Performance of a generative adversarial network using ultrasound images to stage liver fibrosis and predict cirrhosis based on a deep-learning radiomics nomogram

列线图 医学 接收机工作特性 肝硬化 逻辑回归 人工智能 放射科 判别式 机器学习 内科学 计算机科学
作者
Yayang Duan,Jing Qin,W.-Q. Qiu,Sy Li,Chenyang Li,A.-S. Liu,Xiang Chen,Chang Zhang
出处
期刊:Clinical Radiology [Elsevier]
卷期号:77 (10): e723-e731 被引量:9
标识
DOI:10.1016/j.crad.2022.06.003
摘要

To investigate the performance of a generative adversarial network (GAN) model for staging liver fibrosis and its radiomics-based nomogram for predicting cirrhosis.This two-centre retrospective study included 434 patients for whom input data of ultrasound images and histopathological data (obtained within 1 month of ultrasound examinations) were assigned to the training cohort (249 patients), the internal cohort (92 patients), and the external (93 patients) cohort. A data augmentation method based on a GAN model was used. The discriminative performance was evaluated for classifying fibrosis of S4 and ≥S3. Deep-learning radiomics features were extracted for the prediction of cirrhosis (S4). To perform feature reduction and selection, the least absolute shrinkage and selection operator (LASSO) algorithm was applied. Radiomics scores, along with clinical factors, were incorporated into a nomogram using multivariable logistic regression analysis. The performance of the models was estimated with respect to discrimination power, calibration, and clinical benefits.The areas under the receiver operating characteristic curve (AUCs) values of the GAN were 0.832/0.762 (≥S3), and 0.867/0.835 (S4) for internal/external test sets, respectively. The radiomics nomogram that intergrated radiomics scores and clinical factors showed good calibration and discrimination ability of 0.922 (AUC) in the training dataset, 0.896 in the internal dataset, and 0.861 in the external dataset. Decision curve analysis (DCA) demonstrated that the nomogram outperformed radiologist and haematological indices in terms of the most clinical benefits.The GAN model could be applied to discriminate fibrosis stages, and a favourable predictive accuracy for diagnosing cirrhosis was achieved using a deep-learning radiomics nomogram.
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