Developing the Lung Graph-Based Machine Learning Model for Identification of Fibrotic Interstitial Lung Diseases.
Accurate detection of fibrotic interstitial lung disease (f-ILD) is conducive to early intervention. Our aim was to develop a lung graph-based machine learning model to identify f-ILD. A total of 417 HRCTs from 279 patients with confirmed ILD (156 f-ILD and 123 non-f-ILD) were included in this study...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 1; pp. 268 - 280 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=175966503&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175966503 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2024 vid: 37 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 175966503 175966503 175966503 10.1007/s10278-023-00909-7 175966503 ppf: 268 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Developing the Lung Graph-Based Machine Learning Model for Identification of Fibrotic Interstitial Lung Diseases. aug: au: Sun, Haishuang Liu, Min Liu, Anqi Deng, Mei Yang, Xiaoyan Kang, Han Zhao, Ling Ren, Yanhong Xie, Bingbing Zhang, Rongguo Dai, Huaping affil: National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity sug: subj: Lung Diseases, Interstitial Radiography Tomography, X-Ray Computed Methods Machine Learning Algorithms Evaluation Graphics Human Radiographic Image Enhancement Radiomics Lung Anatomy and Histology Predictive Value of Tests Validity Descriptive Statistics Confidence Intervals ROC Curve Funding Source ab: Accurate detection of fibrotic interstitial lung disease (f-ILD) is conducive to early intervention. Our aim was to develop a lung graph-based machine learning model to identify f-ILD. A total of 417 HRCTs from 279 patients with confirmed ILD (156 f-ILD and 123 non-f-ILD) were included in this study. A lung graph-based machine learning model based on HRCT was developed for aiding clinician to diagnose f-ILD. In this approach, local radiomics features were extracted from an automatically generated geometric atlas of the lung and used to build a series of specific lung graph models. Encoding these lung graphs, a lung descriptor was gained and became as a characterization of global radiomics feature distribution to diagnose f-ILD. The Weighted Ensemble model showed the best predictive performance in cross-validation. The classification accuracy of the model was significantly higher than that of the three radiologists at both the CT sequence level and the patient level. At the patient level, the diagnostic accuracy of the model versus radiologists A, B, and C was 0.986 (95% CI 0.959 to 1.000), 0.918 (95% CI 0.849 to 0.973), 0.822 (95% CI 0.726 to 0.904), and 0.904 (95% CI 0.836 to 0.973), respectively. There was a statistically significant difference in AUC values between the model and 3 physicians (p < 0.05). The lung graph-based machine learning model could identify f-ILD, and the diagnostic performance exceeded radiologists which could aid clinicians to assess ILD objectively. Given a sequence of HRCT slices from a patient, the lung field is first automatically extracted. Next, this lung region is divided into 36 sub-regions using geometric rules, obtaining a lung atlas. And then, the lung graph is built based on 3D radiomics features of each sub-region of the lung atlas. Finally, the model's predictions were compared to the physicians' assessment results. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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