Radiomics for the Prediction of Response to Antifibrotic Treatment in Patients with Idiopathic Pulmonary Fibrosis: A Pilot Study

CNU IR > Pharmacy and Science > Dept. of Health and Nutrition (including master's program) > Periodical Articles > Item 310902800/34574

Please use this identifier to cite or link to this item: https://ir.cnu.edu.tw/handle/310902800/34574

Title:	Radiomics for the Prediction of Response to Antifibrotic Treatment in Patients with Idiopathic Pulmonary Fibrosis: A Pilot Study
Authors:	Yang, Cheng-Chun Chen, Chin-Yu Kuo, Yu-Ting Ko, Ching-Chung Wu, Wen-Jui Liang, Chia-Hao Yun, Chun-Ho Huang, Wei-Ming
Contributors:	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
Keywords:	quantitative computed-tomography forced vital capacity pirfenidone nintedanib efficacy tests
Date:	2022
Issue Date:	2023-12-11 13:58:39 (UTC+8)
Publisher:	MDPI
Abstract:	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.
Relation:	DIAGNOSTICS, v.12, n.4, 1002
Appears in Collections:	[Dept. of Health and Nutrition (including master's program)] Periodical Articles

Files in This Item:

File	Description	Size	Format
diagnostics12041002.pdf		2188Kb	Adobe PDF	55	View/Open
index.html		0Kb	HTML	285	View/Open

Loading...