特发性肺纤维化多模态机器学习分类器预测间质性肺疾病的死亡率

IF 2 Q2 RESPIRATORY SYSTEM
Sean J. Callahan , Mary Beth Scholand , Angad Kalra , Michael Muelly , Joshua J. Reicher
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引用次数: 0

摘要

背景:间质性肺疾病(ILD)的预后包括临床病史、肺功能测试(PFTs)和胸部CT类型分类。机器学习分类器Fibresolve包括一个帮助检测与特发性肺纤维化(IPF)相关的CT模式的模型。我们开发并测试了新的Fibresolve软件来预测ILD患者的预后。方法fibresolve使用变压器(ViT)算法分析CT图像,该算法还嵌入了pft、年龄和性别,以产生总体风险评分。该模型经过训练,在602个受试者的数据集中优化风险评分,旨在通过Cox比例风险最大化预测性能。用第一个风险比评估数据集完成验证,然后在第二个数据集进行测试。结果在验证集的研究期间,220名受试者中有61%死亡,而在第二个数据集的研究期间,407名受试者中有40%死亡。验证数据集的死亡率风险比(HR)为3.66 (95% CI: 2.09-6.42),中高危组为4.66 (95% CI: 2.47-8.77)。在第二个数据集中,Fibresolve是初次就诊时死亡率的预测因子,在中等和高危组中,HR为2.79(1.73-4.49)和5.82(3.53-9.60)。在随访中也观察到类似的预测性能,以及连续随访中Fibresolve评分的变化。fibresolve通过自动评估CT、PFTs、年龄和性别的ViT模型来预测死亡率。新的软件算法提供了准确的预测,并证明了检测临床变化的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-modal machine learning classifier for idiopathic pulmonary fibrosis predicts mortality in interstitial lung diseases

Background

Interstitial lung disease (ILD) prognostication incorporates clinical history, pulmonary function testing (PFTs), and chest CT pattern classifications. The machine learning classifier, Fibresolve, includes a model to help detect CT patterns associated with idiopathic pulmonary fibrosis (IPF). We developed and tested new Fibresolve software to predict outcomes in patients with ILD.

Methods

Fibresolve uses a transformer (ViT) algorithm to analyze CT imaging that additionally embeds PFTs, age, and sex to produce an overall risk score. The model was trained to optimize risk score in a dataset of 602 subjects designed to maximize predictive performance via Cox proportional hazards. Validation was completed with the first hazard ratio assessment dataset, then tested in a second datatest set.

Results

61 % of 220 subjects died in the validation set's study period, whereas 40 % of the 407 subjects died in the second dataset's. The validation dataset's mortality hazard ratio (HR) was 3.66 (95 % CI: 2.09–6.42) and 4.66 (CI: 2.47–8.77) for the moderate and high-risk groups. In the second dataset, Fibresolve was a predictor of mortality at initial visit, with a HR of 2.79 (1.73–4.49) and 5.82 (3.53–9.60) in the moderate and high-risk groups. Similar predictive performance was seen at follow-up visits, as well as with changes in the Fibresolve scores over sequential visits.

Conclusion

Fibresolve predicts mortality by automatically assessing combined CT, PFTs, age, and sex into a ViT model. The new software algorithm affords accurate prognostication and demonstrates the ability to detect clinical changes over time.
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来源期刊
Respiratory investigation
Respiratory investigation RESPIRATORY SYSTEM-
CiteScore
4.90
自引率
6.50%
发文量
114
审稿时长
64 days
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