Chia Nan University of Pharmacy & Science Institutional Repository:Item 310902800/34574
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    题名: Radiomics for the Prediction of Response to Antifibrotic Treatment in Patients with Idiopathic Pulmonary Fibrosis: A Pilot Study
    作者: Yang, Cheng-Chun
    Chen, Chin-Yu
    Kuo, Yu-Ting
    Ko, Ching-Chung
    Wu, Wen-Jui
    Liang, Chia-Hao
    Yun, Chun-Ho
    Huang, Wei-Ming
    贡献者: Kaohsiung Medical University
    Department of Health and Nutrition, Chia Nan University of Pharmacy & Science
    National Sun Yat Sen University
    Mackay Memorial Hospital
    National Yang Ming Chiao Tung University
    Taipei Medical University
    Taipei Municipal WanFang Hospital
    Mackay Memorial Hospital
    Mackay Medical College
    Mackay Junior College of Medicine, Nursing & Management
    关键词: quantitative computed-tomography
    forced vital capacity
    pirfenidone
    nintedanib
    efficacy
    tests
    日期: 2022
    上传时间: 2023-12-11 13:58:39 (UTC+8)
    出版者: MDPI
    摘要: Antifibrotic therapy has changed the treatment paradigm for idiopathic pulmonary fibrosis (IPF); however, a subset of patients still experienced rapid disease progression despite treatment. This study aimed to determine whether CT-based radiomic features can predict therapeutic response to antifibrotic agents. In this retrospective study, 35 patients with IPF on antifibrotic treatment enrolled from two centers were divided into training (n = 26) and external validation (n = 9) sets. Clinical and pulmonary function data were collected. The patients were categorized into stable disease (SD) and progressive disease (PD) groups based on functional or radiologic criteria. From pretreatment non-enhanced high-resolution CT (HRCT) images, twenty-six radiomic features were extracted through whole-lung texture analysis, and six parenchymal patterns were quantified using dedicated imaging platforms. The predictive factors for PD were determined via univariate and multivariate logistic regression analyses. In the training set (SD/PD: 12/14), univariate analysis identified eight radiomic features and ground-glass opacity percentage (GGO%) as potential predicators of PD. However, multivariate analysis found that the single independent predictor was the sum entropy (accuracy, 80.77%; AUC, 0.75). The combined sum entropy-GGO% model improved the predictive performance in the training set (accuracy, 88.46%; AUC, 0.77). The overall accuracy of the combined model in the validation set (SD/PD: 7/2) was 66.67%. Our preliminary results demonstrated that radiomic features based on pretreatment HRCT could predict the response of patients with IPF to antifibrotic treatment.
    關聯: DIAGNOSTICS, v.12, n.4, 1002
    显示于类别:[Dept. of Health and Nutrition (including master's program)] Periodical Articles

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