multivariate prediction model based on nomogram to predict the severity of ILD is established by combining multiple typical lesions.

Results: Experimental results showed that three lesions of HC, RO, and GGO could accurately predict ILD staging independently or combined with other HRCT features. Based on the HRCT, the used multivariate model can achieve the highest AUC value of 0.755 for HC, and the lowest AUC value of 0.701 for RO in stage I, and obtain the highest AUC value of 0.803 for HC, and the lowest AUC value of 0.733 for RO in stage II. Additionally, our ILD scoring model could achieve an average accuracy of 0.812 (0.736 - 0.888) in predicting the severity of ILD via cross-validation.

Conclusions: In summary, our proposed method provides effective segmentation of ILD lesions by a comprehensive deep-learning approach and confirms its potential effectiveness in improving diagnostic accuracy for clinicians.

通过深度学习从 HRCT 图像中评估和量化病变指标,对间质性肺病进行严重程度分级。
背景:间质性肺病(ILD)是一类慢性异质性疾病,目前临床上对ILD严重程度和进展的评估主要依赖于放射科医生的视觉筛查,由于观察者之间和观察者内部的差异性较大,这极大地限制了疾病评估的准确性:为了解决这些问题,在这项工作中,我们提出了一种深度学习驱动的框架,可以评估和量化病变指标,并对 ILD 的严重程度进行预测:具体来说,我们首先提出了一种卷积神经网络,它可以从ILD患者的HRCT中分割和量化五种类型的病变,包括HC、RO、GGO、CONS和EMPH,然后根据分割的病变和临床数据进行定量分析,选择与ILD相关的特征。最后,结合多个典型病灶,建立基于提名图的多变量预测模型,预测 ILD 的严重程度:实验结果表明,HC、RO 和 GGO 这三种病变可以独立或结合其他 HRCT 特征准确预测 ILD 分期。基于 HRCT,所使用的多元模型在 I 期对 HC 的 AUC 值最高为 0.755,对 RO 的 AUC 值最低为 0.701,在 II 期对 HC 的 AUC 值最高为 0.803,对 RO 的 AUC 值最低为 0.733。此外,通过交叉验证,我们的 ILD 评分模型在预测 ILD 严重程度方面的平均准确率为 0.812(0.736 - 0.888):总之,我们提出的方法通过综合深度学习方法对 ILD 病灶进行了有效分割,并证实了其在提高临床医生诊断准确性方面的潜在有效性。
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
CiteScore
4.90
自引率
23.30%
发文量
150
审稿时长
3 months
期刊介绍: Research areas within the scope of the journal include: Interaction of x-rays with matter: x-ray phenomena, biological effects of radiation, radiation safety and optical constants X-ray sources: x-rays from synchrotrons, x-ray lasers, plasmas, and other sources, conventional or unconventional Optical elements: grazing incidence optics, multilayer mirrors, zone plates, gratings, other diffraction optics Optical instruments: interferometers, spectrometers, microscopes, telescopes, microprobes
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