Improving prognostic accuracy in lung transplantation using unique features of isolated human lung radiographs

IF 12.4 1区医学 Q1 HEALTH CARE SCIENCES & SERVICES

NPJ Digital Medicine Pub Date : 2024-10-03 DOI:10.1038/s41746-024-01260-z

Bonnie T. Chao, Andrew T. Sage, Micheal C. McInnis, Jun Ma, Micah Grubert Van Iderstine, Xuanzi Zhou, Jerome Valero, Marcelo Cypel, Mingyao Liu, Bo Wang, Shaf Keshavjee

{"title":"Improving prognostic accuracy in lung transplantation using unique features of isolated human lung radiographs","authors":"Bonnie T. Chao, Andrew T. Sage, Micheal C. McInnis, Jun Ma, Micah Grubert Van Iderstine, Xuanzi Zhou, Jerome Valero, Marcelo Cypel, Mingyao Liu, Bo Wang, Shaf Keshavjee","doi":"10.1038/s41746-024-01260-z","DOIUrl":null,"url":null,"abstract":"Ex vivo lung perfusion (EVLP) enables advanced assessment of human lungs for transplant suitability. We developed a convolutional neural network (CNN)-based approach to analyze the largest cohort of isolated lung radiographs to date. CNNs were trained to process 1300 longitudinal radiographs from n = 650 clinical EVLP cases. Latent features were transformed into principal components (PC) and correlated with known radiographic findings. PCs were combined with physiological data to classify clinical outcomes: (1) recipient time to extubation of <72 h, (2) ≥ 72 h, and (3) lungs unsuitable for transplantation. The top PC was significantly correlated with infiltration (Spearman R: 0·72, p < 0·0001), and adding radiographic PCs significantly improved the discrimination for clinical outcomes (Accuracy: 73 vs 78%, p = 0·014). CNN-derived radiographic lung features therefore add substantial value to the current assessments. This approach can be adopted by EVLP centers worldwide to harness radiographic information without requiring real-time radiological expertise.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4000,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01260-z.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"NPJ Digital Medicine","FirstCategoryId":"3","ListUrlMain":"https://www.nature.com/articles/s41746-024-01260-z","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}

引用次数: 0

Abstract

Ex vivo lung perfusion (EVLP) enables advanced assessment of human lungs for transplant suitability. We developed a convolutional neural network (CNN)-based approach to analyze the largest cohort of isolated lung radiographs to date. CNNs were trained to process 1300 longitudinal radiographs from n = 650 clinical EVLP cases. Latent features were transformed into principal components (PC) and correlated with known radiographic findings. PCs were combined with physiological data to classify clinical outcomes: (1) recipient time to extubation of <72 h, (2) ≥ 72 h, and (3) lungs unsuitable for transplantation. The top PC was significantly correlated with infiltration (Spearman R: 0·72, p < 0·0001), and adding radiographic PCs significantly improved the discrimination for clinical outcomes (Accuracy: 73 vs 78%, p = 0·014). CNN-derived radiographic lung features therefore add substantial value to the current assessments. This approach can be adopted by EVLP centers worldwide to harness radiographic information without requiring real-time radiological expertise.

Abstract Image

查看原文本刊更多论文

利用离体人肺X光片的独特特征提高肺移植预后的准确性

体外肺灌注（EVLP）可对人体肺部的移植适宜性进行高级评估。我们开发了一种基于卷积神经网络（CNN）的方法，用于分析迄今为止最大的一组离体肺部X光片。对 CNN 进行了训练，以处理来自 n = 650 个临床 EVLP 病例的 1300 张纵向射线照片。潜在特征被转化为主成分（PC），并与已知的放射学结果相关联。主成分与生理数据相结合，对临床结果进行分类：(1) 受体拔管时间为 72 小时；(2) ≥ 72 小时；(3) 肺部不适合移植。最高 PC 与浸润有明显相关性（Spearman R：0-72，p <0-0001），增加放射学 PC 能明显提高对临床结果的判别能力（准确率：73 vs 78%，p = 0-014）。因此，CNN 导出的肺部放射学特征为当前评估增添了大量价值。世界各地的 EVLP 中心都可以采用这种方法来利用放射学信息，而无需实时放射学专业知识。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

NPJ Digital Medicine Multiple-

CiteScore

25.10

自引率

3.30%

发文量

170

审稿时长

15 weeks

期刊介绍： npj Digital Medicine is an online open-access journal that focuses on publishing peer-reviewed research in the field of digital medicine. The journal covers various aspects of digital medicine, including the application and implementation of digital and mobile technologies in clinical settings, virtual healthcare, and the use of artificial intelligence and informatics. The primary goal of the journal is to support innovation and the advancement of healthcare through the integration of new digital and mobile technologies. When determining if a manuscript is suitable for publication, the journal considers four important criteria: novelty, clinical relevance, scientific rigor, and digital innovation.