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

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標題:	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
貢獻者:	Kaohsiung Medical University 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 Taipei Medical University Mackay Memorial Hospital Mackay Medical College Mackay Junior College of Medicine, Nursing & Management
關鍵字:	quantitative computed-tomography forced vital capacity pirfenidone nintedanib efficacy tests
日期:	2022
上傳時間:	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.CB2, pp.CC2, pp.-,
顯示於類別:	[行政單位] 123

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